The LLM Observability & Eval Index
A neutral index of the LLM observability and evaluation tools teams use to keep AI agents correct and cost-effective in production — grouped by what they focus on (tracing, evaluation, monitoring), how they're hosted, and what each is best at. This is the 'operate' layer of the AI stack: once you've built an agent and given it memory, observability is how you see what it actually did, score whether the output was good, and catch regressions before users do. We describe focus, hosting, and license rather than prices, which change fast. Pair this with the agent-frameworks and vector-database indexes for the full picture.
Last reviewed June 13, 2026 · 12 tools · neutral & vendor-independent
The matrix
| Tool | Focus | Hosting | License | Best for |
|---|---|---|---|---|
| LangSmith | All-in-one | Managed (enterprise self-host) | Proprietary | Teams building on LangChain / LangGraph — native graphs and replay |
| Langfuse | All-in-one | Both (self-host or cloud) | Open-source | Open-source, framework-agnostic tracing + eval with full data ownership (OTel) |
| Arize Phoenix | Tracing + evaluation | Both | Open-source | OTel-native tracing with rigorous, ML-grade evaluation primitives |
| Braintrust | Evaluation + tracing | Managed | Proprietary | Eval-first workflows — datasets, prompt iteration, and scoring |
| Confident AI (DeepEval) | Evaluation | Both | Open-source (DeepEval) | Pytest-style LLM evals and regression tests in CI |
| Weights & Biases Weave | Tracing + evaluation | Managed (enterprise self-host) | Proprietary | Teams already in the Weights & Biases ML ecosystem |
| Comet Opik | Tracing + evaluation | Both | Open-source | Open-source tracing + eval, optionally inside the Comet platform |
| Helicone | Tracing + monitoring | Both | Open-source | Drop-in proxy logging, cost tracking, and caching with minimal code |
| Langtrace | Tracing | Both | Open-source | Vendor-neutral, OpenTelemetry-native tracing |
| MLflow Tracing | Tracing + evaluation | Both | Open-source | Teams standardised on MLflow for the ML lifecycle |
| Latitude | Evaluation | Both | Open-source | Open-source prompt engineering with built-in evals |
| Maxim AI | All-in-one | Managed | Proprietary | End-to-end eval + observability across the agent lifecycle |
Focus, hosting, and license — not version-specific feature claims, which go stale fast. Many tools span tracing, eval, and monitoring; we tag each by its centre of gravity. Verify current capabilities on each project's docs.
How to read the focus
- Tracing
- Capture step-by-step traces of every LLM and tool call in an agent run — inputs, outputs, latency, cost, and errors.
- Evaluation
- Score whether outputs are good — LLM-as-judge, metric libraries, golden datasets, and regression tests in CI.
- Monitoring
- Production dashboards and alerts for cost, latency, throughput, and quality drift over time.
- All-in-one
- Tracing + evaluation + monitoring in one platform — the common shape for tools built for the agent era.
Which one should you pick?
- You build on LangChain / LangGraphLangSmith
- You want open-source and full data ownershipLangfuse
- You want OTel-native tracing with rigorous evalsArize Phoenix
- You want eval-first iteration (datasets, scoring)Braintrust or Confident AI
- You want CI regression tests for promptsConfident AI (DeepEval)
- You want drop-in proxy logging + cost controlHelicone
- You already use W&B, MLflow, or CometWeave, MLflow, or Opik
- You want vendor-neutral OpenTelemetry tracingLangtrace
FAQs
What's the difference between LLM observability and evaluation?
Observability is about seeing what happened: traces of every LLM and tool call, latency, cost, and errors in production. Evaluation is about judging whether the output was good: LLM-as-judge scoring, metric libraries, golden datasets, and regression tests. They're complementary — observability tells you what your agent did, evaluation tells you whether it did it well. Most modern tools now do both, and you need both to operate agents reliably.
Which LLM observability tool is the best?
It depends on your stack and priorities. If you build on LangChain/LangGraph, LangSmith is the path of least friction. For open-source and full data ownership, Langfuse. For OTel-native tracing with rigorous evals, Arize Phoenix. For eval-first iteration, Braintrust or Confident AI (DeepEval). Teams already on Weights & Biases, MLflow, or Comet often use their respective tools. Many production teams pair an open-source tracer with a dedicated eval tool rather than picking one.
Do I need an observability tool to ship an AI agent?
For a prototype, no. For production, effectively yes. Without traces and evaluations you're flying blind on cost, latency, regressions, and hallucinations — and agents fail in ways that are invisible until a user hits them. Even the lightest option (a proxy logger like Helicone) beats nothing; the moment an agent touches real users or money, observability stops being optional.
Are these tools free or open-source?
Many are open-source and free to self-host — Langfuse, Arize Phoenix, Comet Opik, Helicone, Langtrace, MLflow, and DeepEval among them. Managed tiers and proprietary tools (LangSmith, Braintrust, W&B Weave, Maxim AI) bill on usage or seats. As with the rest of the stack, the tool is rarely the dominant cost — the model API calls it observes usually are.
How does observability fit with the rest of the AI stack?
It's the 'operate' layer. Agent frameworks build the agent, vector databases give it memory, automation tools wire it into your systems, and observability + evaluation keep it correct, fast, and cost-effective once it's live. See the AI Agent Frameworks Index and Vector Database Index for the layers underneath, and the AI Automation Tool Index for the no-code wiring.