OpenLIT is an open-source observability platform built on OpenTelemetry, designed specifically for Generative AI and LLM applications. It provides unified traces, metrics, and cost tracking to simplify monitoring, improve performance visibility, and streamline AI development workflows with secure prompt and secrets management.
Free
How to use OpenLIT?
Integrate OpenLIT by adding `openlit.init()` to your LLM application code. It automatically collects traces, monitors exceptions, and tracks costs without significant code changes. Use the platform to visualize request flows, compare LLM performance, manage prompts with versioning, and securely handle API keys. This helps developers quickly identify performance bottlenecks, optimize costs, and troubleshoot errors in real-time.
OpenLIT 's Core Features
Application and Request Tracing: Provides end-to-end tracing of requests across different providers to improve performance visibility and detailed span tracking.
Exceptions Monitoring: Automatically monitors and logs application errors with detailed stacktraces and seamless integration using Python and TypeScript SDKs.
Cost Tracking: Tracks usage costs for LLMs, VectorDB, and GPU to help make informed revenue decisions and optimize budget allocation.
Prompt Management: Centralized repository for creating, editing, versioning, and using prompts with dynamic variable substitution for runtime customization.
Secrets Management: Secure vault for storing and managing sensitive application secrets, with easy retrieval and environment variable integration.
OpenTelemetry Native: Seamless integration with OpenTelemetry for effortless data collection and compatibility with popular observability systems like Datadog and Grafana.
Real-Time Data Streaming: Streams data for quick visualization and decision-making, ensuring low latency without affecting application performance.
OpenLIT 's Use Cases
AI Engineers can use OpenLIT to monitor LLM performance and costs in real-time, helping them optimize model selection and reduce operational expenses.
DevOps Teams implement OpenLIT to trace application requests and exceptions, enabling faster troubleshooting and improved system reliability in production environments.
Data Scientists leverage the platform to compare multiple LLMs side-by-side, making data-driven decisions on model performance and cost-effectiveness for research projects.
Startup Developers utilize OpenLIT's prompt management and secrets vault to streamline development workflows, ensuring secure and efficient handling of AI components.
Enterprise IT Managers deploy OpenLIT for granular insights into GPU and VectorDB usage, achieving better scalability and resource allocation across teams.
Software Architects integrate OpenLIT with existing observability tools to unify metrics and traces, enhancing overall application monitoring and compliance reporting.
