LLM observability
Track prompts, traces, errors, runs, and related metadata so teams can inspect how LLM apps behave in production.
Overview
Lunary is an observability and prompt management platform for LLM-based applications. It is built to help teams monitor, improve, and secure AI chatbots and other LLM workflows as they move from development into production.
The product combines logs, traces, prompt templates, human review, analytics, and guardrails in one place. The site also emphasizes self-hosting options, enterprise controls, and SDK-based integration so teams can add monitoring without placing Lunary directly in the request path.
Core capabilities
Prompt management
Create prompt templates, collaborate with teammates, and use versioning and A/B testing to iterate on prompt changes.
Evaluations and feedback workflows
Review responses, label data, and export logs to JSONL for downstream fine-tuning workflows.
Analytics and exploration
Search, filter, and analyze usage, costs, topics, and satisfaction with analytics and custom dashboards.
Security and access controls
Mask personal information, manage roles and access, and support SSO/SAML for controlled data access.
Integration options
Connect through SDKs, an HTTP API, and integrations across OpenAI, LangChain, LiteLLM, Flowise, and other tools.
Common use cases
Production debugging
Use Lunary to inspect production LLM traffic, review traces and errors, and understand how real users interact with a chatbot or agent.
Prompt iteration
Create templates, compare prompt versions, and run A/B tests when iterating on system prompts or chat flows.
Evaluation and data preparation
Label logs, review responses, and export data to JSONL when preparing datasets for model fine-tuning or analysis.
Security-focused deployment
Apply PII masking, access controls, and self-hosting when handling sensitive user data or compliance-sensitive workloads.
Team collaboration
Share projects, invite teammates, and use human reviews and dashboards to coordinate feedback across technical and non-technical collaborators.
Pros and Cons
Pros
- Covers observability, prompt management, evaluations, and guardrails in a single platform.
- Supports self-hosted and hosted deployment models.
- Offers asynchronous SDKs that are designed not to add latency to LLM calls.
- Includes prompt templates, chat replays, analytics, human reviews, and custom dashboards.
- Provides enterprise controls such as PII masking, RBAC, SSO/SAML, and audit-oriented features.
Cons
- The integrations page text is incomplete in the provided source, so the full connector list is not visible here.
- Some capabilities and limits are plan-specific, so teams will need to check pricing or enterprise details before choosing a deployment option.
FAQ
What is Lunary?
Lunary is a platform for monitoring, improving, and securing AI chatbots. Its FAQ describes it as covering observability, prompt management, evaluations, and LLM guardrails.
What counts as an event or a run?
According to the FAQ, a run can be types such as llm, trace, chain, embedding, or chat. For LLMs, a run is typically one API call to an inference API or a chat message in a thread.
Will Lunary slow down LLM calls?
No. The FAQ says Lunary SDKs run asynchronously rather than sitting between your code and the inference API, so they should not affect request latency.
How long is data kept?
The FAQ says free-plan data is available for 30 days, paid-plan data is kept indefinitely, and self-hosted data stays with you indefinitely.
Is self-hosting free?
Yes. The FAQ says you can self-host for free with the Community Edition, while the Enterprise Edition supports Docker, Kubernetes, and more and is paid.
Quick Facts
- Category
- Observability and prompt management platform
- Primary users
- Teams building LLM apps, chatbots, and agents
- Deployment
- Hosted cloud, self-hosted community edition, and enterprise self-hosting
- Integrations
- SDK-based integrations plus an integrations directory
- Domain
- lunary.ai
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