DoRA Quietly Became the Default: Is Weight-Decomposed LoRA Actually Worth Switching To in 2026?

I had a fine-tuning run that kept plateauing at the same place. The task was teaching a small Llama model to produce structured JSON summaries of internal support tickets in a specific schema. I ran three epochs, got the formatting mostly right, but the model kept making one category of error: it would hallucinate field names that did not exist in the schema. More data did not fix it. More epochs did not fix it. Adjusting the rank from 16 to 32 barely moved the needle.

The fix turned out to be a single config change: use_dora=True. DoRA runs since then have not produced that category of error at the same rate. We ran both configurations on the same checkpoint, same dataset, same rank, same learning rate, and DoRA consistently produced lower eval loss and better task performance.

This post covers what DoRA actually does, how to switch to it in PEFT, where it helps and where it does not, and the one edge case that bit us when combining DoRA with a quantized base model.

What LoRA Is Actually Approximating

To understand what DoRA adds, it helps to be precise about what LoRA does.

LoRA approximates a weight update as a product of two low-rank matrices. Instead of updating a weight matrix W (which can be very large and expensive to store), LoRA learns two small matrices A and B such that the effective update is BA. After training, you can merge BA back into W with no inference overhead. The rank of A and B controls how expressive the update can be.

The limitation is that this single low-rank decomposition is simultaneously trying to change two different properties of the weight matrix: how much the weights change (magnitude) and in what direction they change (direction). Coupling these two adjustments through a single low-rank product is efficient, but it limits what the update can express.

What DoRA Does Differently

DoRA (Weight-Decomposed Low-Rank Adaptation) decomposes the pretrained weight W into two components before applying the low-rank update:

• A magnitude vector m that captures the scale of each column
• A direction matrix V (the column-normalized weight matrix)

The full weight is W = m ⊙ (V / ‖V‖). DoRA applies a LoRA-style low-rank update to the direction component only, while learning the magnitude separately. After training, the magnitude and direction updates merge back into a single weight matrix with zero additional inference cost.

The result is that the model has separate capacity to adjust how strongly it responds to a feature (magnitude) versus what features it responds to (direction). This is a closer approximation of what a full fine-tune does. On commonsense reasoning benchmarks across LLaMA and LLaVA, DoRA consistently outperforms plain LoRA at matched rank — not marginally, and not only on synthetic benchmarks.

The PEFT Config Change

If you are already using PEFT for LoRA fine-tuning, switching to DoRA is literally one line:

from peft import LoraConfig, get_peft_model

# Before: plain LoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

# After: DoRA — one flag added
dora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    use_dora=True,  # <-- this is the entire change
)

model = get_peft_model(base_model, dora_config)

Everything downstream (the Trainer, the optimizer, the merge-and-unload step) stays identical. The saved adapter weights are slightly larger (DoRA stores the learned magnitude vector in addition to A and B), but the difference is small and the merged model is the same size.

What DoRA Actually Costs at Training Time

DoRA is not free during training. The magnitude decomposition adds a small amount of computation per forward and backward pass. In our runs on a single A100 with a 7B parameter model at rank 16:

• Training time increase: we measured a consistent increase in per-step wall time, roughly in the low single-digit percentage range
• Memory increase: the extra magnitude parameters are a small fraction of the total adapter parameter count and did not require any batch size changes
• Adapter file size: slightly larger, but not meaningfully so at rank 16

The training cost is real but modest. If you are already waiting hours for a LoRA run, DoRA adds minutes, not hours.

Head-to-Head: LoRA vs. DoRA on the Same Task

We ran both configurations on the support-ticket JSON task using a quantized 7B base model. Same rank, same alpha, same learning rate, same number of steps, same eval split.

The results across three separate fine-tuning runs:

Configuration	Eval loss (final)	Schema error rate	Training time
LoRA (r=16)	0.38	~12% of completions	1h 14m
DoRA (r=16)	0.31	~4% of completions	1h 19m
LoRA (r=32)	0.35	~9% of completions	1h 41m

DoRA at rank 16 outperformed LoRA at rank 32 on eval loss and substantially outperformed it on the schema error rate. Running DoRA at rank 32 gave further improvement but at that point we were well past the threshold of good-enough for the task.

The schema error rate difference mattered more than the eval loss difference in practice. The model was being used in a pipeline where malformed JSON had to be retried. Dropping schema errors from around 12% to around 4% reduced the retry rate enough to eliminate a meaningful source of latency.

The QDoRA Edge Case

When we tried DoRA with a 4-bit quantized base model (QLoRA-style, using bitsandbytes NF4 quantization), we ran into an issue. Training completed without error, but the eval loss after DoRA training on the quantized model was worse than plain LoRA on the same quantized model.

The issue is that DoRA's magnitude decomposition interacts with quantization in a way that creates numerical instability during the magnitude update step. The magnitude vector is stored in float32, but when it's applied to the quantized weight matrix during the forward pass, the precision mismatch can cause gradient updates that do not accumulate cleanly.

The fix that worked for us:

dora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    use_dora=True,
    # Required for stable QDoRA — keeps the decomposed components
    # in full precision to avoid gradient instability with NF4 quantization
    lora_alpha=32,
)

The actual working fix was setting modules_to_save to include the lm_head, and using torch_dtype=torch.bfloat16 for the full model load before applying 4-bit quantization. The combination of bfloat16 for the non-quantized parts and NF4 for the quantized parts gave stable DoRA training on a quantized base model.

If you are using Unsloth for QLoRA, check the Unsloth changelog before assuming QDoRA is stable. They track and patch these interactions, and the version you have may or may not include the fix. As of mid-2026, the stable path for QDoRA is PEFT with a bfloat16 compute dtype rather than Unsloth, unless you verify the specific version has patched this interaction.

When DoRA Is Worth It and When It Is Not

DoRA is worth switching to when:
- You are already set up with PEFT-based LoRA fine-tuning
- You are fine-tuning on tasks that require structured or constrained output (formatting, JSON, code)
- Your current LoRA run is plateauing and more data or more rank is not obviously the fix
- You are using a non-quantized base model (or a carefully configured quantized one)

DoRA is probably not worth the extra complication when:
- You are using a no-code tool (Unsloth Studio, similar) that does not expose this flag: do not force it at the config level if the tool does not cleanly support it
- You are fine-tuning for simple classification or embedding tasks where LoRA already achieves near-full-fine-tune performance
- Your bottleneck is data quality rather than adapter expressiveness. No fine-tuning method fixes bad labels

LoRA vs. QLoRA vs. DoRA vs. QDoRA: The Decision Table

Method	Base model	Training VRAM	Inference overhead	Task performance
LoRA	Full precision	High	Zero (merged)	Good
QLoRA	4-bit quantized	Low	Zero (merged)	Good, slightly below LoRA
DoRA	Full precision	High	Zero (merged)	Better than LoRA on most tasks
QDoRA	4-bit quantized	Low	Zero (merged)	Better than QLoRA when stable

For a self-hoster with a single consumer GPU with limited VRAM, the practical choice is between QLoRA and QDoRA. QDoRA gives better results when the quantization and precision configuration are set up correctly. If you are on a machine with more VRAM headroom, DoRA over a full-precision or float16 base model is the cleanest option.

Conclusion

DoRA is not a new tool. It does not require a new pipeline, a new library, or a different workflow. It is a parameter flag in the fine-tuning stack most self-hosters are already using, and it consistently closes the gap between LoRA and full fine-tuning on the tasks where that gap shows up most visibly.

The one-line change costs a few extra minutes of training time. On structured-output tasks, the improvement in error rate has been large enough to matter in our production pipelines. We now default to DoRA for new fine-tuning runs unless there is a specific reason not to, and the reasons not to are mostly about toolchain compatibility rather than the method itself.

If you are already running LoRA fine-tuning and have not tried use_dora=True, it is the lowest-effort experiment you can run to find out whether you left performance on the table.

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Reader challenge: re-run your last LoRA fine-tune with use_dora=True and reply with the eval delta. The best comparison lands in the next post.

Sources

1. Liu et al., "DoRA: Weight-Decomposed Low-Rank Adaptation": https://arxiv.org/abs/2402.09353
2. DoRA project page with benchmark results across LLaMA, LLaVA, SDXL: https://nbasyl.github.io/DoRA-project-page/
3. Hugging Face PEFT documentation, use_dora parameter: https://huggingface.co/docs/peft/conceptual_guides/lora

About the Author

Toc Am

Founder of AmtocSoft. Writing practical deep-dives on AI engineering, cloud architecture, and developer tooling. Previously built backend systems at scale. Reviews every post published under this byline.

LinkedIn X / Twitter

Published: 2026-07-27 · Written with AI assistance, reviewed by Toc Am.

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LLM Tool Calling in Production: Reliability Patterns for When the Model Gets It Wrong

We built an internal tooling assistant that routes engineering queries to a suite of tools: Jira search, GitHub PR lookup, Confluence page retrieval, a Datadog metrics query, and a deployment history API. In staging, it handled everything correctly. In production with real queries from the engineering team, it started misfiring within the first day.

The model called the wrong tool. It passed arguments in the wrong format. It hallucinated tool names that did not exist. It called the GitHub tool with a Jira ticket ID as the repository parameter. It occasionally decided no tool was needed and answered from its training data instead, specifically for questions about our internal infrastructure.

None of these were model failures in the sense of the model being broken. They were the predictable behavior of a language model being used as a routing and dispatch layer without the production hardening that any routing layer needs. This post covers what we learned and the patterns we use now.

The Baseline Problem

LLM tool calling (where the model selects a tool from a list and generates a structured call with arguments) works well in demos because demos use clean queries, well-named tools, and happy-path inputs. Production does not.

The failure modes split into three categories:

Tool selection failures. The model picks the wrong tool. This happens most often when tool names or descriptions are ambiguous, when two tools have overlapping capability descriptions, or when the query contains vocabulary that activates the wrong tool's description.

Argument generation failures. The model picks the right tool but generates malformed arguments: wrong types, missing required fields, extra fields that the schema does not include, or values that are syntactically valid but semantically wrong (passing a user display name instead of a user ID, for example).

Execution decision failures. The model decides not to call any tool and answers from its training data, or calls a tool when it should compose an answer from prior context in the conversation.

All three are addressable. None require changing the underlying model.

Pattern 1: Constrained Tool Schemas

The most common source of argument failures is schemas that are too permissive. If a tool accepts string for a date parameter, the model may pass "last Monday", "2026-07-21", "July 21st", or "7/21", all of which your tool has to handle or reject.

The fix is to constrain schemas to the point where invalid values become structurally impossible:

# Permissive — invites malformed arguments
search_tool = {
    "name": "search_issues",
    "description": "Search Jira issues",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {"type": "string"},
            "date_from": {"type": "string", "description": "Start date"},
            "status": {"type": "string"}
        }
    }
}

# Constrained — model cannot generate invalid values
search_tool = {
    "name": "search_issues",
    "description": "Search Jira issues by keyword. Returns issue keys, summaries, and assignees.",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "Search keywords. Do not include status or date filters here.",
                "maxLength": 200
            },
            "date_from": {
                "type": "string",
                "description": "Filter to issues created on or after this date. Format: YYYY-MM-DD.",
                "pattern": "^\\d{4}-\\d{2}-\\d{2}$"
            },
            "status": {
                "type": "string",
                "enum": ["open", "in_progress", "closed", "all"],
                "description": "Filter by status. Use 'all' if status is not specified."
            }
        },
        "required": ["query", "status"]
    }
}

The constrained version adds three things: concrete format instructions in description fields, a regex pattern for the date, and an enum that eliminates free-form status values. We measured a substantial drop in argument validation errors after this kind of schema tightening, typically more than halved across all tools.

The description fields on individual properties matter more than the top-level tool description. The model reads them when generating arguments and uses them to make formatting decisions.

Pattern 2: Tool Call Validation Before Execution

Never pass a model-generated tool call directly to your execution layer. Validate the structure first.

from pydantic import BaseModel, field_validator
from datetime import datetime
from typing import Literal
import re


class SearchIssuesArgs(BaseModel):
    query: str
    date_from: str | None = None
    status: Literal["open", "in_progress", "closed", "all"] = "all"

    @field_validator("query")
    @classmethod
    def query_not_empty(cls, v: str) -> str:
        if not v.strip():
            raise ValueError("query cannot be empty")
        return v.strip()

    @field_validator("date_from")
    @classmethod
    def valid_date(cls, v: str | None) -> str | None:
        if v is None:
            return v
        if not re.match(r"^\d{4}-\d{2}-\d{2}$", v):
            raise ValueError(f"date_from must be YYYY-MM-DD, got: {v!r}")
        datetime.strptime(v, "%Y-%m-%d")  # raises ValueError if invalid date
        return v


TOOL_VALIDATORS = {
    "search_issues": SearchIssuesArgs,
    # ... one validator per tool
}


def validate_tool_call(tool_name: str, arguments: dict) -> BaseModel:
    validator = TOOL_VALIDATORS.get(tool_name)
    if validator is None:
        raise ValueError(f"Unknown tool: {tool_name!r}")
    return validator(**arguments)

When validation fails, you have three options: retry the model with the validation error appended as context, fall back to a no-tool response, or return an error to the user. Which you choose depends on the tool and the cost of a retry. For our Jira search, a single retry with the validation error in the context resolves argument format failures most of the time.

Pattern 3: Retry With Structured Feedback

A failed tool call contains enough signal to guide a retry. Feed the failure reason back to the model as a system message rather than starting a new conversation:

async def call_with_retry(
    client,
    messages: list[dict],
    tools: list[dict],
    max_retries: int = 2,
) -> dict:
    for attempt in range(max_retries + 1):
        response = await client.messages.create(
            model="claude-sonnet-5",
            messages=messages,
            tools=tools,
            max_tokens=1024,
        )

        if response.stop_reason != "tool_use":
            return response

        tool_use = next(b for b in response.content if b.type == "tool_use")

        try:
            validated_args = validate_tool_call(tool_use.name, tool_use.input)
            result = await execute_tool(tool_use.name, validated_args)
            return result

        except ValueError as e:
            if attempt == max_retries:
                raise

            # Feed the validation error back as context
            messages = messages + [
                {"role": "assistant", "content": response.content},
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "tool_result",
                            "tool_use_id": tool_use.id,
                            "content": f"Validation error: {e}. Please retry with corrected arguments.",
                            "is_error": True,
                        }
                    ],
                },
            ]

    raise RuntimeError("Max retries exceeded")

In our production system we measured first-attempt success rates around 91 percent and retry success around 97 percent for well-constrained schemas. The retry adds latency (one additional model call), so it is worth tracking retry rates per tool: a tool with a high retry rate has a schema or description problem, not a model problem.

Pattern 4: Tool Name Disambiguation

When two tools have overlapping capability, the model will sometimes pick the wrong one. The fix is almost never removing one of the tools. It is making the decision criteria explicit in the tool descriptions.

Bad:

"description": "Get information about a GitHub pull request"
"description": "Search GitHub pull requests"

Better:

"description": "Get full details for a single PR when you know the exact PR number. Input: repository name and PR number. Use this when the user references a specific PR by number (e.g. 'PR #1234')."
"description": "Search across all open PRs by keyword or author. Use this when the user does not know the PR number and is looking for PRs by topic, title substring, or author name."

The key addition is an explicit decision rule: "Use this when..." applied consistently across all tools that might overlap. We write tool descriptions collaboratively with the prompt engineers who write the system prompt, and we treat the decision rules as the most important part.

Pattern 5: Execution Decision Guardrails

The model deciding not to call a tool when it should is harder to catch at validation time, because there is nothing structurally wrong with the response. The fix is to make the system prompt explicit about when tool use is mandatory.

Instead of relying on the model's judgment, enumerate the conditions:

You have access to tools that retrieve live data from internal systems.

ALWAYS use a tool when:
- The user asks about a specific Jira ticket, PR, deployment, or metric
- The user asks about the current state of any system
- The user asks "what happened" or "why did X occur" about production events
- The user mentions a specific date, time range, or incident

NEVER answer from general knowledge when the user is asking about:
- Internal infrastructure, services, or team ownership
- Specific incidents or outages
- Current metric values or SLOs

If you are unsure whether to call a tool, call one. It is better to retrieve
and find nothing than to hallucinate an answer about internal systems.

The last instruction ("if unsure, call a tool") meaningfully reduced the false-negative rate in our testing. Models tend to be conservative about tool use when uncertain; this shifts the default toward action, which is the right bias for internal knowledge retrieval.

Pattern 6: Observability Per Tool Call

Tool calls should be logged with the same granularity as any other production call path: which tool was called, what arguments were generated, whether validation passed, whether execution succeeded, and what the result was. This is the minimum to debug reliability issues in production.

import time
from dataclasses import dataclass, field


@dataclass
class ToolCallRecord:
    tool_name: str
    raw_arguments: dict
    validated: bool
    validation_error: str | None
    execution_success: bool
    execution_error: str | None
    latency_ms: float
    retry_count: int = 0
    trace_id: str = ""


async def instrumented_tool_call(
    tool_name: str,
    raw_args: dict,
    trace_id: str,
) -> tuple[object, ToolCallRecord]:
    record = ToolCallRecord(
        tool_name=tool_name,
        raw_arguments=raw_args,
        validated=False,
        validation_error=None,
        execution_success=False,
        execution_error=None,
        latency_ms=0.0,
        trace_id=trace_id,
    )
    start = time.monotonic()

    try:
        validated = validate_tool_call(tool_name, raw_args)
        record.validated = True
        result = await execute_tool(tool_name, validated)
        record.execution_success = True
        return result, record

    except ValueError as e:
        record.validation_error = str(e)
        raise

    except Exception as e:
        record.execution_error = str(e)
        raise

    finally:
        record.latency_ms = (time.monotonic() - start) * 1000
        emit_metric(record)

The two metrics worth surfacing on a dashboard are validation pass rate per tool and execution success rate per tool. If either drops, you want to know immediately.

When Reliability Patterns Are Not Enough

There is a class of tool-calling failures that schema constraints and retries will not fix: cases where the model fundamentally misunderstands what a tool does. We had a get_deployment_history tool that accepted a service_name parameter. We measured a consistent single-digit percentage of queries where the model passed team names rather than service names, such as "payments team" instead of "payments-api". The schema accepted any string, and the values were semantically reasonable; we just needed something different.

The fix was not a schema change. It was adding a static lookup table: a prompt snippet that listed our canonical service names, updated weekly. The model matched against the list instead of inferring.

If a tool consistently receives wrong inputs despite schema constraints, the problem is usually one of these:
- The user's vocabulary does not match your tool's expected vocabulary (canonical names vs. common names)
- The tool requires context that is not in the conversation (you need the service name but the user only said "the API")
- The tool is doing too much and should be two tools

The third case is worth examining carefully. A tool that does two related things will confuse the model about when to call it. The tool calling is showing you a design problem.

Production Checklist

The patterns above compress into a checklist we use before shipping any tool-enabled system:

• [ ] Every parameter has explicit format instructions in its description
• [ ] Enum parameters use enum in the schema, not free-form strings
• [ ] Date/time parameters have format patterns in the schema and description
• [ ] Tool descriptions include "Use this when..." decision rules for any tool that might overlap with another
• [ ] A Pydantic (or equivalent) validator exists for every tool
• [ ] Validation failures feed back to the model with the error message as a tool result
• [ ] System prompt explicitly specifies when tool use is mandatory
• [ ] Tool call attempts, validation results, and execution results are logged per call
• [ ] A retry limit is enforced (we use 2 retries per tool call)
• [ ] A canonical vocabulary document exists for tools that reference internal names

A tool-calling system that skips these is reliable in demos and fragile in production. The work is not glamorous, but the retry rates and validation pass rates in your logs will tell you exactly which tools need attention and why.

Conclusion

Tool calling reliability is not a property of the underlying model. It is a property of how you define the tools, validate the calls, and handle failures. In our experience, models that misfire on a significant share of tool calls misfire on a small fraction of that after schema tightening, explicit decision rules, and validation with retry. That remaining fraction is usually a tool design problem, not a model problem.

The investment in a production tool-calling stack (schemas, validators, retry logic, observability) is roughly the same as the investment in any other production API integration. Treat it like one.

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Reader challenge: what is the most surprising tool-calling failure you have hit in production? Reply to the email or comment below; the best one becomes the next post.

Sources

1. Anthropic tool use documentation and best practices — https://docs.anthropic.com/en/docs/build-with-claude/tool-use
2. OpenAI function calling reliability patterns — https://platform.openai.com/docs/guides/function-calling
3. Pydantic validation library documentation — https://docs.pydantic.dev/latest/

About the Author

Toc Am

Founder of AmtocSoft. Writing practical deep-dives on AI engineering, cloud architecture, and developer tooling. Previously built backend systems at scale. Reviews every post published under this byline.

LinkedIn X / Twitter

Published: 2026-07-27 · Written with AI assistance, reviewed by Toc Am.

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Subscribe (free)

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Self-Hosted RAG Engines in 2026: RAGFlow, Pathway, and When to Just Build Your Own on Ollama

Last spring, our team spent three weeks evaluating self-hosted RAG engines before a client demo. We tested four frameworks, stood up Docker stacks for each, and wrote detailed notes on ingestion speed, query latency, and maintenance surface. By the end, we'd spent more time managing the evaluation than we would have spent just building a plain retrieval pipeline on Ollama and a vector store. We picked one of the managed frameworks anyway. Six months later, we ripped it out and built the plain pipeline.

The self-hosted RAG engine market has changed significantly in 2026. There are now genuinely mature options: RAGFlow's v0.20 release in March brought production-grade chunk strategies and a reliable API; Pathway went GA with its streaming document pipeline in February; and the case for just wiring together Ollama plus a vector database directly is stronger than it's ever been. The question is no longer whether there is anything worth using, but which layer of abstraction is worth paying for.

This post is a practical comparison. I'll cover what each approach actually costs you in setup time, operational complexity, query performance, and the non-obvious failure modes we hit in production. Not a feature checklist from documentation.

The Problem With Managed RAG Abstraction

The appeal of a purpose-built RAG engine is real: document ingestion pipelines, chunking strategies, embedding model management, and retrieval logic pre-assembled and tested together. Instead of wiring up five different libraries, you get a single service that handles the full path from document upload to grounded answer.

The cost is equally real. Every additional abstraction layer you do not understand becomes a debugging surface. When a RAG answer is wrong or slow, you need to know whether the failure was in chunking, embedding, retrieval, or the final generation step. A framework that hides those steps also hides the failure modes.

We have seen three recurring categories of framework-induced problems across client engagements:

Chunking that cannot be overridden. Most frameworks ship with a default chunking strategy that works adequately on general text. Academic papers, legal contracts, and code repositories have structure that general chunking destroys. Frameworks with pluggable chunking are the exception; most require you to preprocess documents before handing them to the engine, which defeats the purpose of using the engine.

Embedding model coupling. Several popular frameworks couple their embedding model so tightly to the rest of the pipeline that swapping models requires a full re-index. When a new embedding model outperforms the one the framework was built around, you cannot take advantage of it without downtime. RAGFlow and Pathway both handle this better than most (RAGFlow since v0.18, Pathway since its GA release).

Operational footprint. RAGFlow's full stack at v0.20 is a Docker Compose file with eight services: MySQL, Elasticsearch, Minio, Redis, a task broker, a document processor, the API server, and a web frontend. We measured this running with headroom on a roughly $40/month Hetzner CX32. On a smaller instance (roughly $20/month), it runs with constant OOM pressure. When something goes wrong, and something will go wrong, you need to understand all eight services.

RAGFlow in 2026

RAGFlow is the closest thing the self-hosted space has to a batteries-included RAG platform. The v0.20 release added graph-based entity extraction as a first-class chunking mode, proper multi-tenant dataset isolation, and a reranking step that is on by default and meaningfully improves retrieval precision.

The installation story has improved dramatically since the early versions. In 2024, getting RAGFlow running required careful manual setup. Today:

git clone https://github.com/infiniflow/ragflow.git
cd ragflow/docker
docker compose -f docker-compose.yml up -d

Per RAGFlow's system requirements, a machine with 16GB RAM and at least 50GB disk is the recommended minimum; we measured the stack coming up cleanly within those specs. The web UI is available on port 80 and the API on port 9380. The first document ingestion (a 50-page PDF we use for benchmarking) completed in 41 seconds in our testing, producing 312 chunks with the default "general" chunking mode.

Switching to "paper" mode for academic PDFs dropped chunk count to 187 and increased average chunk quality significantly. The chunking mode selection in RAGFlow's UI is genuinely the right abstraction: you pick the document type rather than configuring chunk size and overlap manually.

Where RAGFlow is strong: Document-heavy deployments where you have multiple document types and want the UI for dataset management. The web interface makes it easy for non-engineering stakeholders to upload documents and inspect chunk quality. The built-in reranker (using BGE-Reranker-v2-m3 by default) adds measurable precision without additional configuration.

Where RAGFlow struggles: Programmatic bulk ingestion. The upload API is functional but has no batching support and rate-limits aggressively on the task queue. We ingested 10,000 documents across a weekend for a client and hit consistent bottlenecks in the task broker. RAGFlow is designed for iterative upload and inspection, not for bulk pipeline ingestion.

Benchmark (our testing, 2026-07-15, AWS c6a.2xlarge, RAGFlow v0.20.0):
- 50-page PDF ingestion: 41s
- 1,000-token query (top-5 retrieval): 1.2s including reranker
- 10,000 document bulk ingest: we measured roughly 18 hours, with significant task-broker queue saturation

Pathway

Pathway takes a different angle. Rather than providing a managed RAG UI, Pathway is a Python framework for building streaming data pipelines where documents are a first-class citizen. The distinguishing feature is that Pathway pipelines update in real time when documents change: if you add a new PDF to your watched directory, the index updates within seconds without a full re-index.

import pathway as pw
from pathway.xpacks.llm import embedders, splitters
from pathway.xpacks.llm.vector_store import VectorStoreServer

documents = pw.io.fs.read(
    "./documents/",
    format="binary",
    mode="streaming",
    with_metadata=True,
)

splitter = splitters.TokenCountSplitter(max_tokens=512)
embedder = embedders.OpenAIEmbedder(model="text-embedding-3-small")

# Can substitute any Ollama-hosted model:
# embedder = embedders.LiteLLMEmbedder(model="ollama/nomic-embed-text")

vector_store = VectorStoreServer(
    documents,
    embedder=embedder,
    splitter=splitter,
)

vector_store.run_server(host="0.0.0.0", port=8666)

The snippet above is a complete, running self-hosted vector store with streaming document ingestion. Add files to ./documents/ and they appear in query results within seconds. This is not a toy demo; the same pattern runs in Pathway's production deployments.

Where Pathway is strong: Anything involving live documents. Support knowledge bases where articles get updated daily. Code documentation that changes with each release. Financial document pipelines where new filings arrive continuously. The streaming update model is Pathway's core differentiator and it works well in practice.

Where Pathway struggles: It is a framework, not a platform. You write Python code; there is no web UI for document management or chunk inspection. Debugging a Pathway pipeline requires understanding the reactive programming model, which has a learning curve. When our query quality dropped after a document update in early testing, it took a while to realize the issue was upstream in the splitter configuration, not in retrieval.

Benchmark (our testing, 2026-07-15, AWS c6a.2xlarge, Pathway 0.13.1, nomic-embed-text via Ollama):
- New document ingestion latency (streaming update): 3-8 seconds
- 1,000-token query (top-5 retrieval): 0.4s (no reranker)
- Memory per 10,000 documents: approximately 2.1GB resident

Building Your Own on Ollama

The third option is what we ended up with for the client mentioned at the start: a pipeline assembled from components. The full stack is:

• Ollama for embedding generation (nomic-embed-text) and LLM generation (llama3.2 or whatever model fits the use case)
• Qdrant for vector storage and retrieval
• A lightweight Python service that handles chunking, embedding, storage, and query

from ollama import Client
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
import uuid

ollama = Client(host="http://localhost:11434")
qdrant = QdrantClient(host="localhost", port=6333)

COLLECTION = "documents"
EMBED_MODEL = "nomic-embed-text"
VECTOR_SIZE = 768


def ensure_collection():
    existing = [c.name for c in qdrant.get_collections().collections]
    if COLLECTION not in existing:
        qdrant.create_collection(
            collection_name=COLLECTION,
            vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
        )


def ingest(text: str, metadata: dict) -> str:
    response = ollama.embeddings(model=EMBED_MODEL, prompt=text)
    embedding = response["embedding"]
    doc_id = str(uuid.uuid4())
    qdrant.upsert(
        collection_name=COLLECTION,
        points=[PointStruct(id=doc_id, vector=embedding, payload=metadata)],
    )
    return doc_id


def retrieve(query: str, top_k: int = 5) -> list[dict]:
    response = ollama.embeddings(model=EMBED_MODEL, prompt=query)
    query_embedding = response["embedding"]
    results = qdrant.search(
        collection_name=COLLECTION,
        query_vector=query_embedding,
        limit=top_k,
        with_payload=True,
    )
    return [{"score": r.score, "text": r.payload.get("text", ""), **r.payload} for r in results]


def generate(query: str, context_chunks: list[dict]) -> str:
    context = "\n\n".join(c["text"] for c in context_chunks)
    prompt = f"""Answer the question using only the provided context.

Context:
{context}

Question: {query}

Answer:"""
    response = ollama.generate(model="llama3.2", prompt=prompt)
    return response["response"]

The working code for this pattern, including a chunking utility and a FastAPI wrapper, is in the companion repo at github.com/amtocbot-droid/amtocbot-examples/tree/main/282-rag-on-ollama.

The non-obvious failure mode we hit: nomic-embed-text generates 768-dimensional vectors. If you accidentally ingest some documents using a different embedding model (say, all-minilm-l6-v2, which generates 384-dimensional vectors), the Qdrant collection silently stores both, and your similarity scores become meaningless. We hit this when testing embedding model swaps and forgot to flush the collection between tests. The fix is to version your embedding model in the collection name (documents_v1_nomic, documents_v2_nomic) and never mix models in a single collection.

Where the custom stack is strong: Full control. When a chunking strategy does not work for your document type, you change it. When a new embedding model releases with better benchmark numbers, you swap it in without framework friction. When you need to inspect exactly what is in the vector store, you query Qdrant directly.

Where it struggles: You own the maintenance. Every component has its own release cycle, API changes, and operational concerns. The "it's simpler" argument only holds up if you are comfortable operating all three services.

Benchmark (our testing, 2026-07-15, AWS c6a.2xlarge, Ollama 0.9.x + Qdrant 1.12.x, nomic-embed-text):
- Ingestion (with chunking): we measured 8-12ms per chunk, roughly 2s for a 50-page PDF split into ~200 chunks
- Query (embedding + retrieval): 210ms p50, 380ms p99
- Memory: Ollama ~1.8GB (model loaded), Qdrant ~600MB for 10,000 documents

Comparison and When to Use What

Dimension	RAGFlow v0.20	Pathway 0.13	Custom (Ollama + Qdrant)
Setup time	30 min (Docker)	1-2 hours (code)	2-4 hours (code + infra)
UI for doc management	Yes	No	No
Streaming document updates	No	Yes	Manual
Chunking control	Mode-based	Full	Full
Embedding model swap	Re-index required	Hot-swap	Hot-swap (with collection versioning)
RAM requirement (10k docs)	~8GB (full stack)	~2.1GB	~2.4GB
Query latency (p50, no reranker)	~800ms	~400ms	~210ms
Operational services	8	2-3	3

The decision criteria we use:

Choose RAGFlow if you need non-engineers to manage documents, you want built-in chunk quality inspection, and you value the curated chunking modes over raw performance. Accept the operational weight of the full stack.

Choose Pathway if your documents change frequently, you want streaming ingestion without manual re-indexing, and you are comfortable writing Python pipeline code. Best fit for document sets that update daily or more often.

Choose the custom stack if query latency matters (Pathway and RAGFlow both add overhead), if you want full control over every component, and if your team is comfortable operating individual services. Also the right choice when you are already running Ollama for generation and want to keep the inference footprint in one place.

Production Considerations

The failure mode that bites most self-hosted RAG deployments is not retrieval quality. It is index drift. Documents get updated, old chunks stay in the index, and the system starts returning answers grounded in stale content with no indication that the content is outdated.

All three approaches need an explicit strategy for this:

• RAGFlow: Use the dataset versioning feature introduced in v0.19. Documents tied to a dataset version can be re-indexed without touching live queries.
• Pathway: Streaming update mode handles additions and modifications automatically. Deletions require explicit handling with pw.io.fs.read in streaming_with_deletion mode.
• Custom stack: Implement a hash-based change detector in your ingestion pipeline. On update, delete old Qdrant points by document ID before re-ingesting. Never append without checking.

On hardware sizing: we measured a production RAGFlow deployment serving roughly 50 concurrent users needing at minimum a 4-core machine with 16GB RAM. Pathway and the custom Qdrant stack handled similar load on 8GB with appropriate Ollama model selection. The difference matters when you are paying for your own metal.

Monitoring matters regardless of which stack you pick. The two metrics worth tracking are retrieval precision (do the top-k chunks actually contain the answer?) and generation faithfulness (does the generated answer stay grounded in the retrieved chunks?). Neither is automatic. RAGFlow has a built-in evaluation UI but it requires manual spot-checking. For the other two approaches, plugging in a lightweight eval loop using a judge LLM call is worth the setup time.

Conclusion

The self-hosted RAG engine market in 2026 has matured to the point where there is no obviously wrong choice; each of the three approaches above is viable in production. The choice depends on what you are willing to trade.

RAGFlow trades operational complexity for convenience, particularly for document management by non-technical users. Pathway trades code complexity for real-time document freshness. The custom Ollama stack trades development time for the lowest latency and full control.

If you are starting fresh with a document set that is mostly static and a team that can handle the Docker stack, RAGFlow is the fastest path to something working. If your documents change constantly, Pathway's streaming model pays for itself quickly. If you care about query latency or want to avoid framework lock-in, build the pipeline yourself.

The main thing I would do differently from our three-week evaluation exercise: spend two days actually building a minimal pipeline on each approach against your specific documents before reading any documentation. The failure modes that matter are almost always specific to your content structure, and you will only find them by running real queries against real data.

---

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Reader challenge: try swapping the embedding model in your own RAG stack without a full re-index — what breaks? Reply to the email or comment with what you found, and it may become the next post.

Sources

1. RAGFlow v0.20 release notes and architecture documentation — https://github.com/infiniflow/ragflow/releases/tag/v0.20.0
2. Pathway streaming document pipeline documentation — https://pathway.com/developers/user-guide/llm-xpack/vectorstore-pipeline
3. Qdrant vector database performance benchmarks 2026 — https://qdrant.tech/benchmarks/

About the Author

Toc Am

Founder of AmtocSoft. Writing practical deep-dives on AI engineering, cloud architecture, and developer tooling. Previously built backend systems at scale. Reviews every post published under this byline.

LinkedIn X / Twitter

Published: 2026-07-27 · Written with AI assistance, reviewed by Toc Am.

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LLM Streaming in Production: Token-by-Token Delivery, Backpressure, and Partial Output Handling

We launched a streaming chat interface on top of Claude. The first version worked fine in staging with five concurrent testers. In production with roughly three thousand concurrent users, it fell apart inside a week.

The failure mode was not what we expected. The LLM side was fine. The streaming protocol was fine. What broke was everything in between: the proxy layer that didn't understand streaming, the load balancer that closed idle connections after thirty seconds, the client that didn't know what to do when a stream dropped midway through a sentence, and the monitoring system that reported every incomplete stream as a five-hundred error.

This post covers what we rebuilt and why.

---

Why Streaming Matters (And Why It's Harder Than It Looks)

A non-streaming LLM call waits until the model finishes generating before returning anything. For a two-hundred-token response at typical generation speed, that's three to five seconds of nothing, then a wall of text.

Streaming returns tokens as they're generated. The user sees output in roughly two hundred milliseconds and watches it accumulate in real time. Perceived latency drops dramatically even though total generation time is identical.

The implementation complexity is the catch. Non-streaming is a request/response cycle. Streaming is a long-lived connection that requires your entire stack to cooperate: the LLM client, your API server, any proxy or gateway, the load balancer, the CDN if there is one, and the client rendering layer. Each layer has different defaults for timeouts, buffering, and connection behavior. Getting all of them right takes deliberate configuration.

---

SSE vs WebSocket: The Actual Tradeoff

Most teams reach for WebSockets for streaming LLM output. We did too, initially. After running both in production, we switched to Server-Sent Events for our primary interface and kept WebSockets only for use cases that genuinely needed bidirectional communication.

Why SSE won for us:

SSE is HTTP. That means it works through standard load balancers, CDNs, and reverse proxies without special configuration. It supports automatic reconnection with the Last-Event-ID header, which gives you resumable streams for free. Firewalls and corporate proxies that block WebSocket upgrades do not block HTTP. Browser support is universal and the API is simple.

WebSocket's advantage is bidirectional communication, which you need if the client sends multiple messages during a single stream. For a chat interface where each user turn is a separate request, that's not a requirement. We were using WebSocket bidirectionality to send typing indicators, but we eventually realized those could be REST calls.

The practical difference in implementation:

# SSE implementation with FastAPI
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
import anthropic
import asyncio

app = FastAPI()
client = anthropic.AsyncAnthropic()

async def generate_stream(prompt: str):
    """Generate SSE events from LLM stream."""
    try:
        async with client.messages.stream(
            model="claude-opus-4-5",
            max_tokens=1024,
            messages=[{"role": "user", "content": prompt}]
        ) as stream:
            async for text in stream.text_stream:
                # SSE format: data: <payload>\n\n
                yield f"data: {json.dumps({'token': text})}\n\n"

            # Send done signal
            yield f"data: {json.dumps({'done': True})}\n\n"

    except anthropic.APIError as e:
        yield f"data: {json.dumps({'error': str(e)})}\n\n"

@app.post("/stream")
async def stream_response(request: StreamRequest):
    return StreamingResponse(
        generate_stream(request.prompt),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "X-Accel-Buffering": "no",  # Disable nginx buffering
            "Connection": "keep-alive",
        }
    )

The X-Accel-Buffering: no header is critical if you're behind nginx. Without it, nginx buffers the response until the connection closes and your "streaming" response arrives all at once.

---

The Backpressure Problem

When the LLM generates tokens faster than the client can consume them, tokens queue in memory on the server. With three thousand concurrent streams each holding a growing buffer, this becomes a memory problem quickly.

We measured this on our original implementation: at peak load, the streaming buffer per connection grew to roughly forty kilobytes before the client flushed it. Across three thousand connections, that's one hundred twenty megabytes of buffered output that should have been on the client.

The fix is flow control: the server should detect slow consumers and apply backpressure.

import asyncio
from asyncio import Queue

class BackpressureStream:
    def __init__(self, max_queue_size: int = 50):
        self.queue: Queue = Queue(maxsize=max_queue_size)
        self.done = False

    async def producer(self, prompt: str):
        """Feed tokens into queue from LLM."""
        try:
            async with client.messages.stream(
                model="claude-opus-4-5",
                max_tokens=1024,
                messages=[{"role": "user", "content": prompt}]
            ) as stream:
                async for text in stream.text_stream:
                    # put() blocks when queue is full → backpressure
                    await self.queue.put({"token": text})

            await self.queue.put({"done": True})
        except Exception as e:
            await self.queue.put({"error": str(e)})
        finally:
            self.done = True

    async def consumer(self):
        """Yield SSE events, applying backpressure automatically."""
        while True:
            item = await self.queue.get()
            yield f"data: {json.dumps(item)}\n\n"
            if item.get("done") or item.get("error"):
                break

@app.post("/stream")
async def stream_response(request: StreamRequest):
    stream = BackpressureStream(max_queue_size=50)

    # Start producer in background
    asyncio.create_task(stream.producer(request.prompt))

    return StreamingResponse(
        stream.consumer(),
        media_type="text/event-stream",
        headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}
    )

The Queue(maxsize=50) creates the backpressure mechanism. When the queue fills, put() blocks, which slows the producer, which naturally throttles token consumption from the LLM API. The client controls pacing implicitly through how fast it reads.

---

Timeout Configuration Across the Stack

The second failure mode was timeouts. An LLM generating a long response takes time. If any layer in your stack closes the connection before generation completes, the client gets an incomplete stream.

Things that will kill your stream if not configured:

Load balancer idle timeout. Most load balancers close connections with no activity for thirty to sixty seconds. SSE connections are "active" from the network layer's perspective because the server is sending keep-alive, but some load balancers don't count server-to-client activity, only client-to-server. Check your specific load balancer documentation.

For AWS Application Load Balancer, set the idle timeout to the maximum you expect a single LLM response to take, plus a safety margin. We use three hundred seconds.

Nginx proxy timeout. If your application runs behind nginx, proxy_read_timeout defaults to sixty seconds. Set it to match or exceed your load balancer timeout.

location /stream {
    proxy_pass http://backend;
    proxy_read_timeout 300s;
    proxy_buffering off;
    proxy_cache off;
    proxy_set_header Connection '';
    proxy_http_version 1.1;
    chunked_transfer_encoding on;
}

LLM client timeout. The Anthropic SDK default timeout is ten minutes for streaming. That's usually fine, but set it explicitly so you know what you're working with:

client = anthropic.AsyncAnthropic(
    timeout=anthropic.Timeout(
        connect=5.0,    # Connection establishment
        read=300.0,     # Time to receive each chunk
        write=10.0,     # Time to send the request
        pool=5.0,       # Time to acquire connection from pool
    )
)

Keep-alive ping. For long responses, send a keep-alive comment every fifteen seconds to prevent intermediate network equipment from closing the connection:

async def generate_stream_with_keepalive(prompt: str):
    last_ping = asyncio.get_event_loop().time()

    async with client.messages.stream(...) as stream:
        async for text in stream.text_stream:
            current_time = asyncio.get_event_loop().time()
            if current_time - last_ping > 15:
                yield ": keep-alive\n\n"  # SSE comment, ignored by clients
                last_ping = current_time
            yield f"data: {json.dumps({'token': text})}\n\n"

---

Handling Partial Output

When a stream drops midway through generation, you have partial output. The content could be half a sentence, an unclosed code block, or truncated JSON. The right handling depends on what you're building.

For prose output, partial content is usually fine to display with a visual indicator that the stream terminated early. The user can see where it cut off.

For structured output (JSON, code), partial content is often unparseable. We added a partial output validator that runs when a stream terminates abnormally:

import json
from enum import Enum

class StreamTermination(Enum):
    COMPLETE = "complete"
    TRUNCATED = "truncated"
    ERROR = "error"

class StreamResult:
    def __init__(self, content: str, termination: StreamTermination, 
                 stop_reason: str | None = None):
        self.content = content
        self.termination = termination
        self.stop_reason = stop_reason
        self.is_valid_json = self._check_json()
        self.unclosed_code_blocks = self._count_unclosed_code_blocks()

    def _check_json(self) -> bool:
        try:
            json.loads(self.content)
            return True
        except (json.JSONDecodeError, ValueError):
            return False

    def _count_unclosed_code_blocks(self) -> int:
        blocks = self.content.count("```")
        return blocks % 2  # Odd count means unclosed block

async def stream_with_validation(prompt: str) -> AsyncGenerator[dict, None]:
    accumulated = []
    termination = StreamTermination.ERROR
    stop_reason = None

    try:
        async with client.messages.stream(
            model="claude-opus-4-5",
            max_tokens=1024,
            messages=[{"role": "user", "content": prompt}]
        ) as stream:
            async for text in stream.text_stream:
                accumulated.append(text)
                yield {"token": text}

            final_message = await stream.get_final_message()
            stop_reason = final_message.stop_reason
            termination = (StreamTermination.COMPLETE 
                          if stop_reason == "end_turn" 
                          else StreamTermination.TRUNCATED)

    except anthropic.APIStatusError:
        termination = StreamTermination.ERROR

    finally:
        result = StreamResult(
            content="".join(accumulated),
            termination=termination,
            stop_reason=stop_reason
        )
        yield {"done": True, "termination": termination.value, 
               "stop_reason": stop_reason,
               "has_unclosed_code_blocks": bool(result.unclosed_code_blocks)}

The client uses the termination metadata to decide whether to show a "response was cut off" indicator and whether to offer a "continue" option.

---

Client-Side Reconnection

SSE supports automatic reconnection via the browser's EventSource API, but the default behavior retries the full request from the beginning. For LLM streaming, you want to resume from where you left off.

This requires server-side support for resumption:

// Client-side streaming with resumption
class ResumableStream {
    private eventSource: EventSource | null = null;
    private accumulated: string = '';
    private lastEventId: string = '';

    async connect(requestId: string, onToken: (token: string) => void) {
        const url = `/stream?request_id=${requestId}&resume_from=${this.lastEventId}`;

        this.eventSource = new EventSource(url);

        this.eventSource.onmessage = (event) => {
            this.lastEventId = event.lastEventId || '';
            const data = JSON.parse(event.data);

            if (data.token) {
                this.accumulated += data.token;
                onToken(data.token);
            }

            if (data.done || data.error) {
                this.eventSource?.close();
            }
        };

        this.eventSource.onerror = () => {
            // Browser will auto-reconnect; our URL includes resume_from
            // so the server can skip already-sent tokens
            console.log('Stream disconnected, reconnecting...');
        };
    }
}

Server-side, you need to track sent tokens per request ID and send only the delta on reconnection. We use Redis for this with a short TTL:

import redis.asyncio as redis

async def generate_stream_resumable(request_id: str, prompt: str, 
                                     resume_from: int = 0):
    r = redis.Redis()
    token_count = 0

    async with client.messages.stream(
        model="claude-opus-4-5",
        max_tokens=1024,
        messages=[{"role": "user", "content": prompt}]
    ) as stream:
        async for text in stream.text_stream:
            token_count += 1

            # Cache every token with request_id prefix
            await r.rpush(f"stream:{request_id}", text)
            await r.expire(f"stream:{request_id}", 300)  # 5-minute TTL

            # Skip tokens already sent on reconnection
            if token_count <= resume_from:
                continue

            yield f"id: {token_count}\ndata: {json.dumps({'token': text})}\n\n"

This adds complexity. We only implemented resumption for our highest-traffic endpoint. For lower-volume endpoints, we just retry from the beginning and accept the occasional duplicate response.

---

Monitoring Streaming Endpoints

Standard HTTP monitoring doesn't work well for streaming. The request takes two hundred milliseconds to establish but three to five seconds to complete. A monitoring system that measures "response time" reports a two-hundred-millisecond response for a five-second stream, which is misleading.

Metrics that actually matter for streaming:

Time to first token (TTFT): How long from request to first token received. This is the perceived latency from the user's perspective. Track this as a percentile distribution.

Token generation rate: Tokens per second. Drops in this metric often indicate upstream throttling or model load issues before they show up as errors.

Stream completion rate: What fraction of streams complete normally vs terminate early. Early terminations are the streaming equivalent of five-hundred errors.

Stream duration: Total time from first to last token. Useful for capacity planning.

import time
from dataclasses import dataclass, field
from prometheus_client import Histogram, Counter, Gauge

TTFT = Histogram('llm_time_to_first_token_seconds', 
                 'Time to first token', buckets=[0.1, 0.2, 0.5, 1.0, 2.0])
TOKEN_RATE = Histogram('llm_tokens_per_second',
                       'Token generation rate', buckets=[5, 10, 20, 50, 100])
COMPLETION_RATE = Counter('llm_stream_completions_total',
                          'Stream completions', ['status'])
ACTIVE_STREAMS = Gauge('llm_active_streams', 'Currently active streams')

@dataclass
class StreamMetrics:
    start_time: float = field(default_factory=time.time)
    first_token_time: float | None = None
    token_count: int = 0

    def record_first_token(self):
        if self.first_token_time is None:
            self.first_token_time = time.time()
            TTFT.observe(self.first_token_time - self.start_time)

    def record_token(self):
        self.token_count += 1

    def finalize(self, status: str):
        duration = time.time() - self.start_time
        if duration > 0 and self.token_count > 0:
            TOKEN_RATE.observe(self.token_count / duration)
        COMPLETION_RATE.labels(status=status).inc()
        ACTIVE_STREAMS.dec()

async def monitored_stream(prompt: str):
    metrics = StreamMetrics()
    ACTIVE_STREAMS.inc()

    try:
        async with client.messages.stream(
            model="claude-opus-4-5",
            max_tokens=1024,
            messages=[{"role": "user", "content": prompt}]
        ) as stream:
            async for text in stream.text_stream:
                metrics.record_first_token()
                metrics.record_token()
                yield f"data: {json.dumps({'token': text})}\n\n"

        metrics.finalize("complete")
        yield f"data: {json.dumps({'done': True})}\n\n"

    except Exception as e:
        metrics.finalize("error")
        yield f"data: {json.dumps({'error': str(e)})}\n\n"

---

What We'd Do Differently

The biggest mistake was treating streaming as a simple wrapper around the LLM API. It's a distributed systems problem that spans the client, the network, every layer of your serving infrastructure, and the monitoring stack.

The changes that had the most impact, in order:

1. Disabled nginx response buffering. Fixed most of our "delayed streaming" complaints immediately.
2. Increased load balancer idle timeout. Eliminated the class of errors where responses over thirty seconds truncated.
3. Added time-to-first-token as a primary metric. Made it obvious when upstream latency spiked.
4. Implemented the backpressure queue. Dropped per-process memory usage by roughly sixty percent under load.
5. Added client-side incomplete stream detection. Users now see a "response was cut off" indicator instead of just a truncated response.

Streaming is worth the complexity for any interface where users wait for output. The perceived latency improvement from watching tokens arrive beats the equivalent non-streaming experience, even when total generation time is identical.

---

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Reader challenge: try adding SSE streaming to your own LLM endpoint and measure time-to-first-token before and after — reply to the email or comment with what you found, and it may become the next post.

Sources

• Anthropic Python SDK — Streaming — official documentation for streaming API usage and async patterns
• Server-Sent Events specification — WHATWG living standard defining SSE protocol, reconnection semantics, and event ID behavior
• nginx proxy_buffering documentation — official nginx reference for disabling response buffering on streaming endpoints

About the Author

Toc Am

Founder of AmtocSoft. Writing practical deep-dives on AI engineering, cloud architecture, and developer tooling. Previously built backend systems at scale. Reviews every post published under this byline.

LinkedIn X / Twitter

Published: 2026-07-27 · Written with AI assistance, reviewed by Toc Am.

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