OpenLIT's Zero-code LLM Observability

Product Introduction

1. OpenLIT's Zero-code LLM Observability is an open-source platform designed to provide full-stack observability for AI agents and LLM applications without requiring manual code instrumentation. It leverages OpenTelemetry-native monitoring to track LLMs, VectorDBs, and GPU performance while integrating built-in guardrails, evaluations, a prompt hub, and secure secret management. The platform is fully self-hostable, allowing deployment in any environment while maintaining privacy and control over data.
2. The core value of OpenLIT lies in simplifying AI development workflows by automating observability, reducing manual overhead, and enhancing security for generative AI systems. It enables developers to monitor costs, performance, and errors in real time while streamlining prompt management and API key security.

Main Features

1. End-to-End Application and Request Tracing: OpenLIT provides granular visibility into LLM operations by tracing requests across multiple providers, including OpenAI, Anthropic, and Cohere. It automatically captures spans for response times, token usage, and error rates using OpenTelemetry, enabling detailed performance analysis without code changes. Users can visualize traces to identify bottlenecks and optimize latency or costs.
2. Cost and Performance Tracking: The platform monitors expenses across LLM providers, vector databases, and GPU utilization, correlating costs with usage patterns for revenue decision-making. Real-time dashboards display metrics like tokens per dollar, latency percentiles, and error rates, allowing teams to balance budget constraints with performance requirements.
3. Secure Prompt and Secret Management: OpenLIT includes a centralized repository for version-controlled prompt templates with dynamic variable substitution (e.g., {{variableName}}) and a vault for encrypting API keys. Secrets can be injected as environment variables in Python or Node.js, ensuring secure access without exposing credentials in application code.

Problems Solved

1. Lack of Visibility in LLM Operations: Developers often struggle to debug latency issues, cost overruns, or unexpected outputs in generative AI applications due to opaque API interactions. OpenLIT solves this by auto-instrumenting all LLM calls, vector database queries, and GPU usage, providing actionable metrics and traces.
2. Target User Groups: The platform serves AI engineers building LLM-powered applications, DevOps teams managing production deployments, and security teams requiring audit trails for sensitive data. It is particularly valuable for organizations scaling multi-LLM architectures or regulated industries needing self-hosted solutions.
3. Typical Use Cases: Teams use OpenLIT to compare LLM performance/cost during A/B testing, monitor real-time error rates in chatbots, and securely manage prompts across development stages. Financial institutions deploy it to track compliance with data residency laws through private hosting.

Unique Advantages

1. Native OpenTelemetry Integration: Unlike proprietary observability tools, OpenLIT builds directly on OpenTelemetry standards, ensuring compatibility with existing monitoring stacks like Prometheus or Datadog. This eliminates vendor lock-in and allows exporting traces to third-party systems.
2. Built-In AI-Specific Tooling: The platform uniquely combines observability with a prompt versioning system (supporting semantic versioning like v1.2.3-draft) and a secrets vault, which competitors typically address via separate tools. The integrated playground enables side-by-side LLM comparisons using live production data.
3. Zero-Code Deployment: Competitors often require manual SDK integration, but OpenLIT starts tracing with a single openlit.init() call in Python/TypeScript. Its Docker-based self-hosting supports air-gapped deployments, a critical advantage for enterprises with strict data governance requirements.

Frequently Asked Questions (FAQ)

1. How does OpenLIT integrate with existing LLM applications? OpenLIT requires adding one line of code (openlit.init()) to auto-instrument supported LLM libraries like OpenAI, LangChain, or LlamaIndex. For custom models, users can extend instrumentation via OpenTelemetry APIs while retaining zero-code setup for common providers.
2. Can OpenLIT be deployed on-premises? Yes, the platform offers a Docker Compose setup for fully private deployments, ensuring data never leaves your infrastructure. This is preferred for industries like healthcare or finance where cloud-based SaaS solutions are non-compliant.
3. Which LLM providers does OpenLIT support? OpenLIT currently supports OpenAI, Anthropic, Cohere, Hugging Face, and custom endpoints. Vector database coverage includes Pinecone, Weaviate, and Milvus, with plans to add Qdrant and Chroma in future updates.
4. How does OpenLIT handle data privacy? All telemetry data is processed locally when self-hosted, with optional anonymization for cloud exports. The codebase is auditable, and secrets are encrypted using AES-256-GCM, never stored in plaintext.
5. What metrics can I track for GPU utilization? OpenLIT monitors GPU memory usage, compute utilization percentages, and thermal metrics via integration with NVIDIA DCGM. This helps optimize resource allocation in inference pipelines and training workloads.