What Sets Them Apart
The explosion of LLM-powered applications has created an entirely new category of observability challenges. Traditional APM tools capture HTTP requests and database queries but miss the AI-specific signals that matter most: prompt content, completion quality, token consumption, model latency, and hallucination rates. The three platforms in this comparison represent distinct architectures for solving these challenges, ranging from a pure instrumentation library to full-stack observability platforms, and the right choice depends on your existing infrastructure and operational maturity.
OPA, Cedar, and Casbin at a Glance
OpenLLMetry, built by Traceloop, is an open-source instrumentation framework that extends OpenTelemetry with AI-specific extensions. Rather than being a standalone platform, OpenLLMetry is a set of instrumentations that capture LLM-specific telemetry including prompts, completions, token usage, model parameters, and latency data, then ships this data in standard OpenTelemetry format to any compatible backend. This means you can send LLM traces to Datadog, New Relic, Honeycomb, Grafana, or any OTLP-compatible system alongside your existing application traces. Its semantic conventions are now officially part of the OpenTelemetry project.
Langfuse is a comprehensive open-source LLM engineering platform that provides tracing, prompt management, evaluation, and dataset management in a unified interface. Its v3 SDK is built natively on OpenTelemetry, meaning it can receive traces from OpenLLMetry and other OTEL-instrumented libraries alongside its own SDK traces. Langfuse excels at the full LLM development lifecycle: teams use it to trace complex chains and agent workflows, manage prompt versions with A/B testing, run evaluation pipelines with custom scoring, and build datasets from production traces for regression testing.
Helicone is designed for maximum simplicity: change your LLM provider's base URL to route through Helicone's proxy, and you get instant observability with zero code changes. This proxy architecture captures every request and response, providing cost tracking, latency monitoring, request caching, rate limiting, and usage analytics out of the box. Helicone supports over 100 models across major providers and offers both a managed cloud service and a self-hosted option. Its one-line setup has made it especially popular with teams that want immediate visibility without investing in instrumentation.
Policy Language, Performance, and Integration
The architectural philosophy behind each tool creates clear trade-offs. OpenLLMetry is the most vendor-neutral since it produces standard OpenTelemetry data that works with any backend, but it requires you to bring your own visualization and analysis platform. Langfuse provides the richest feature set with built-in evaluation workflows, prompt management, and dataset curation, but its full value requires adopting it as a primary LLM development tool. Helicone offers the fastest time to value with its proxy approach, but routing all LLM traffic through a proxy introduces a network hop and potential single point of failure.
For tracing depth and flexibility, Langfuse leads with its nested trace model that captures entire agent workflows including sub-spans for retrieval steps, tool calls, and intermediate LLM completions. OpenLLMetry provides similarly detailed traces since it instruments at the library level, capturing every call to OpenAI, Anthropic, LangChain, vector databases, and more. Helicone traces individual LLM calls effectively but has more limited visibility into the surrounding application logic since it only sees traffic that passes through the proxy.
Evaluation capabilities represent a major differentiator. Langfuse provides a complete evaluation framework where teams can define custom scoring functions, run automated evaluations, annotate traces manually, and build evaluation datasets from production traffic. OpenLLMetry focuses purely on instrumentation and leaves evaluation to whatever backend platform you choose. Helicone offers basic quality scoring and feedback collection but lacks the depth of Langfuse's evaluation pipeline for teams that need systematic quality assessment.
Cloud Support and Community
Cost tracking and management features are strongest in Helicone, which was designed with cost visibility as a primary use case. It provides detailed cost breakdowns by model, user, project, and time period with caching that can significantly reduce API spend. Langfuse also tracks costs and token usage with configurable pricing models. OpenLLMetry captures token counts and model information in its traces, but cost calculation depends on the backend platform you use for visualization.
Self-hosting and data privacy options exist for all three tools. OpenLLMetry is entirely self-hosted by design since it just produces telemetry data for your own backend. Langfuse offers a Docker-based self-hosted deployment alongside its managed cloud, making it suitable for organizations with strict data residency requirements. Helicone provides self-hosted options through Docker as well, though many teams use its cloud proxy for simplicity. All three are open source under permissive licenses.