OpenLIT's Zero-code LLM Observability

OpenLIT's Zero-code LLM Observability

OpenLIT's Zero-code LLM Observability is an open-source platform designed to simplify AI development workflows, particularly for Generative AI and Large Language Models (LLMs). It provides a suite of tools to help developers experiment with LLMs, manage prompts, track performance, and secure API keys. The platform is built with a focus on privacy, allowing users to see exactly what the code does or host it themselves.

Benefits

OpenLIT's Zero-code LLM Observability offers several key advantages:

• Simplified AI Development: Streamlines essential tasks like experimenting with LLMs, organizing and versioning prompts, and securely handling API keys.
• Enhanced Performance Visibility: Provides end-to-end tracing of requests across different providers to improve performance visibility.
• Comprehensive Error Monitoring: Tracks and logs application errors to help detect and troubleshoot issues.
• Cost Tracking: Monitors the cost of making requests, helping users make informed revenue decisions.
• Side-by-Side LLM Comparison: Allows users to test and compare different LLMs based on performance, cost, and other key metrics.
• Secure Secrets Management: Offers a secure way to store and manage sensitive application secrets.
• Easy Integration: Can be easily integrated into existing projects with minimal code changes.
• Real-Time Data Streaming: Provides real-time data streaming to help users make quick decisions and modifications.
• Low Latency: Ensures that data is processed quickly without affecting the performance of your application.
• Connect to Observability Platforms: Connect to popular observability systems with ease, including Datadog and Grafana Cloud, to export data automatically.

Use Cases

OpenLIT's Zero-code LLM Observability can be used in various scenarios:

• AI Development: Simplifies the process of experimenting with LLMs and managing prompts.
• Performance Monitoring: Helps developers monitor the performance of their AI applications and identify areas for improvement.
• Error Detection: Assists in detecting and troubleshooting application errors.
• Cost Management: Tracks the cost of making requests, helping users manage their budgets effectively.
• LLM Comparison: Allows users to compare different LLMs to find the best fit for their needs.
• Secrets Management: Provides a secure way to manage sensitive application secrets.

Additional Information

OpenLIT's Zero-code LLM Observability is an open-source project, making it easy to start. Users can run the platform with a simpledocker-compose up -dcommand. The platform is also OpenTelemetry native, ensuring seamless integration with existing projects. It offers granular usage insights, allowing users to analyze LLM, Vectordb, and GPU performance and costs to achieve maximum efficiency and scalability.