We're pushing 50k+ GPT-4 requests per day through our inference service, and our current monitoring is a duct-taped mess of logs and custom dashboards. We looked at LLM Pulse, but the pricing scales poorly for our volume and we need deeper integration with our existing Grafana/Prometheus/OpenTelemetry stack.
I need alternatives that are built for high throughput and give real engineering insight, not just pretty graphs. My core requirements:
* **Low overhead:** Can't add >100ms latency per call.
* **Cost tracking per project/tenant:** We need to attribute OpenAI costs internally.
* **Trace sampling:** Must sample 100% of errors, but only 5-10% of successful calls.
* **Prometheus/Grafana native:** Or full OTLP export. No vendor lock-in for viewing data.
* **Token usage and latency breakdowns:** By model, and by stage (network, processing, etc.).
What's actually running in production at scale? I've shortlisted a few but have concerns:
* **Langfuse:** Open-source, which is a plus. But I'm unsure about the performance impact at our scale. The hosted version has similar pricing concerns to Pulse.
* **Helicone:** Looks good for proxy-based capturing, but does it handle the sampling we need? Also becomes another critical network dependency.
* **OpenTelemetry LLM Semantic Conventions + our own instrumentation:** The most control, but also the most work. Has anyone built a robust system this way?
I'm leaning towards a self-hosted OSS tool or a heavily customized OTEL approach. What are you all using, and what's the actual operational burden like? Share your configs if you can.
Example of the bare minimum I want to see in a trace, in a span attribute:
```json
{
"llm.token.usage.prompt": 1250,
"llm.token.usage.completion": 350,
"llm.model": "gpt-4-turbo-2024-04-09",
"llm.total_cost.usd": 0.0425
}
```
Build once, deploy everywhere
Hey there. I'm Bob, a platform engineer at a mid-sized fintech handling a similar volume of API calls for document processing. We've been running Langfuse self-hosted on Kubernetes for about six months, specifically to monitor and cost-attribute our OpenAI and Anthropic traffic.
Here's my breakdown based on what we've tested and run.
1. **Deployment & Integration Effort:** Langfuse's self-hosted setup via Docker Compose or Helm is straightforward. The main integration work is instrumenting your service. Their SDKs add a few decorators/traces to your existing LLM calls. For our Python FastAPI service, this was a day's work. The proxy (Helicone's approach) is easier to bolt on but gives you less granular control.
2. **Performance Overhead (Latency):** With the Langfuse Python SDK set to async batch exporting, we measured a consistent 15-45ms added latency per *trace* (a full request/response chain), not per individual LLM call within it. The key is the async exporter. You must keep it async and batch events; a synchronous export *will* blow your latency budget.
3. **Cost Tracking & Data Model:** This is where Langfuse shines for your need. You can attach user IDs, project tags, and any custom metadata at the trace level. Their dashboard breaks down cost per user, per project, per model automatically using the latest provider pricing. You can also pipe these metrics directly to Prometheus via their OpenTelemetry integration.
4. **Where It Can Struggle / Limitation:** At 50k+ daily calls, you'll be generating massive trace data. While the ingestion handles it fine, the default PostgreSQL backend can get sluggish for complex queries over 30 days of data. Our fix was to set aggressive trace retention (14 days) in the database and export everything to our data lake via OTLP for long-term analysis. This is an extra step.
For your stated requirements, I'd pick self-hosted Langfuse. It gives you the deepest engineering insight and OTLP export, and you control the cost. But your choice hinges on two things: does your team have the bandwidth to manage the self-hosted instance and data retention, and can you commit to the async instrumentation? If the answer to either is no, then a managed proxy like Helicone becomes the more practical, albeit less customizable, choice.
null
That's a great practical concern. The proxy-based approach Helicone uses can indeed be a bottleneck at high volumes, as you're routing all your traffic through an additional service point. This introduces a single point of failure and adds network hop latency, even if their systems are fast.
Given your explicit need for Grafana/Prometheus native integration, I'd encourage you to look at tools built on OpenTelemetry from the start. A setup using the OpenTelemetry Semantic Conventions for GenAI can pipe metrics like token counts and latency directly into your existing Prometheus scraper, with near-zero overhead. This gives you the vendor-agnostic data you want, though you'll have to build the attribution dashboards yourself.
—HR