OpenLIT

What is OpenLIT?

OpenLIT is a self-hosted, open-source platform designed to streamline AI development workflows, particularly for Generative AI and large language models (LLMs). It offers comprehensive tools for monitoring AI application performance via OpenTelemetry-native tracing and metrics, managing and versioning prompts securely, and safeguarding against prompt injection and jailbreak attacks. OpenLIT supports observability across the entire GenAI stack, including LLMs, vector databases, and GPUs, enabling developers to track costs, exceptions, and operational metrics with minimal code integration. Its modular SDKs and dashboards facilitate smooth transitions from experimentation to production while ensuring privacy and security.

Key Features

• OpenTelemetry-Native Observability

  Enables automatic tracing and metrics collection for AI apps, including detailed span tracking, latency, and cost monitoring across LLMs, vector DBs, and GPUs.

• Prompt Hub and Versioning

  Centralized management and version control of prompts with support for dynamic variables, ensuring consistency and ease of updates across AI agents.

• Secure Vault for API Keys

  Safely stores and manages sensitive API keys and secrets to prevent leaks and unauthorized access.

• Guardrails for AI Safety

  Built-in protections against prompt injection, jailbreak attempts, and sensitive data leaks to maintain application integrity.

• Programmatic AI Evaluations

  Automated evaluation of AI outputs for bias, toxicity, hallucinations, and other quality metrics to improve model reliability.

• GPU and Cost Monitoring

  Tracks GPU usage metrics and calculates costs for custom and fine-tuned models, aiding budgeting and resource optimization.

Use Cases

• AI Application Observability : Developers can monitor performance, latency, and errors in LLM-powered applications to maintain high reliability and optimize user experience.
• Prompt Management and Version Control : Teams managing multiple AI agents can centrally organize, update, and version prompts to ensure consistent behavior across deployments.
• Security and Compliance : Protect AI systems from injection attacks and data leaks by leveraging built-in guardrails and secure key management.
• Cost and Resource Optimization : Track usage and expenses of AI models and GPUs in real time to make informed decisions on scaling and budgeting.
• AI Output Quality Assurance : Automatically evaluate generated content for bias, toxicity, and hallucinations to maintain ethical and accurate AI responses.

FAQs