Langfuse vs Helicone vs OpenLLMetry: Self-Hosted LLM Observability Comparison 2026

As organizations deploy LLM-powered applications to production, they quickly discover that traditional observability tools fall short. You need to trace prompt execution, track token costs, evaluate response quality, and debug hallucination issues — all in real-time. LLM observability platforms fill this gap by providing specialized tracing, evaluation, and monitoring for generative applications.

In this guide, we compare three open-source LLM observability platforms: Langfuse, Helicone, and OpenLLMetry. Each takes a different approach to the problem, and the right choice depends on your stack, your priorities, and how deeply you want to integrate observability into your development workflow.

Langfuse Overview

Langfuse is an open-source LLM engineering platform offering observability, metrics, evaluations, prompt management, and a playground for testing. Built by the Langfuse team (YC W23), it integrates with LangChain, OpenAI SDK, LiteLLM, and more via OpenTelemetry.

Key stats:

• ⭐ 26,300+ GitHub stars
• 📅 Last updated: April 2026 (very active)
• 🐹 Full-stack platform with web UI, API, and SDK integrations
• Includes prompt management, datasets, A/B testing, and evaluation scoring

Langfuse is the most feature-complete of the three — it’s not just an observability tool but a full LLM engineering platform that covers the entire development lifecycle from prompt experimentation to production monitoring.

Helicone Overview

Helicone is an open-source LLM observability platform focused on simplicity. One line of code integration provides request logging, cost tracking, caching, rate limiting, and experimentation — all through a clean web dashboard. Also YC W23.

Key stats:

• ⭐ 5,500+ GitHub stars
• 📅 Last updated: April 2026 (active)
• 🦋 Designed for minimal integration overhead
• Built-in request caching and retry logic

Helicone’s philosophy is “one line of code” — you point your OpenAI SDK at Helicone’s proxy URL and get observability without changing your application code. It’s the quickest to deploy and integrate.

OpenLLMetry Overview

OpenLLMetry (by Traceloop) provides OpenTelemetry-native observability for LLM applications. It instruments your code using standard OpenTelemetry spans, meaning you can use any OTel-compatible backend (Jaeger, Grafana Tempo, SigNoz) to store and visualize your traces.

Key stats:

• ⭐ 7,000+ GitHub stars
• 📅 Last updated: April 2026 (active)
• 🔧 OpenTelemetry-based — works with any OTel backend
• Integrates with LangChain, OpenAI, LlamaIndex, Haystack, and more

OpenLLMetry is the most flexible option — it doesn’t lock you into a specific observability backend. If your organization already runs Jaeger, Grafana, or SigNoz, OpenLLMetry plugs right in.

Feature Comparison

Deployment: Docker Compose

Langfuse

Langfuse is the most complex to deploy — it requires PostgreSQL, ClickHouse, MinIO (S3-compatible storage), and Redis:

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services:
  langfuse-web:
    image: docker.io/langfuse/langfuse:3
    restart: always
    ports:
      - "3000:3000"
    environment:
      DATABASE_URL: postgresql://postgres:changeme@postgres:5432/postgres
      NEXTAUTH_URL: http://localhost:3000
      SALT: "mysalt"
      ENCRYPTION_KEY: "generate-a-256-bit-key-here"
      CLICKHOUSE_URL: http://clickhouse:8123
      CLICKHOUSE_USER: clickhouse
      CLICKHOUSE_PASSWORD: clickhouse
      S3_EVENT_UPLOAD_BUCKET: langfuse
      S3_EVENT_UPLOAD_ENDPOINT: http://minio:9000
      S3_EVENT_UPLOAD_ACCESS_KEY_ID: minio
      S3_EVENT_UPLOAD_SECRET_ACCESS_KEY: minioadmin

  langfuse-worker:
    image: docker.io/langfuse/langfuse-worker:3
    restart: always
    environment:
      DATABASE_URL: postgresql://postgres:changeme@postgres:5432/postgres
      CLICKHOUSE_URL: http://clickhouse:8123
      REDIS_HOST: redis

  postgres:
    image: postgres:16
    environment:
      POSTGRES_PASSWORD: changeme

  clickhouse:
    image: clickhouse/clickhouse-server:24.3.13.40
    environment:
      CLICKHOUSE_USER: clickhouse
      CLICKHOUSE_PASSWORD: clickhouse

  redis:
    image: redis:7

  minio:
    image: minio/minio:latest
    command: server /data
    environment:
      MINIO_ROOT_USER: minio
      MINIO_ROOT_PASSWORD: minioadmin

Helicone

Helicone requires PostgreSQL and ClickHouse:

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services:
  helicone-api:
    image: helicone/api:latest
    ports:
      - "8080:8080"
    environment:
      DATABASE_URL: postgresql://postgres:testpassword@postgres:5432/helicone
      CLICKHOUSE_URL: http://clickhouse:8123
      CLICKHOUSE_USER: default
      CLICKHOUSE_PASSWORD: ""

  helicone-web:
    image: helicone/front:latest
    ports:
      - "3000:3000"
    environment:
      NEXT_PUBLIC_HELICONE_API_HOST: http://localhost:8080

  postgres:
    image: postgres:17.4
    environment:
      POSTGRES_DB: helicone
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: testpassword

  clickhouse:
    image: clickhouse/clickhouse-server:24.3.13.40

OpenLLMetry

OpenLLMetry is just an SDK — there’s no server to deploy. You add the OpenLLMetry package to your Python application and configure it to send traces to your existing OTel collector:

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from opentelemetry import trace
from openllmetry import TracerWrapper

# Initialize OpenLLMetry with your OTel endpoint
wrapper = TracerWrapper(
    endpoint="http://your-otel-collector:4318",
    # Works with Jaeger, Grafana Tempo, SigNoz, etc.
)

# Your existing LLM code works automatically
from openai import OpenAI
client = OpenAI(base_url="http://localhost:4000/v1")
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)
# Traces are automatically captured and sent to your OTel backend

When to Choose Langfuse

• You want a complete LLM engineering platform — not just observability but prompt management, datasets, evaluation, and A/B testing in one tool
• Your team builds and iterates on prompts heavily — Langfuse’s versioned prompts and playground are unmatched
• You need built-in evaluation scoring — compare model outputs, score responses, and track quality metrics over time
• You don’t mind operational complexity — 6+ services to manage, but you get a full platform in return

When to Choose Helicone

• You want the fastest path to observability — point your SDK at Helicone’s proxy and you’re done
• Request caching is important — Helicone’s semantic cache can reduce LLM costs by 30-50%
• You need rate limiting built-in — Helicone handles rate limiting at the proxy level
• You prefer fewer moving parts — simpler than Langfuse but more feature-rich than OpenLLMetry alone

When to Choose OpenLLMetry

• You already run OpenTelemetry infrastructure — Jaeger, Grafana Tempo, SigNoz, or any OTel backend
• You don’t want vendor lock-in — OpenLLMetry is just an instrumentation layer; your data stays in your existing stack
• Your organization has strict data governance — traces go to your existing observability backend with all its access controls
• You want minimal additional infrastructure — no new databases or services to manage

Related Reading

For broader context on observability tooling, see our OpenObserve vs Quickwit vs Siglens comparison and SigNoz vs Coroot vs HyperDX guide. If you’re building the full LLM stack, our MLflow vs ClearML vs Aim experiment tracking guide covers the evaluation side.

FAQ

What is LLM observability?

LLM observability refers to the practice of monitoring, tracing, and analyzing the behavior of LLM-powered applications. Unlike traditional application monitoring, LLM observability tracks prompt inputs, model responses, token usage, costs, latency, and response quality — metrics that are specific to generative AI workloads.

Do I need a dedicated LLM observability platform, or can I use standard APM tools?

Standard APM tools (Datadog, New Relic, etc.) can track latency and error rates, but they lack LLM-specific features like prompt versioning, token cost tracking, response quality scoring, and semantic caching. Dedicated LLM observability platforms like Langfuse and Helicone provide these features out of the box.

Can I self-host all three platforms?

Yes. Langfuse and Helicone are fully self-hostable with Docker Compose. OpenLLMetry is an SDK, so there’s nothing to host — it sends data to whatever OpenTelemetry backend you already run (which can be self-hosted Jaeger, Grafana Tempo, SigNoz, etc.).

Which platform has the lowest operational overhead?

OpenLLMetry has the lowest overhead since it’s just an SDK — no new services to deploy. Helicone is next with ~3 services. Langfuse is the most complex with 6+ services but offers the most features.

Does OpenLLMetry work with non-Python languages?

OpenLLMetry has primary support for Python. For other languages, you can use the broader OpenTelemetry SDKs with LLM-specific span attributes, but you won’t get the automatic instrumentation that OpenLLMetry provides for Python.

Can I migrate from one platform to another?

Since Langfuse and Helicone store data in their own databases (PostgreSQL + ClickHouse), migration between them is non-trivial. OpenLLMetry has an advantage here — since it uses the standard OpenTelemetry format, you can switch backends without changing your instrumentation code.