Skip to content
Stork.AIstork.ai
AI Tool

LLMonitor Review

LLMonitor, rebranded as Lunary.ai as of December 10, 2023, is an AI observability and analytics platform designed to help developers monitor, debug, and optimize applications built on Large Language Models (LLMs).

shipped Nov 21, 2025buildpaid
Domain rating33Traffic rankoutside top 1MAI-readableblocked
BuildObservability & GuardrailsCost/Latency
LLMonitor - AI tool

Why it matters

1Rebranded to Lunary.ai on December 10, 2023.
2Offers self-hosted tracing and cost dashboards for LLM applications.
3Includes a free tier and paid plans starting from $29 per month.
4Provides an API and SDKs for Python and JavaScript for integration.

Stork’s verdict on LLMonitor

Lunary.ai provides detailed cost dashboards for LLMs, but its narrow focus won't cover broader application infrastructure.

LLMonitor reviewed by Stork AI · stork.ai/en/llmonitor

Specs

API Docs

View Documentation →

API Available

Yes, public API

overview

What is LLMonitor?

LLMonitor, rebranded as Lunary.ai as of December 10, 2023, is an AI observability and analytics platform developed by Lunary that enables developers to monitor, debug, and optimize applications built on Large Language Models (LLMs). It provides self-hosted tracing and cost dashboards for LLM applications, alongside features for data labeling and performance evaluation. The platform offers comprehensive tools for tracking requests, analyzing token usage, and managing expenses associated with LLM operations, aiming to enhance the reliability and efficiency of AI-driven applications.

features

Key Features of LLMonitor

LLMonitor (Lunary.ai) provides a robust set of features designed for comprehensive LLM observability, enabling developers to gain deep insights into their AI applications' performance and costs. These capabilities support debugging, optimization, and continuous improvement of LLM-powered systems.

  • Self-hosted tracing capabilities for LLM applications, offering deployment flexibility.
  • Detailed cost dashboards for monitoring token usage, latency, and overall expenses.
  • Real-time LLM monitoring for insights into response times, performance, and error rates.
  • Agent debugging features, including replaying agent executions and tracing user conversations.
  • Data labeling and feedback capture to create labeled training datasets for fine-tuning LLM models.
  • API for programmatic access and integration with existing development workflows.
  • Built-in SDKs for Python and JavaScript for seamless integration into LLM applications.
  • Instant search and filters for efficient data analysis and issue identification.

use cases

Who Should Use LLMonitor?

LLMonitor (Lunary.ai) is primarily designed for developers and teams building and managing applications powered by Large Language Models. Its features cater to various roles involved in the lifecycle of AI product development and operations.

  • Developers: For real-time monitoring, debugging complex agents, and identifying chatbot knowledge gaps in AI-driven applications.
  • Data Scientists & ML Engineers: For labeling data to fine-tune models and enhance application performance and quality.
  • Product Managers: For tracking user activity patterns, gathering user feedback, and improving AI product features based on usage data.
  • Operations Teams: For managing and reducing operational costs associated with LLM API usage through detailed cost dashboards and token analytics.

how to use

How to Use LLMonitor

To utilize LLMonitor (Lunary.ai), developers integrate its SDKs into their LLM applications to begin tracing requests and collecting operational data. The platform's web interface then provides dashboards for monitoring, debugging, and cost analysis.

  • 1Sign up for an account on the Lunary.ai website.
  • 2Install the appropriate SDK (Python or JavaScript) into your LLM application's codebase.
  • 3Configure the SDK to initialize tracing and send operational data to your Lunary.ai instance.
  • 4Deploy your LLM application to begin collecting real-time performance, cost, and usage data.
  • 5Access the Lunary.ai dashboard to monitor LLM application performance, debug agent executions, and analyze token usage and costs.
  • 6Utilize the platform's features to label data for fine-tuning models and gather user feedback for continuous improvement.

pricing

LLMonitor Pricing & Plans

LLMonitor, now operating as Lunary.ai, offers a freemium pricing model with several tiers designed to accommodate different scales of LLM application development. Users are advised to consult the official Lunary.ai website for the most current pricing details.

  • Free Plan: $0 per month, offering basic monitoring capabilities.
  • Pro Plan: $29 per month, providing enhanced features for individual developers or small projects.
  • Team Plan: $20 per month (mentioned as cheapest business-usable plan), offering 50,000 events and unlimited projects.
  • Advanced Analytics Plan: $199 per month, for more extensive analytics and larger-scale operations.
  • Enterprise Plan: Custom pricing starting from $599 per month, tailored for large organizations requiring specific features and support.

Pros

  • +Offers self-hosted deployment options for enhanced data control and privacy.
  • +Provides detailed cost dashboards and token analytics for effective LLM expense management.
  • +Includes robust agent debugging features like replaying executions and tracing conversations.
  • +Facilitates data labeling and feedback capture for continuous model fine-tuning and improvement.
  • +Features comprehensive API and SDKs (Python, JavaScript) for flexible integration into LLM applications.
  • +Supports real-time monitoring of LLM application performance, response times, and error rates.

Cons

  • −The recent rebranding from LLMonitor to Lunary.ai (December 10, 2023) may cause initial user confusion.
  • −Primarily focused on LLM observability, potentially offering less comprehensive monitoring for broader application infrastructure.
  • −Specific pricing tiers might require careful evaluation for very small teams or highly custom enterprise needs.
  • −Requires integration of SDKs into applications, which may involve code changes.

Policies

Pricing Page

View Pricing→

Similar Tools

LLMonitor vs Competitors

LLMonitor (Lunary.ai) operates within the competitive landscape of LLM observability and monitoring platforms, differentiating itself through its focus on self-hosted deployment options and comprehensive tracing and cost management for LLM applications. It competes with several established and emerging tools in this specialized domain.

1
LangfuseOn StorkCompare

Langfuse is an open-source LLM observability platform that integrates tracing, evaluation, and cost tracking into a single, self-hostable solution.

Similar to LLMonitor, Langfuse provides self-hosted tracing and cost dashboards for LLM applications, but it also offers comprehensive evaluation features for LLM outputs and prompt management.

2
HeliconeOn StorkCompare

Helicone is an open-source AI gateway that provides observability, caching, and cost tracking for LLM applications with minimal code changes through a proxy-based approach.

Helicone offers a proxy-based solution for LLM observability and cost management, which aligns with LLMonitor's cost dashboards, and is also open-source and can be self-hosted.

3
OpenLLMetry (by Traceloop)On StorkCompare

OpenLLMetry is an open-source, OpenTelemetry-native library specifically designed for LLM observability, enabling flexible data capture and integration with various observability backends.

While LLMonitor provides self-hosted tracing, OpenLLMetry focuses on the instrumentation layer using the OpenTelemetry standard, allowing users to send LLM trace and cost data to any compatible backend, including self-hosted ones.

4
OpenObserveOn StorkCompare

OpenObserve is an open-source, self-hostable observability platform built in Rust, optimized for cost-efficient storage and SQL-native querying of logs, metrics, and traces, including LLM cost monitoring.

OpenObserve provides a broader, full-stack observability solution that includes LLM-specific tracing and cost transparency, offering self-hosting capabilities and SQL-native querying for detailed analysis, similar to LLMonitor's core features.

See every LLMonitor alternative, compared→
Visit LLMonitor↗