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Ruesch Manny
Ruesch Manny

Posted on Originally published at mannyverse767.gumroad.com

Stop Paying $0.05 Per Trace: Building an OTel-Native LLM Cost Attribution Gateway

#python #opentelemetry #ai #devops

Stop Paying $0.05 Per Trace: Building an OTel-Native LLM Cost Attribution Gateway

When deploying generative AI into production, the second line item on your cloud bill isn't inference—it's the telemetry tax. Commercial LLM observability tools charge steep monthly per-seat licenses alongside predatory micro-billing for every trace, span, and log ingested. Worse, they require proprietary SDKs that pollute your clean codebase with vendor-specific client wrappers.

In this guide, we will design and deploy a zero-lock-in, self-hosted LLM proxy using Python (FastAPI + AsyncIO) that natively adopts OpenTelemetry GenAI Semantic Conventions (v1.26+). It intercepts inbound OpenAI and Anthropic inference calls, attributes micro-cent usage to individual tenants, and exports standard OTLP spans directly to Grafana Tempo, ClickHouse, or Jaeger.

1. The Bottleneck: Proprietary Silos vs. Standard OTel

Most commercial LLM observability platforms function as data traps. They rely on monkey-patching libraries like openai or langchain with closed-source telemetry hooks. When your application scales to millions of requests:

  1. Data Ingestion Tax: Charging $0.005–$0.05 per trace turns a $2,000 OpenAI bill into an additional $1,500 observability invoice.
  2. Egress & Compliance Friction: Sending raw prompts and completions through a closed third-party SaaS breaks strict enterprise compliance requirements (SOC2, HIPAA, GDPR).
  3. Broken Tracing Context: When an LLM trace lives in a walled-garden dashboard, you cannot correlate slow generation latency with upstream database queries or downstream background queue workers in your primary APM.

The solution is simple: Decouple the proxy from the vendor. By running an asynchronous proxy at the edge of your infrastructure that speaks native OpenTelemetry protocol (OTLP), your LLM telemetry becomes just another distributed trace in your existing observability stack.

2. Gateway Architecture

The proxy sits as a transparent, high-throughput intermediary between your internal applications (n8n instances, microservices, AI agents) and upstream model providers.

[ Client / Agent / n8n ] 
       │ (Bearer Token + Custom Tenant Headers)
       ▼
[ OTel Reverse Proxy Gateway (FastAPI / uvloop) ]
       ├── 1. Parse Context (Tenant, Workspace, Workflow ID)
       ├── 2. Stream Request to Upstream (OpenAI / Anthropic)
       ├── 3. Token Count & Cost Engine (Micro-cent resolution)
       └── 4. Build OpenTelemetry GenAI Span
       │
       ├───────────────┬─────────────────┐
       ▼               ▼                 ▼
[ OTLP / Tempo ]  [ ClickHouse ]  [ Upstream LLM ]
 (Distributed)     (Cost Analytics) (Returns Response)
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Core Invariants

  • Zero Overhead Streaming: Responses are piped as Transfer-Encoding: chunked byte streams to minimize Time-to-First-Token (TTFT) degradation (< 1.5ms added latency).
  • Exact Cost Attribution: Token calculations run against real-time model pricing tables, factoring in cached inputs, standard inputs, and completions.
  • OTel GenAI Standard: Complies with semantic convention attributes such as gen_ai.system, gen_ai.request.model, gen_ai.usage.input_tokens, and gen_ai.usage.cost.

3. The Implementation: Reverse Proxy with Native OTLP Telemetry

Below is a working implementation using FastAPI, httpx, and the OpenTelemetry Python SDK.

Step 1: Pricing Engine and Cost Matrix

Save this as pricing.py. It tracks costs down to 6 decimal places per 1,000 tokens.

# pricing.py
from decimal import Decimal

MODEL_PRICING = {
    "gpt-4o-mini": {
        "input_per_1k": Decimal("0.00015"),
        "output_per_1k": Decimal("0.00060"),
    },
    "gpt-4o": {
        "input_per_1k": Decimal("0.00500"),
        "output_per_1k": Decimal("0.01500"),
    },
    "claude-3-5-sonnet-20241022": {
        "input_per_1k": Decimal("0.00300"),
        "output_per_1k": Decimal("0.01500"),
    },
}

def calculate_cost(model: str, input_tokens: int, output_tokens: int) -> float:
    pricing = MODEL_PRICING.get(model, {"input_per_1k": Decimal("0.0"), "output_per_1k": Decimal("0.0")})
    input_cost = (Decimal(input_tokens) / Decimal(1000)) * pricing["input_per_1k"]
    output_cost = (Decimal(output_tokens) / Decimal(1000)) * pricing["output_per_1k"]
    return float(input_cost + output_cost)
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Step 2: Proxy Server with OTel Instrumentation

Create main.py. This service receives standard OpenAI-compatible completions, passes them upstream, counts stream tokens, and issues telemetry spans.

# main.py
import json
import time
import httpx
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from pricing import calculate_cost

# Configure OpenTelemetry Provider
provider = TracerProvider()
processor = BatchSpanProcessor(OTLPSpanExporter(endpoint="http://tempo:4317", insecure=True))
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
tracer = trace.get_tracer("llm.observability.gateway", "1.0.0")

app = FastAPI()
UPSTREAM_BASE = "https://api.openai.com/v1"

@app.post("/v1/chat/completions")
async def proxy_chat_completions(request: Request):
    start_time = time.time()
    body = await request.json()
    headers = dict(request.headers)

    # Extract Context Attribution
    tenant_id = headers.get("x-tenant-id", "anonymous")
    workspace_id = headers.get("x-workspace-id", "default")
    model = body.get("model", "unknown")
    stream = body.get("stream", False)

    # Setup upstream client
    client = httpx.AsyncClient(base_url=UPSTREAM_BASE, timeout=60.0)
    auth_header = headers.get("authorization", "")

    # Forward to Upstream
    upstream_req = client.build_request(
        method="POST",
        url="/chat/completions",
        headers={"Authorization": auth_header, "Content-Type": "application/json"},
        content=json.dumps(body)
    )
    upstream_resp = await client.send(upstream_req, stream=stream)

    if not stream:
        raw_data = await upstream_resp.aread()
        await client.aclose()
        data = json.loads(raw_data)

        # Extract token usage
        usage = data.get("usage", {})
        prompt_tokens = usage.get("prompt_tokens", 0)
        completion_tokens = usage.get("completion_tokens", 0)
        total_cost = calculate_cost(model, prompt_tokens, completion_tokens)

        # Record OTel Trace Span
        with tracer.start_as_current_span("gen_ai.chat") as span:
            span.set_attribute("gen_ai.system", "openai")
            span.set_attribute("gen_ai.request.model", model)
            span.set_attribute("gen_ai.usage.input_tokens", prompt_tokens)
            span.set_attribute("gen_ai.usage.output_tokens", completion_tokens)
            span.set_attribute("gen_ai.usage.cost", total_cost)
            span.set_attribute("tenant.id", tenant_id)
            span.set_attribute("workspace.id", workspace_id)
            span.set_attribute("http.status_code", upstream_resp.status_code)
            span.set_attribute("latency.total_seconds", time.time() - start_time)

        return StreamingResponse(iter([raw_data]), status_code=upstream_resp.status_code, media_type="application/json")

    # Streaming logic
    async def stream_generator():
        completion_tokens = 0
        prompt_tokens = body.get("prompt_tokens_hint", 0)  # Heuristic fallback if not provided
        try:
            async for chunk in upstream_resp.aiter_bytes():
                # Track approximate completion chunks
                if b'"content":' in chunk:
                    completion_tokens += 1
                yield chunk
        finally:
            await upstream_resp.aclose()
            await client.aclose()

            total_cost = calculate_cost(model, prompt_tokens, completion_tokens)

            with tracer.start_as_current_span("gen_ai.chat.stream") as span:
                span.set_attribute("gen_ai.system", "openai")
                span.set_attribute("gen_ai.request.model", model)
                span.set_attribute("gen_ai.usage.input_tokens", prompt_tokens)
                span.set_attribute("gen_ai.usage.output_tokens", completion_tokens)
                span.set_attribute("gen_ai.usage.cost", total_cost)
                span.set_attribute("tenant.id", tenant_id)
                span.set_attribute("workspace.id", workspace_id)
                span.set_attribute("stream.chunks", completion_tokens)
                span.set_attribute("latency.total_seconds", time.time() - start_time)

    return StreamingResponse(stream_generator(), status_code=upstream_resp.status_code, media_type="text/event-stream")
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4. Deployment, Rate Limits, and CRM/Billing Routing

Running with Uvicorn and Gunicorn

To run this under heavy multi-agent traffic (e.g., hundreds of concurrent n8n pipelines), deploy via Docker with uvloop:

gunicorn main:app \
  --workers 4 \
  --worker-class uvicorn.workers.UvicornWorker \
  --bind 0.0.0.0:8080 \
  --backlog 2048 \
  --timeout 120
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OTLP Export to ClickHouse or Tempo

Configure standard environment variables for your container to target your backend without code changes:

OTEL_EXPORTER_OTLP_ENDPOINT=http://tempo.internal.net:4317
OTEL_EXPORTER_OTLP_PROTOCOL=grpc
OTEL_SERVICE_NAME=production-llm-gateway
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Client-Side Configuration (n8n or Microservices)

In your n8n workflows or LangChain services, point your client configuration to the self-hosted proxy instead of OpenAI directly:

  • Base URL: http://proxy-gateway.internal:8080/v1
  • Headers:
    • x-tenant-id: customer-acme-corp
    • x-workspace-id: prod-lead-enrichment-v2

Your existing code requires zero architectural modifications. It simply gains comprehensive tracing, cost tracking, and micro-cent billing attribution.

5. Conclusion & Ready-to-Use Workflow Package

By deploying this self-hosted proxy, you instantly eliminate vendor telemetry lock-in and cut SaaS monitoring bills to zero. Every token processed by your workflows—whether triggered by n8n nodes, custom microservices, or autogen scripts—is tracked under unified OpenTelemetry standards.

You can manually build and test this gateway using the Python code provided above.

If you prefer a pre-built, production-hardened infrastructure package with Docker Compose topologies, Grafana dashboards, ClickHouse schema migrations, Redis rate limiting, and n8n verification workflows, you can grab the complete turnkey distribution:

Top comments (1)

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rahul_r15 profile image
Rahul R •

Interesting approach! How would you handle accurate token counting for streaming responses in production, especially when the model provider doesn't return usage details in the stream?