Trace LLM requests + costs with OpenTelemetry monitoring
OpenLIT provides zero-code observability for AI agents and LLM apps. Monitor your full stack, from LLMs and VectorDBs to GPUs, without changing any code. See exactly what your AI agents are doing at every step. Catch problems before they reach users, improve response quality over time, and optimize costs as you scale. Ship reliable AI faster with complete visibility and control. It's OpenTelemetry native, open source, self-hostable, and works with 50+ providers.
Prompt Hub enables centralized prompt versioning, deployment, and management across applications. OpenGround playground allows side-by-side comparison of different LLMs and prompt variations to identify best-performing configurations before production deployment.
OpenLIT exports all telemetry as standard OpenTelemetry (OTLP) format, enabling seamless integration with any OTLP-compatible backend including Grafana, Datadog, New Relic, SigNoz, and Jaeger. Users avoid vendor lock-in and leverage existing observability investments.
OpenLIT provides distributed tracing with OpenTelemetry-native spans, automatic token counting, latency measurement, and cost tracking across 60+ LLM providers and frameworks. Users add one line of code (openlit.init()) to get full visibility into prompt/completion flows, bottlenecks, and request lifecycle without code changes to existing applications.
OpenLIT Kubernetes Operator provides zero-code automatic instrumentation via AutoInstrumentation CR deployment. Workloads are instrumented without rebuilds or code changes, perfect for containerized LLM applications and AI agents running on Kubernetes.
OpenLIT auto-calculates token counts (input/output) and costs per call with custom model pricing configuration. Fleet Hub provides unified cost visibility across multiple AI deployments and environments, enabling budget tracking and spend optimization by model, user, and environment.