Consolidate Your LLM Observability Stack

Stop juggling separate tools for tracing, evals, and cost monitoring. Adopt a single, unified platform to reduce integration overhead, accelerate debugging, and gain a holistic view of your LLM application's performance and spend.

beginner15 minutes5 steps

The play

Implement Basic Tracing
Pick a unified observability platform (e.g., LangSmith, Arize, Helicone) and wrap your primary LLM client calls with their SDK. This immediately gives you request/response logging in their UI with minimal code changes.
Connect Traces to Cost and Latency
In your chosen platform's UI, find the traces you just generated. Navigate to the trace details and identify the associated cost and latency metrics. Most platforms now provide this out-of-the-box, linking operational spend directly to specific API calls.
Run Your First Integrated Evaluation
Collect a small dataset of 10-20 production traces. Use the platform's built-in evaluation features to run a simple 'Correctness' or 'Helpfulness' evaluation against them. This shifts evals from ad-hoc scripts to a repeatable, integrated process.
Set Up a Proactive Monitor
Move from passive logging to active monitoring. Create a simple monitor or alert for a key business metric. A good starting point is setting a threshold for P95 latency or creating an alert for a sudden increase in traces with negative user feedback scores.
Build a Production-Ready Stack
Now that you've experienced the core components of a unified platform, apply these principles to a real project. For a complete, hands-on implementation, follow our guide to build a flagship observability stack from scratch.

Starter code

Wrap one of your existing OpenAI or Anthropic calls with the SDK from a unified observability platform. In just a few lines of code, you'll see your first traces appear in a dashboard.