lunary.ai

Lunary is an observability and prompt management platform for LLM-based apps. It can be used as a cloud workspace starting at $20 per user per month (payment required to activate the workspace), or self-hosted if you want to own your data. Integration is advertised as a 1-line change, and the platform covers analytics, prompt templates, chat replays, agent tracing, and more. It fits teams that want to ship and monitor LLM features quickly while keeping a path to data control.

Deployment options

Lunary offers two deployment paths, and the choice mostly comes down to who should hold the data and how much operational work you want to take on.

The site lists "Own Your Data" and "Self Hostable" as platform capabilities, so self-hosting is a first-class option rather than an afterthought. If your LLM stack already handles sensitive prompts or user data, self-hosting lets you keep that data inside your own boundary while still using Lunary's tooling.

Pricing model

The published entry point is $20 per user / month for cloud workspaces, and payment is required to activate your workspace. That last detail matters: this is not a free-forever tier — you pay before the workspace becomes usable.

A few practical implications:

• Pricing scales per user, not per request or per token, based on the published signal. That makes cost predictable as traffic grows, but it means adding teammates raises the bill.
• Activation is gated by payment. Budget for this before you plan a trial rollout.
• The site also offers "Request a demo" and "Schedule a Demo," which is the usual route for enterprise or custom arrangements. Specific enterprise terms are not published, so treat them as a conversation rather than a listed price.

If you need exact current numbers beyond the $20 starting point, check the pricing page directly, since published figures can change.

Integration into an existing LLM stack

Lunary advertises a 1-line integration, which is the core of its low-friction pitch. The intent is that you add a small amount of code to your existing app and start capturing LLM data without restructuring your stack.

What that integration feeds into:

• Capture LLM data across your entire stack — the platform is positioned to collect data from wherever your LLM calls happen, not just one service.
• Chat replays — see how conversations actually unfolded.
• Analytics and topic classification — understand how users interact and where the chatbot falls short.
• Agent tracing — follow multi-step agent behavior and catch errors early.
• Prompt templates — manage and iterate on prompts centrally.
• Custom dashboards, scoring LLM responses, PII masking, and feedback tracking — the operational layer around monitoring.

The practical workflow looks like this:

1. Create or activate a workspace (cloud) or stand up a self-hosted instance.
2. Add the 1-line integration to your app so LLM calls are captured.
3. Use the dashboard to inspect traces, replays, and analytics.
4. Iterate on prompts and monitor whether quality improves.

The expected result is that you go from "we shipped an LLM feature" to "we can see what it's doing and why it fails" without building that observability layer yourself.

Which setup fits your team

• Choose the cloud workspace if you want the fastest start, don't mind Lunary hosting the data, and are comfortable with per-user pricing starting at $20/month with payment required up front.
• Choose self-hosting if data ownership, residency, or internal security rules are non-negotiable, and you have the capacity to run the service.
• Request a demo if you're an enterprise evaluating custom terms, since published pricing only covers the standard cloud entry point.

Common sticking points to plan for

• Payment before activation means you can't casually spin up a workspace to "just look around" — decide on budget first.
• Per-user pricing can surprise teams that expect usage-based billing; estimate your seat count before committing.
• Self-hosting shifts operational burden to you. "Self Hostable" means the capability exists, not that it's zero-maintenance.
• The 1-line integration is the starting point, not the whole job. Getting value from analytics, tracing, and prompt management still requires you to review the data and act on it.

For the most current deployment details and pricing, verify against Lunary's own site, since these are the figures and capabilities it publishes today.

Lunary is an observability and prompt management platform for LLM-based applications, positioned for teams building internal tools, customer-facing chatbots, and autonomous agents. Its feature set covers four areas: analytics and replay, prompt management, tracing and dashboards, and quality/safety controls. The platform is available as a cloud workspace starting at $20 per user per month, and payment is required to activate a workspace; self-hosting is also listed as an option for teams that want to own their data.

Analytics and chat replay

Lunary's chatbot analytics are aimed at showing how an LLM performs in real time and how users actually interact with it — the gap between intended behavior and observed behavior. Chat replays let you reconstruct individual conversations rather than only reading aggregate metrics, which is the practical way to debug a bad answer or an abandoned session.

Prompt management

Prompt templates are a first-class feature, so prompts live in the platform rather than being hardcoded across your codebase. Combined with the 1-line integration the site advertises, this means you can change prompt content without redeploying the application. The listed capabilities include prompt templates alongside chat replays and analytics, which supports iterating on prompts against real traffic.

Tracing, classification, and dashboards

For agent and multi-step use cases, Lunary provides agent tracing so you can follow execution across steps and catch errors early. Topic classification groups incoming requests into categories, which is useful for spotting which user intents are failing. Custom dashboards let you assemble the metrics that matter to your team instead of relying on a fixed view.

Quality and safety controls

The platform includes score LLM responses, PII masking, and feedback tracking. Scoring and feedback tracking give you a way to attach quality signals to individual responses, while PII masking reduces the risk of sensitive data appearing in logs and replays.

Feature summary

How to decide if it fits

Choose Lunary if you need observability and prompt management in one place and want to avoid stitching together separate tools for replay, tracing, and prompt versioning. The self-hostable option matters if data ownership is a requirement. If you only need basic logging and never change prompts in production, the analytics and prompt features may be more than you need — in that case, confirm the current plan details on the pricing page before committing, since workspace activation requires payment.

Lunary is an observability and prompt management platform for LLM-based applications. It gives teams the tools to monitor, debug, evaluate, and improve AI experiences — chatbots, customer support bots, internal knowledge assistants, and autonomous agents — so they can ship and scale AI with more confidence. It's aimed at AI teams ranging from next-gen startups to established enterprises, and it's relevant if you're building anything that calls an LLM and you need visibility into how it behaves in production.

What problems it addresses

LLM apps are hard to reason about after deployment. A prompt that worked in testing can drift, a retrieval step can return the wrong document, and a user can get a confidently wrong answer with no obvious trace of why. Lunary's stated focus is closing that gap — understanding "the gap between your chatbot and your users" by showing how your LLM performs in real time and how users actually interact with it.

Concretely, that means answering questions like:

• Which conversations ended in frustration or escalation?
• What did the model actually receive and return on a given turn?
• Is a prompt change improving or hurting response quality?
• Are users engaging with the agent the way you intended?

Core capabilities

Based on the platform's own feature list, Lunary covers:

It also lists self-hostable deployment and own your data as options, plus a 1-line integration — meaning the intended setup is to drop in a small hook rather than rebuild your logging stack.

Who it fits

Lunary's own framing splits use cases into four patterns, which map cleanly onto whether it's a fit for you:

• Chatbot analytics — you have a user-facing bot and want to see where it underperforms.
• Internal knowledge — you're automating access to company documents and workflows.
• Customer support — you're building a docs-grounded support bot and want to track satisfaction.
• Agents — you're running autonomous multi-step tasks and need real-time monitoring and early error detection.

If your app is a single LLM call with no users to observe and no quality bar to track, the observability layer adds little. If you have real traffic, multiple prompt versions, or agents taking multi-step actions, the tracing and replay features are where the value concentrates.

Pricing and getting started

The site states cloud workspaces start at $20 per user / month, and that payment is required to activate your workspace. There's a "Get started with Team" path and a "Request a demo" / "Schedule a Demo" option for teams that want a walkthrough first. If data residency or compliance matters to you, the self-hostable option is the relevant one to evaluate — the site lists it as a capability but doesn't publish self-host pricing or terms, so that's a question for their team.

A practical first step: pick one LLM feature you already have running, integrate it, and watch a week of real conversations before deciding whether the dashboards and scoring features change any decisions you make. If they don't, the platform isn't earning its cost yet.

Lunary is built for "every LLM use-case," covering both internal tools and customer-facing applications. Its site names three concrete application types it supports: customer support chatbots, internal knowledge assistants, and autonomous agents. If your product falls into one of those categories, Lunary's observability and prompt management features apply directly.

Customer support chatbots

Lunary's site describes this use case as building customer-facing chatbots that understand your documentation and provide accurate responses, then tracking satisfaction and improving continuously.

What that means in practice:

• The chatbot answers from your docs, so answer quality depends on retrieval and prompting.
• Lunary captures how users actually interact with the bot, so you can see where it falls short.
• You can score LLM responses and collect feedback, which turns vague "the bot feels off" complaints into something measurable.

A typical example: a support bot that handles subscription upgrade questions. When a user asks how to upgrade, the bot walks them through Settings > Billing. Lunary lets you replay that conversation, check whether the answer matched your actual billing flow, and flag responses that need prompt fixes.

Internal knowledge assistants

The second named use case is helping teams access company knowledge and automate workflows faster.

The site's example is a remote work policy question. An employee asks about the policy, and the assistant answers from an internal document (remote work up to 3 days per week, core hours 10 AM–4 PM). Lunary's role here is monitoring usage and improving from real feedback — so you can see which internal questions get asked most, where answers are wrong, and whether the assistant is actually reducing repetitive internal requests.

This category matters if you're building tools for employees rather than customers, since the failure modes differ: wrong internal answers erode trust quickly, and usage patterns tell you which documents need better coverage.

Autonomous AI agents

Lunary also covers agents that execute complex tasks. The site's example is an agent that creates a Q4 sales report, queries data, builds charts, and adds month-over-month comparisons.

For agents, the emphasis is on monitoring performance in real-time and catching errors early. Agent tracing is listed as a core capability, which fits multi-step tasks where a failure at step three is hard to diagnose from the final output alone. If your agent chains tool calls or data queries, tracing lets you see each step rather than just the end result.

What the platform provides across all three

The features listed on Lunary's site apply regardless of which application type you're building:

Lunary also lists self-hosting and a 1-line integration, so you can own your data and connect it without a large setup effort.

Is Lunary a fit for your application?

Lunary is a reasonable choice if you're building any of the following:

• A customer-facing chatbot that needs continuous quality improvement
• An internal knowledge assistant where you want to track usage and accuracy
• An autonomous agent with multi-step tasks that need tracing

It's less clearly aimed at one-off LLM scripts or simple single-prompt tools, since the value comes from monitoring, replaying, and improving interactions over time.

On cost: Lunary's site states cloud workspaces start at $20 per user per month, and payment is required to activate your workspace. Self-hosting is also listed as an option, which may matter if data ownership is a requirement for your team.