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Lunary

by Lunary

Open-source LLM logging and analytics platform providing prompt/response logging, cost tracking, user feedback collection, and team-level LLM usage analytics for APAC production AI applications.

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AIMenta verdict
Decent fit
4/5

"LLM logging and analytics — APAC AI teams use Lunary as an open-source LLM observability platform for logging prompts, responses, and costs across GPT-4o, Claude, and open-source models in APAC production AI applications."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Prompt/response logging: capture all APAC LLM interactions with metadata
  • Cost tracking: per-feature, per-model APAC LLM spend analytics
  • User feedback: thumbs-up/down and ratings on APAC AI responses
  • Self-hostable: Docker + PostgreSQL for APAC data sovereignty requirements
  • OpenAI-compatible: logs any APAC LLM via OpenAI SDK with wrapper pattern
  • Team analytics: APAC usage trends by feature, model, and time period
When to reach for it

Best for

  • APAC AI product teams needing lightweight LLM logging and cost analytics with user feedback collection — particularly teams building customer-facing APAC AI features who need to monitor output quality and LLM cost attribution without a full observability platform.
Don't get burned

Limitations to know

  • ! Less trace depth than Phoenix — APAC complex agent workflows need more detailed tooling
  • ! Smaller APAC ecosystem than Langfuse or Helicone for enterprise integrations
  • ! Self-hosted APAC option requires PostgreSQL maintenance vs managed alternatives
Context

About Lunary

Lunary is an open-source LLM logging and analytics platform — providing prompt and response logging, cost tracking, user feedback collection, and team-level usage analytics for APAC AI applications running on GPT-4o, Claude, open-source models, or any OpenAI-compatible API. APAC teams use Lunary to monitor production LLM behavior, track costs per feature, and collect explicit user feedback on AI output quality.

Lunary's logging SDK wraps LLM API calls with minimal code changes — APAC applications add 2-3 lines to start capturing prompts, responses, token counts, latency, and model metadata for every LLM interaction. Lunary supports JavaScript/TypeScript and Python SDKs for APAC web and backend applications, with a REST API for custom APAC integrations.

Lunary's user feedback collection allows APAC applications to capture thumbs-up/thumbs-down, rating scores, or custom annotations on individual LLM responses — these user signals attach to the logged APAC prompt/response pair and appear in the Lunary dashboard for quality trend analysis. APAC teams building customer-facing AI features use this to monitor whether user satisfaction correlates with model or prompt changes.

Lunary's team analytics dashboard shows APAC LLM usage by feature, model, and time period — APAC engineering leads can see which product features consume the most tokens, which models perform best for specific APAC task categories, and how costs trend over time. Lunary is self-hostable for APAC teams with data sovereignty requirements, deploying as a Docker container with a PostgreSQL backend.

Beyond this tool

Where this category meets practice depth.

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