“1. Langfuse Strong general-purpose choice”
“Strong general-purpose choice”
“- Captures prompts, completions, traces, latency, and token usage.”
“- Supports evaluation, datasets, scores, and prompt versioning.”
“- Can be deployed in your own Kubernetes cluster or cloud infrastructure.”
“Note: Some enterprise governance features are separately licensed.”
“3 | Arize Phoenix | Best if OpenTelemetry and modular observability are priorities”
“Strong for OpenTelemetry”
“- Particularly useful for investigating complex RAG pipelines.”
“- Can run within your own infrastructure.”
“Note: Phoenix uses the Elastic License 2.0, not a conventional OSI-approved open-source license.”
“5. Helicone - Proxy-based LLM request logging.”
“- Useful when you want a relatively simple integration point between applications and model providers.”
“- Offers self-hosting.”
“3. Opik Strong for testing and evaluation”
“Strong for testing and evaluation”
“- Test-driven evaluation workflows, including PyTest integration.”
“- Self-hostable.”
“I would then add DeepEval and Ragas selectively rather than relying exclusively on each platform's built-in scoring.”
“- DeepEval handles automated response evaluation.”
“For example, DeepEval can execute in your CI pipeline and produce a pass/fail result without requiring a managed observability service.”
“I would then add DeepEval and Ragas selectively rather than relying exclusively on each platform's built-in scoring.”
“Ragas | Faithfulness, context precision, context recall | RAG evaluation”
“Ragas is particularly useful when you need to measure the quality of a retrieval-augmented generation pipeline.”
“Promptfoo | Prompt testing, model comparisons, security testing | Yes”
“Promptfoo | Prompt testing, model comparisons, security testing | Yes”
“2 | MLflow | Best if you want evaluation integrated into a broader ML platform”
“Strong for enterprise infrastructure”
“- Experiment tracking and dataset management.”
“- LLM-as-a-judge and code-based scoring.”
“Trade-off: The user experience and workflow are more ML-platform-oriented than dedicated LLM observability products.”
“Inspect AI | Agent behavior, task completion, tool use | Complex agent evaluation”
“Inspect AI | Agent behavior, task completion, tool use | Complex agent evaluation”