Feature Comparison
| Tool | Best for | Core observability features | Cost and retention watch |
|---|---|---|---|
| Langfuse | Open-source LLM observability and prompt analytics | Traces, spans, prompt versions, datasets, evals, scores, user feedback, and self-hosting. | Watch trace volume, storage, self-hosting operations, and production retention policy. |
| Helicone | Gateway-level logging, model routing, and spend visibility | Request logs, prompt metadata, latency, cache, rate limits, user attribution, and cost dashboards. | Every model call can become a log event; set sampling, redaction, and retention early. |
| Braintrust | Eval-driven AI product teams | Experiments, scorers, datasets, prompt comparison, logs, feedback, and release evaluation. | Eval runs can grow quickly; budget for datasets, scorers, experiments, and retained traces. |
| LangSmith / LangChain | LangChain apps, chains, retrieval, and prompt debugging | Run traces, chain spans, datasets, evaluation, prompt debugging, and LangChain-native workflows. | Strong when the stack is LangChain; compare portability and storage costs if the app is framework-neutral. |
| OpenTelemetry | Portable instrumentation across app and model calls | Standard spans, metrics, traces, collectors, semantic attributes, and export to existing backends. | The standard is free, but collector, backend, indexed span, and retention costs move elsewhere. |
| Datadog | Enterprise production monitoring and incident workflows | Dashboards, logs, traces, alerts, SLOs, service maps, and LLM events beside app infrastructure. | Usually best for existing Datadog teams; control indexed logs, spans, seats, and long retention. |
| MLflow | Experiment tracking and model lifecycle teams adding LLM evaluation | Runs, parameters, artifacts, model registry, prompt/eval artifacts, and experiment lineage. | Useful bridge for ML teams; less agent-specific unless paired with trace or prompt tooling. |
Direct Answer
The best LLM observability tools expose prompt versions, model requests, traces, eval scores, latency, token usage, cost, retention, and feedback. Start with prompt analytics and request logs, add evals before prompt releases, then move to agent observability when tools, browsers, MCP servers, or approvals enter the workflow.
Decision Checklist
A mature LLM observability setup should connect quality, reliability, cost, and privacy. Logging every prompt is not enough if teams cannot compare versions, reproduce failures, or understand budget burn.
[ ] Prompt analytics: prompt version, model version, system message, retrieved context, and release owner [ ] Traces: request span, retrieval span, tool span if present, latency, errors, retries, and cache status [ ] Evals: datasets, scorers, human review, pass/fail thresholds, and release comparison [ ] Cost: tokens, model, cache, user, feature, environment, and projected monthly spend [ ] Log retention: redaction, sampling, PII policy, customer data rules, and deletion workflow [ ] Production alerting: quality regression, latency spike, error rate, spend anomaly, and provider outage
Prompt Analytics, Traces, Evals, And Logs
Treat these as separate layers. Prompt analytics explains what changed; traces explain where a request moved through the system; evals explain whether output quality regressed; logs help debug raw production failures.
- Prompt analytics: compare prompt versions, templates, variables, retrieved context, and release owners.
- Trace analytics: inspect model calls, retrieval, reranking, tool calls, retries, provider latency, and app boundaries.
- Eval analytics: track task quality, hallucination checks, regression suites, human feedback, and release gates.
- Log analytics: keep enough metadata to debug failures without storing sensitive raw prompts longer than needed.
LLM Observability Versus Agent Observability
LLM observability is the foundation for model requests, prompts, traces, evals, latency, and cost. Agent observability adds plans, tool calls, approvals, browser sessions, MCP server calls, intermediate state, and final task success. If the product is a coding agent, browser agent, MCP workflow, or back-office agent, use both layers.
Pricing And Retention
Cost comes from event volume, indexed logs, trace retention, eval runs, seats, storage, and self-hosting operations. Keep sensitive prompt logs on the shortest useful retention path and store summarized payloads when full prompts are not needed.
- Use sampling for low-value success traces, but keep failures and release-eval traces.
- Separate prompt debugging retention from compliance or audit retention.
- Track cost per feature, user, model, and workflow.
- Review self-hosting cost against managed plans before assuming open source is cheaper.
- Confirm whether file uploads, retrieved context, screenshots, browser traces, or tool outputs are stored.
Recommended Adoption Path
Start with simple request logs and cost dashboards, add evals before major prompt changes, then instrument traces once the app has chains, retrieval, tools, or agents.
- Week 1: request logs, prompt version, latency, cost, and redaction.
- Week 2: regression datasets, eval scorers, and release comparison.
- Week 3: trace spans for retrieval, tools, retries, and app boundaries.
- Week 4: alerts, on-call routing, and agent-specific dashboards.
Which Tool To Start With
Choose by the system you already have. Langfuse and Helicone are practical first choices for many LLM apps; Braintrust is strongest when evals are the core workflow; LangSmith is natural for LangChain teams; OpenTelemetry and Datadog fit platform teams; MLflow fits ML organizations that already track experiments.
- Early product: start with Langfuse or Helicone, then add evals.
- Eval-heavy team: start with Braintrust or LangSmith datasets and scorers.
- LangChain app: try LangSmith before building custom trace plumbing.
- Enterprise platform: instrument with OpenTelemetry and export to Datadog or the existing observability backend.
- ML lifecycle team: use MLflow for experiment lineage, then add LLM-specific prompt and trace tooling.
FAQ
What are LLM observability tools?
They monitor LLM-powered applications by collecting prompt versions, requests, responses, traces, evals, latency, errors, cost, and feedback.
What is the difference between LLM observability and evals?
Evals measure output quality against tests or scorers. Observability connects evals with live traces, prompts, latency, cost, errors, and user feedback.
When do I need agent observability?
You need agent observability when your app uses multi-step plans, tools, browser actions, MCP servers, approvals, retries, or autonomous workflows.
Is LangSmith only for LangChain apps?
LangSmith is most natural when your app uses LangChain or LangGraph-style workflows, but teams should still compare portability, retention, and cost against framework-neutral tools.
Can I use OpenTelemetry for LLM observability?
Yes. OpenTelemetry is useful for portable traces and integration with existing observability backends, but you may still need LLM-specific prompt, eval, and cost features.
Should I store full prompts and responses?
Only when your privacy policy and data classification allow it. Many teams redact PII, sample successful traces, keep failures longer, and store summaries for sensitive tool outputs.