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Opik by Comet

by Comet ML

Open-source LLM tracing, testing, and monitoring platform by Comet ML — enabling APAC ML and data science teams to instrument LLM application calls, build automated test datasets for prompt regression testing, and monitor production LLM quality with integration into Comet ML experiment tracking.

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AIMenta verdict
Decent fit
4/5

"Open-source LLM tracing and testing platform by Comet ML — APAC ML teams use Opik to trace LLM application calls, run automated test suites, and monitor production quality with Comet ML experiment tracking integration."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Open-source: APAC self-hosted option for data sovereignty; no vendor lock-in
  • LLM tracing: APAC OpenAI/Anthropic/LangChain auto-instrumentation via SDK
  • Test datasets: APAC automated LLM-as-judge scoring and CI/CD regression gates
  • Comet ML integration: APAC LLM quality alongside training experiment metrics
  • RAG analysis: APAC retrieval quality and context attribution tracing
  • Production monitoring: APAC statistical quality alerts and trace search
When to reach for it

Best for

  • APAC ML and data science teams already using Comet ML for training experiment tracking who want to extend observability into their LLM application layer — particularly APAC teams building both the underlying models and the LLM applications that deploy them, who need unified visibility across the full ML pipeline.
Don't get burned

Limitations to know

  • ! APAC self-hosted setup requires infrastructure management — cloud version simpler but adds data residency questions
  • ! Comet ML ecosystem dependency — less compelling for APAC teams not already using Comet
  • ! Smaller community than Langfuse or OpenLLMetry for APAC self-service troubleshooting
Context

About Opik by Comet

Opik is an open-source LLM tracing, testing, and monitoring platform developed by Comet ML — providing APAC ML and data science teams with call-level LLM tracing, automated test suites for prompt regression, and production quality monitoring that integrates with Comet ML's broader experiment tracking ecosystem. APAC teams already using Comet ML for model training experiment tracking use Opik to extend that observability into LLM application layers.

Opik's tracing SDK auto-instruments APAC LLM calls from OpenAI, Anthropic, and LangChain — capturing inputs, outputs, latency, and token usage per call and storing traces for analysis in Opik's dashboard. APAC ML engineers debugging RAG pipeline quality issues use Opik traces to identify which retrieval steps produced poor context, which LLM calls generated low-quality responses, and where latency bottlenecks occur in multi-step pipelines.

Opik's testing module allows APAC teams to build datasets of input/expected-output pairs and run LLM-as-judge scoring against those datasets — creating regression test suites that can be triggered in CI/CD before merging prompt changes. APAC engineering teams treating prompt changes as code changes use Opik's testing integration to block merges when test scores regress below configured thresholds.

Opik's connection to Comet ML allows APAC teams to track LLM application quality alongside training experiment metrics — viewing RAG faithfulness scores next to model perplexity, or comparing LLM output quality across different fine-tuned model versions in the same Comet ML dashboard. APAC teams building the full ML pipeline from training to LLM application deployment use this unified observability to understand how model improvements propagate to downstream application quality.

Beyond this tool

Where this category meets practice depth.

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