Langfuse integration with OpenTelemetry and Helicone

Langfuse provides an open-source engineering platform for LLM observability. Since January 2026, ClickHouse has owned the company. The platform utilizes a three-tier strategy to support diverse development environments. Tier 1 provides native SDKs for Python and JavaScript to offer type safety and IDE autocomplete. Tier 2 employs OpenTelemetry to allow developers in languages like Java, Go, or Rust to send traces via an OTLP endpoint. Tier 3 provides zero-code instrumentation for frameworks including LangChain and LlamaIndex. This structure allows teams to use standard OpenTelemetry SDKs and GenAI semantic conventions without needing a Langfuse-specific library. Langfuse uses a ClickHouse open source database to power its data model of traces, observations, and scores. Python users utilize the @observe() decorator to make tracing easy. This capability allows for the recording of every LLM call, tool execution, and sub-agent delegation as an individual span. These spans preserve the full delegation chain across complex multi-agent workflows.

Teams choosing between Helicone and OpenTelemetry must decide between a proxy-based gateway and a standardized tracing protocol. Helicone operates as an AI gateway that supports providers like OpenAI, Anthropic, and Google. It uses a proxy to provide request/response caching, rate limiting, and API key management. This position allows endpoint changes to enable logging without application code modifications. While Helicone provides a fast way to add request logging and cost tracking by simply changing a base URL, it fails to provide the deep agent debugging and nested trace analysis that teams need for complex multi-agent workflows. You know that managing prompt drift requires more than just logging tokens. Langfuse focuses on tracing, prompt management, and evaluation. It allows users to link prompts to generations and manage versioned prompts in a UI-editable playground. When users see a bad result in tracing, they jump to the playground to iterate on it. Langfuse also enables testing via datasets for regression and human-annotation evaluation. Langfuse supports LLM-as-judge, code evaluators, and custom evaluation pipelines via APIs and SDKs. Because Langfuse nests child spans beneath parent traces, it preserves the delegation chain in multi-agent workflows. Helicone provides session grouping for multi-step agent traces and multi-provider cost and latency visibility. Helicone also provides cost breakdowns per user, project, or provider. How will teams balance the low latency of a proxy with the need for complex trace analysis?

Deployment and cost structures vary between these two open-source tools. Langfuse allows self-hosting via Docker Compose or Helm charts for Kubernetes. This helps teams meet data-residency requirements. Langfuse offers a free Hobby tier and flat-fee Core and Pro plans. These plans include a monthly allotment of usage units. The Enterprise tier adds audit logs, SCIM, custom rate limits, and a support SLA. Braintrust requires a more involved setup with a split control-plane and data-plane deployment via Terraform, unlike the single-container self-host options for Langfuse. Helicone also supports self-hosting. Helicone offers a free Hobby tier with a monthly request cap. Helicone uses usage-based overages for requests and storage on its paid plans.