1
Honeycomb LLM Observability
Shares tags: build, observability & guardrails, cost/latency
AI ToolDead Man Walking
LLMonitor Review
LLMonitor, rebranded as Lunary.ai on December 10, 2023, is an AI tool providing self-hosted tracing and cost dashboards for applications built on Large Language Models.
shipped Nov 21, 2025buildpaid
1Rebranded to Lunary.ai on December 10, 2023.
2Offers a freemium pricing model, with paid plans starting at $29 per month.
3Provides self-hosted tracing and cost dashboards for LLM applications.
4Includes an API for integration and public documentation at llmonitor.com/docs.
Stork Quadrant
Dead Man Walking· 11/100
An LLM can do most of what this tool's UI promises. No moat, no agent presence.
“LLMonitor is a thin observability layer on top of LLM API calls. An LLM plus a logging library plus a BI tool replicates most of this. No proprietary data, no network effects, no regulatory gate. This will get absorbed by the platforms it monitors — OpenAI, Anthropic, and cloud providers are all building native cost dashboards.”
Defensibility · 0/100
- Physical-world coupling
- Regulatory moat
- Network liquidity
- Proprietary refreshing data
- High-trust catastrophic workflows
- Multi-party coordination
- Brand / community / taste
An LLM alone could replace
- Summarize LLM usage costs from API logs
- Generate a dashboard or report of token consumption by model
- Identify slow or expensive LLM calls from structured log data
- Write code to instrument an LLM app with tracing and logging
Agent-Readiness · 25/100
- Verified MCP
- Listed on agent surfaces
- Usage-based pricing
- Headless agent auth— https://llmonitor.com/docs (api-key auth)
- Public OpenAPI— https://llmonitor.com/docs
- Active changelog
- llms.txt
Score history · +4 pts over 2 re-scores
How to defend
Go vertical: pick one high-stakes industry (healthcare, finance) where LLM audit trails carry compliance weight, and own the liability. Or stop being a dashboard and become the SDK that agents call to enforce spend limits and routing rules programmatically.
- Ship an MCP server and list it on Stork — biggest single point gain (+25).
- Get listed in the Anthropic MCP registry, Cursor, or Claude Desktop (+20).
- Add a usage-based or per-call tier; per-seat-only pricing dies when agents replace seats (+15).
- Publish a public changelog and ship in the last 90 days — silence reads as abandonment (+10).
- Ship an /llms.txt file pointing agents to your most important docs (+5, easy win).
LLMonitor at a Glance
Similar Tools
Compare Alternatives
Other tools you might consider
View on Stork→
2
Baseten Traces
Shares tags: build, observability & guardrails, cost/latency
3
Log10
Shares tags: build, observability & guardrails, cost/latency
4
Spice.ai Cost Guard
Shares tags: build, observability & guardrails, cost/latency
</>Embed "Featured on Stork" Badge▼
<a href="https://www.stork.ai/en/llmonitor" target="_blank" rel="noopener noreferrer"><img src="https://www.stork.ai/api/badge/llmonitor?style=dark" alt="LLMonitor - Featured on Stork.ai" height="36" /></a>
[](https://www.stork.ai/en/llmonitor)
overview
What is LLMonitor?
LLMonitor is an LLM observability and analytics platform developed by Lunary.ai (formerly LLMonitor) that enables developers to monitor, debug, and optimize their AI agents and chatbots. It provides self-hosted tracing and cost dashboards for applications built on Large Language Models. The platform aims to help users track requests, analyze token usage, and manage expenses associated with LLM operations.
quick facts
Quick Facts
| Attribute | Value |
|---|---|
| Developer | Lunary.ai (formerly LLMonitor) |
| Business Model | Freemium |
| Pricing | Freemium starting at $0/mo, Pro at $29/mo |
| Platforms | Web, API |
| API Available | Yes |
| Integrations | Langchain (via callback handlers) |
| Founded | Not specified (rebranded Dec 10, 2023) |
| HQ | Not specified |
| Funding | Not specified |
features
Key Features of LLMonitor
Lunary.ai (formerly LLMonitor) offers a suite of features designed to provide comprehensive observability for LLM-powered applications. These capabilities include detailed tracing of LLM requests, cost management tools, and mechanisms for data optimization. The platform supports both hosted and self-hosted deployments, providing flexibility for various development environments and integrates with common LLM frameworks.
- 1Self-hosted tracing for LLM applications.
- 2Cost dashboards for monitoring token usage and expenses.
- 3API for programmatic access and integration.
- 4Instant search and filtering of logged data.
- 5Data labeling capabilities for fine-tuning LLM models.
- 6Replaying agent executions and tracing user conversations for debugging.
- 7Logging prompts and outputs for performance evaluation.
- 8Running assertions to validate agent functionality.
- 9Capturing user feedback for dataset creation.
- 10Tracking user activity patterns.
use cases
Who Should Use LLMonitor?
LLMonitor, now Lunary.ai, is primarily utilized by developers and AI engineering teams focused on building, deploying, and maintaining applications that leverage Large Language Models. Its functionalities are tailored to address the unique challenges of LLM application development, including performance optimization, cost control, and data quality improvement across various stages of the development lifecycle.
- 1**AI Application Developers**: For monitoring, debugging, and optimizing LLM-powered agents and chatbots.
- 2**Data Scientists & ML Engineers**: For labeling data to fine-tune models and improve application performance.
- 3**Product Managers**: For tracking user activity and gathering feedback to enhance AI product features.
- 4**Operations Teams**: For managing and reducing the operational costs associated with LLM API usage.
pricing
LLMonitor Pricing & Plans
Lunary.ai (formerly LLMonitor) operates on a freemium model, offering various tiers to accommodate different scales of LLM application development. The platform provides a free tier for initial exploration and testing, alongside paid plans that unlock advanced analytics and enterprise-grade features. Users are advised to verify pricing on the official Lunary.ai website at llmonitor.com/pricing for the most current details.
- 1**Free**: $0 per month.
- 2**Pro**: $29 per month.
- 3**Advanced analytics**: $199 per month.
- 4**Enterprise**: Custom plans starting from $599 per month.
competitors
LLMonitor vs Competitors
Lunary.ai (formerly LLMonitor) operates within the competitive landscape of LLM observability and monitoring platforms. It differentiates itself through its focus on self-hosted deployment options and ease of integration, particularly with frameworks like Langchain. The platform competes with both open-source solutions and managed cloud services, each offering distinct features and deployment models for LLM application development.
1
Langfuse is a comprehensive open-source LLM engineering platform offering end-to-end visibility with tracing, evaluations, prompt management, and metrics.
Langfuse offers both self-hosting and a managed cloud service, providing flexibility similar to LLMonitor's self-hosted focus but also a cloud option. It extends beyond basic tracing and cost, including prompt management and evaluation features.
2
Helicone provides a simple, gateway-first approach to LLM observability, focusing on monitoring, debugging, and improving LLM applications with minimal code changes.
Helicone is open-source and offers a self-hosted option, directly competing with LLMonitor's self-hosted model. It emphasizes ease of setup and provides unified billing and cost tracking across various LLM providers.
3
OpenLLMetry is an open-source observability product for LLM applications built on OpenTelemetry, allowing data capture from various LLM providers and frameworks to be sent to multiple destinations.
OpenLLMetry is open-source and focuses on leveraging OpenTelemetry for LLM observability, offering flexibility in data destination, which contrasts with LLMonitor's more integrated dashboard approach. Traceloop also provides a backend for accepting these traces.
4
SigNoz↗
SigNoz is a full-stack open-source observability platform that provides correlated traces, logs, and metrics for LLMs alongside traditional application monitoring.
SigNoz offers both self-hosting and a cloud version, aligning with LLMonitor's self-hosted focus. Its strength lies in providing comprehensive observability for the entire application stack, not just LLMs, which can be a broader offering.
5
OpenObserve is an open-source, Rust-based observability platform optimized for cost-efficient storage and SQL-native querying of logs, metrics, and traces, including LLM cost monitoring.
OpenObserve is open-source and self-hostable, directly aligning with LLMonitor's deployment model. Its primary differentiator is its cost-efficiency and SQL-native query capabilities for detailed cost attribution and analysis.
❓
Frequently Asked Questions
+What is LLMonitor?
LLMonitor is an LLM observability and analytics platform developed by Lunary.ai (formerly LLMonitor) that enables developers to monitor, debug, and optimize their AI agents and chatbots. It provides self-hosted tracing and cost dashboards for applications built on Large Language Models.
+Is LLMonitor free?
Yes, Lunary.ai (formerly LLMonitor) offers a free tier. Paid plans start at $29 per month for the Pro tier, $199 per month for Advanced analytics, and custom pricing starting from $599 per month for Enterprise solutions.
+What are the main features of LLMonitor?
Key features of Lunary.ai (formerly LLMonitor) include self-hosted tracing for LLM applications, comprehensive cost dashboards for token usage and expenses, an API for integration, instant search and filtering of logged data, and data labeling capabilities for fine-tuning LLM models. It also supports replaying agent executions, tracing user conversations for debugging, and logging prompts and outputs for performance evaluation.
+Who should use LLMonitor?
LLMonitor, now Lunary.ai, is designed for AI application developers and engineering teams who need to monitor, debug, and optimize their LLM-powered agents and chatbots. It is also beneficial for data scientists and ML engineers for data labeling, product managers for user activity tracking, and operations teams for managing LLM API costs.
+How does LLMonitor compare to alternatives?
Lunary.ai (formerly LLMonitor) differentiates itself with its self-hosted deployment options and ease of integration, particularly with frameworks like Langchain. Compared to Langfuse, it focuses more on tracing and cost dashboards. Unlike Helicone, which is gateway-first, LLMonitor provides an integrated dashboard. It offers a more integrated solution than OpenLLMetry's OpenTelemetry-based approach and is more LLM-specific than full-stack observability platforms like SigNoz or cost-optimized platforms like OpenObserve. It also differs from LangSmith's focus on LangChain-integrated debugging and evaluation, and Portkey's primary function as an AI Gateway.
For builders
This page is doing a job for someone else’s tool.
AI agents read it. Buyers find it. Backlinks accrue. Your tool can have one too — live in 24 hours, indexed by Claude, ChatGPT, and Perplexity, queryable via MCP.