Lunary Review (2026)
A lightweight Apache-2.0 observability platform aimed at RAG pipelines and chatbots. The fastest path from nothing to basic tracing, with a free tier metered daily rather than monthly - which catches people out.
Rating
Starting Price
Around $20-30/mo
Free Plan
Yes
SDKs & Frameworks
5
Deployment
3
Best For
Small teams shipping RAG pipelines or chatbots who want basic tracing working this afternoon, and who will either stay small or self-host before volume becomes expensive.
Last Updated:
10 Things You Should Know About Lunary
- 1 Licensed Apache 2.0 and self-hostable with no per-event cost
- 2 The free tier is limited to 1,000 events per day, not per month
- 3 Y Combinator backed, with a reported 1,000+ companies using it
- 4 Radar categorises LLM responses by predefined criteria for later analysis
- 5 Model-independent, compatible with LangChain and OpenAI agents
- 6 Published cloud pricing is inconsistent across third-party sources
Pros & Cons
Pros
- ✓ Genuinely one of the fastest tools to go from nothing to useful tracing
- ✓ Apache 2.0 and self-hostable at zero per-event cost, with full data control
- ✓ Radar is a neat idea - bucketing responses against criteria you define surfaces patterns that raw traces hide
- ✓ Well-suited to RAG and chatbot shapes specifically, rather than being a generic tracer
- ✓ Y Combinator backed with a reported 1,000+ companies using it
Cons
- ✕ The free tier is metered per day at 1,000 events, not per month, which is a much tighter constraint than it first appears
- ✕ Published pricing is inconsistent across sources - we found both $30/mo and a $20-$200/mo range, and could not resolve it
- ✕ Lighter than Langfuse on nearly every axis - fewer integrations, smaller community, thinner eval tooling
- ✕ No meaningful regression testing or CI/CD gating workflow
- ✕ Smaller project, so fewer worked examples when something goes wrong
Features
What Lunary is for
Lunary is a lightweight, open-source LLM observability platform, and it is unusually clear about its target: RAG pipelines and chatbots.
That focus is a strength. A lot of tools in this category try to be complete AI engineering platforms and end up with a lot of concepts you must learn before you see your first trace. Lunary is one of the fastest things here to go from nothing to useful tracing, and reviewers consistently place it alongside Langfuse as a sensible starting point before you invest in a full evaluation platform.
It is model-independent, works with LangChain and OpenAI agents, and is Apache 2.0 with self-hosting available. Y Combinator backed, with a reported 1,000+ companies using it.
The free tier is daily, not monthly
This is the detail most likely to catch you out, and it is easy to misread when scanning a comparison table.
The free tier is 1,000 events per day. Not per month.
Most competitors meter monthly. Langfuse, Logfire, Datadog and Arize AX all publish monthly allowances, so the eye reads “1,000 events” and files it next to “10 million records a month” without registering that the unit changed.
A daily cap behaves very differently. It is not 30,000 events a month you can spend as you like - it is a hard ceiling that resets, and a chatbot having a busy day can exhaust it before lunch and go dark for the rest of the day. That is precisely the day you wanted observability.
If you are evaluating Lunary’s free tier, model your peak day, not your average day.
Pricing we could not pin down
We try to publish a rate card. Here we could not, and we would rather say so.
One directory lists Lunary starting at $30 per month. Another cites self-host free with cloud running $20 to $200 per month depending on tier. These are both third-party sources and they do not reconcile.
We are recording this as unresolved rather than picking a number. Verify directly with the vendor before budgeting.
The figure we are confident about is the useful one: self-hosting is free under Apache 2.0 with zero per-event cost. That is your escape hatch, and it is a real one.
Radar is a genuinely good idea
Worth calling out because it is the most distinctive thing in the product.
Radar categorises LLM responses against criteria you define in advance. Rather than reading traces one at a time, you see how many responses fell into each bucket and investigate the interesting ones.
For a chatbot this closes a real gap. Raw tracing tells you what happened in one conversation. Formal evaluation tells you how you scored on a test set. Neither tells you that 8% of yesterday’s production responses declined to answer, or that a chunk of them cited no source. Radar sits in that middle space.
It is lighter than a full LLM-as-judge suite and should not be mistaken for one. But it is a well-chosen feature for the workloads Lunary targets.
Self-hosting is the sensible default here
More so than with most tools on this site, because it fixes both of Lunary’s weaknesses simultaneously.
Self-hosting is Apache 2.0, free, with zero per-event cost. That removes the daily event cap and makes the pricing ambiguity irrelevant in one move.
It also does not carry the operational weight that self-hosting SigNoz or OpenLIT does - you are not standing up ClickHouse. For any team with modest infrastructure capability, self-hosted Lunary is arguably the more sensible deployment, and you get full data control as a bonus.
Where it is thin
Against Langfuse, Lunary is lighter on nearly every axis: fewer integrations, smaller community, thinner evaluation tooling, no comparable prompt management. Langfuse is MIT, also self-hosts free, and is the better default for most teams choosing a platform to grow into.
There is also no real regression testing or CI/CD gating. You can evaluate prompts and models against expected replies, and Radar gives you categorised production data, but blocking a deploy on a quality regression is not a workflow here. Braintrust and promptfoo are built for that.
To Lunary’s credit, it does not pretend otherwise. That is more than several competitors manage.
Should you use it?
Use Lunary if you are shipping a RAG pipeline or chatbot, you want tracing working today, and you will either stay small or self-host before volume matters.
Don’t use it if you need regression testing and release gating, you are choosing a platform for the next three years, or you need the integration breadth of a larger project.
Bottom line: a good, honest, lightweight tool that fits a specific shape of application well. Self-host it - that removes the daily cap and the pricing uncertainty at once, and it is easy to run. If you want one platform to grow into rather than a fast start, Langfuse is the stronger choice.
Pricing figures are drawn from third-party directories that disagree with each other, and are flagged as unresolved rather than verified. License and feature details verified on 31 July 2026. This is a researched directory entry - we have not yet instrumented this platform with our reference application.
Pricing Plans
Free
$0
- 1,000 events per day, not per month
- Core tracing and analytics
- Community support
Cloud (paid)
Around $20-30/mo
- Higher event allowances
- Sources disagree on exact entry price - verify directly
- Reported range extends to roughly $200/mo at higher tiers
Self-hosted
$0
- Apache 2.0 licensed
- Full data control
- Zero per-event cost
- You operate it
SDKs & Frameworks
Deployment
Eval Methods
Our Verdict
Lunary is a good starter tool that knows what it is. It is optimised for RAG pipelines and chatbots rather than trying to be a complete AI engineering platform, it is Apache 2.0 and self-hostable, and it is among the fastest things in the category to get running. Radar - which buckets responses against criteria you define - is a genuinely useful idea for spotting patterns that individual traces hide. Two caveats matter. The free tier is 1,000 events per day rather than per month, which is a meaningfully tighter constraint than the headline suggests and will not survive a real chatbot for long. And published pricing is inconsistent across sources in a way we could not resolve, with figures ranging from around $20 to $200 a month depending on where you look. Against Langfuse it is lighter on nearly every axis, and Langfuse is the better default for most teams. Lunary earns its place when speed to first trace matters more than depth.
Similar Tools
Traceloop
Teams that want vendor-neutral OpenTelemetry LLM instrumentation via OpenLLMetry, which is genuinely safe to adopt. The managed platform suits existing ServiceNow customers, or teams needing air-gapped deployment who can get contractual clarity on the roadmap.
Helicone
Almost nobody new - existing users who need a light open-source logging proxy and understand it's frozen. New buyers should look elsewhere.
New Relic AI Monitoring
Organisations already standardised on New Relic for APM, logs, Kubernetes and SRE workflows, running relatively low-volume LLM features, who need correlation more than evaluation.
Sentry
Error tracking and front-end debugging, which it does superbly. It is not the right tool for observing or evaluating LLM applications, and should be paired with one rather than substituted for one.
Frequently Asked Questions
What is the catch with the free tier?
It is metered daily, not monthly, and that distinction does more work than it looks. One thousand events per day is not thirty thousand a month you can spend as you like - it is a hard daily ceiling that resets. A chatbot with modest traffic can exhaust it before lunch on a busy day and go dark for the rest of it, which is precisely when you most want observability. Most competitors meter monthly, so this is easy to misread when comparing tiers. If you are evaluating Lunary, model your peak day rather than your average day.
What does it actually cost?
We could not establish this confidently, and we would rather say so than publish a number we are not sure of. One directory lists Lunary starting at $30 per month; another cites self-host free with cloud in a $20 to $200 per month range. Those are not reconcilable and both are third-party. Verify directly with the vendor before budgeting. The one figure we are confident about is that self-hosting is genuinely free under Apache 2.0, with zero per-event cost, which is your reliable escape hatch if cloud pricing turns out not to suit you.
What is Radar?
A classification layer over your responses. Radar categorises LLM outputs against criteria you define in advance, so instead of reading individual traces you can see how many responses fell into each bucket and go looking at the interesting ones. For a chatbot this is genuinely useful - patterns like "responses that declined to answer" or "responses that cited no source" are visible in aggregate rather than one trace at a time. It is lighter than a full LLM-as-judge evaluation suite and should not be mistaken for one, but it addresses a real gap between raw tracing and formal evaluation.
How does it compare with Langfuse?
Langfuse is the better default for most teams and it is not especially close. It has a larger community, far more integrations, more mature evaluation tooling, prompt management and an MIT license, and it also self-hosts free. Lunary's argument is speed and simplicity - it is one of the fastest paths from zero to useful tracing, and its RAG and chatbot focus means fewer concepts to learn. If you want a tool running this afternoon on a small chatbot, Lunary is a fine choice. If you are choosing a platform to grow into, choose Langfuse.
Is self-hosting worth it here?
More than usual, because it neutralises the tool's two main weaknesses at once. Self-hosting is Apache 2.0 with zero per-event cost, which eliminates both the daily event cap and the pricing uncertainty. Lunary is also a lighter system than SigNoz or OpenLIT, so you are not standing up ClickHouse to run it. For a team with any infrastructure capability, self-hosted Lunary is arguably the more sensible deployment, and it means the ambiguity around cloud pricing simply stops mattering.
Can I use it for regression testing?
Not really. You can evaluate prompts and models against desired replies, and Radar gives you categorised production data, but there is no substantial regression testing or CI/CD gating workflow. If you want to block a deploy when a quality score drops, this is not the tool - Braintrust and promptfoo are built for that. Lunary is an observability and analytics tool with light evaluation attached, and it is more honest about that scope than several competitors who overclaim.