OpenLIT
Monitor and optimize generative AI applications with OpenTelemetry-native observability
Target Audience
- AI Engineers
- LLM Application Developers
- DevOps Teams
Hashtags
Overview
OpenLIT is an open-source platform that helps developers manage AI development workflows for large language models (LLMs) and generative AI. It provides performance monitoring, cost tracking, and error detection while keeping your data private through self-hosting options.
Key Features
OpenTelemetry Integration
Automatic tracing for AI applications using OpenTelemetry standards
Cost Tracking
Real-time expense monitoring for LLM API usage decisions
LLM Comparison
Side-by-side testing of different AI models' performance
Secrets Management
Secure storage for API keys and sensitive data
Low Latency
Lightweight data processing without slowing apps
Use Cases
Compare LLM performance metrics
Track API usage costs in real-time
Manage prompt versions & templates
Monitor application errors automatically
Securely handle API keys/secrets
Pros & Cons
Pros
- Open-source transparency and self-hosting
- Native OpenTelemetry compatibility
- Multi-LLM provider support
- Granular cost/performance analytics
Cons
- Primarily supports Python/TypeScript SDKs
- Requires Docker for self-hosting setup
- Limited native UI (relies on external observability platforms)
Frequently Asked Questions
How does OpenLIT integrate with existing systems?
Adds single-line initialization code to your app and works with OpenTelemetry-compatible platforms
What makes OpenLIT different from other observability tools?
Specialized tracking for LLM costs, performance, and AI-specific metrics
Can I self-host OpenLIT?
Yes, using Docker for private infrastructure deployment
Integrations
Reviews for OpenLIT
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