LLM Observability & Evals
Explore tools for tracing LLM applications, evaluating outputs, and investigating quality, latency, and usage.
LLM observability follows what happens inside an AI application, while evaluations check whether its outputs meet your criteria. Together they help a team investigate failures and compare changes to prompts, models, or agent workflows. This collection focuses on tracing and evaluation instead of model hosting or retrieval APIs. Choose around the data you need to inspect, the tests you can repeat, and the deployment controls your application requires.
Compare by workflow
- Tracing and instrumentation
- Evaluation datasets
- Scoring workflow
- Usage and latency analysis
- Hosting and data controls
Shortlist by use case
| Tool | Consider it for |
|---|---|
| Langfuse | Application tracing and evaluation workflows |
| Helicone | Observability alongside an AI gateway |
| Braintrust | Tracing and evaluating application workflows |
| Arize Phoenix | Local or self-hosted tracing and evaluation |
| Opik | Self-hosted or managed tracing and model evaluations |
Explore all LLM Observability & Evals
How to choose
- Trace a representative request through your application and check which model calls, tool steps, and errors are captured.
- Build evaluations around expected outcomes and difficult examples from your own workflow.
- Compare a gateway-centric approach with instrumentation inside the application, based on your stack.
- Check what is captured in traces, how sensitive data is handled, and whether cloud or self-hosted deployment fits your policies.
Frequently asked questions
- What is the difference between tracing and evaluation?
- Tracing records the steps and behavior of a request. Evaluation judges an output or workflow against a dataset or criteria. A trace can help explain why an evaluation failed.
- Is a model benchmark enough to test an application?
- A model benchmark does not cover your application's prompts, retrieval, tools, or business rules. Use application-specific examples and repeat them when the system changes.
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