OpenLIT vs. Langfuse vs. Arize Phoenix: Choosing Your LLM Observability Stack

TL;DR: OpenLIT, Langfuse, and Arize Phoenix are all solid open-source LLM observability tools. They differ in architecture (OTel-native vs. proprietary), scope (tracing-focused vs. all-in-one platform), and operational model (self-hosted vs. cloud). This guide breaks down the tradeoffs so you can pick the right one for your team.

Why This Comparison Exists

If you're evaluating LLM observability tools, you've probably looked at all three. They show up in the same GitHub searches, the same Hacker News threads, and the same "awesome-llm" lists.

They overlap in core functionality — all three trace LLM calls, capture tokens and latency, and provide a UI to explore traces. But they diverge in philosophy, architecture, and feature scope.

This is an honest comparison. We built OpenLIT, so we're obviously biased — but we'll call out where the others are stronger, because engineers can smell marketing a mile away.

The Quick Comparison Table

Architecture: The Fundamental Difference

This is where the three tools diverge most. It affects everything downstream — from how you deploy to where your data ends up.

OpenLIT: OTel-Native

OpenLIT is built on OpenTelemetry from the ground up. The SDK produces standard OTLP traces, metrics, and logs. The data follows OpenTelemetry GenAI semantic conventions.

What this means in practice:

• Your data isn't locked in. Send it to ClickHouse (via OpenLIT's platform), Grafana Cloud, Datadog, New Relic, Elastic, SigNoz, or any OTLP-compatible backend. Send it to multiple backends simultaneously.

• You can use existing OTel infrastructure. If you already have an OTel Collector pipeline, OpenLIT data flows through it like any other telemetry.

• Standard tooling works. Jaeger, Zipkin, Grafana Tempo — they all understand the traces OpenLIT produces.

The tradeoff: the OpenLIT platform UI is optimized for its own ClickHouse backend. If you send data elsewhere, you're using that backend's native UI (which may not have LLM-specific views).

Langfuse: Proprietary API, Moving Toward OTel

Langfuse uses its own REST API for data ingestion. You send traces through the Langfuse SDK or their API, and data lands in their PostgreSQL (now ClickHouse in v4) backend.

What this means:

• Tight integration between SDK and UI. The SDK and platform are designed together, so the UI understands every data point perfectly.

• Less flexibility on the backend side. Data goes to Langfuse, not to your existing observability stack (though OTel export is on their roadmap).

• Mature cloud offering. Langfuse Cloud handles storage, scaling, and infrastructure for you.

Arize Phoenix: ML Observability Roots

Phoenix started as an ML observability tool and expanded to LLMs. The open-source Phoenix product focuses on local development and experimentation. The enterprise Arize AX product adds production-grade features.

What this means:

• Strong eval framework. Phoenix has deep evaluation capabilities, especially for embedding analysis and retrieval quality.

• ML + LLM in one tool. If you're doing both traditional ML and LLM work, Phoenix covers both.

• Enterprise path. Phoenix (OSS) → Arize AX (enterprise) is the natural upgrade path.

Where Each Tool Shines

Choose OpenLIT If...

You want vendor-neutral observability. Your data stays in OTLP format. You can switch backends without re-instrumenting your code. If you leave OpenLIT tomorrow, your data and your OTel Collector pipeline still work.

You're already invested in OpenTelemetry. If your org uses OTel for backend services, OpenLIT extends the same pipeline to LLM workloads. Same Collector, same Grafana dashboards, same alerting.

You need GPU monitoring. Nobody else offers GPU metric collection as OTel signals. If you run self-hosted inference (vLLM, Ollama, TGI), this is a differentiator.

You want an all-in-one platform. Prompt Hub + Vault + OpenGround + Guardrails + Evals + Fleet Hub — all in one Docker Compose. No stitching together separate tools.

You run Kubernetes. The OpenLIT Operator injects instrumentation via pod labels. No other LLM observability tool has a K8s operator.

Choose Langfuse If...

You want a managed cloud service. Langfuse Cloud is mature and handles scale. You don't want to run ClickHouse yourself.

Prompt management is your primary use case. Langfuse's prompt management is more mature, with features like prompt caching and A/B testing.

You prefer manual instrumentation. Langfuse uses explicit decorators (@observe) and SDK methods to create traces. Some teams prefer this level of control over auto-instrumentation.

Community size matters. Langfuse has a large community (24K+ GitHub stars) and extensive ecosystem integrations.

Choose Arize Phoenix If...

You need deep evaluation capabilities. Phoenix's eval framework is extensive — retrieval quality, embedding drift, custom evaluators. If evals are your primary concern, Phoenix is strong here.

You're doing both ML and LLM work. Phoenix covers traditional ML observability (feature drift, model performance) alongside LLM tracing.

You want an enterprise upgrade path. If you need SOC2, RBAC, audit logs, and dedicated support, Arize AX is the commercial offering built on Phoenix.

You care about embedding analysis. Phoenix has unique capabilities for visualizing and analyzing embedding spaces — useful for understanding retrieval quality in RAG.

The Integration Breadth Question

One practical consideration: how many of your LLM providers and frameworks does each tool support out of the box?

OpenLIT Python SDK auto-instruments:

OpenAI, Anthropic, Cohere, Mistral, Groq, Google AI Studio, Bedrock, Azure AI, Vertex AI, Ollama, vLLM, Together, LiteLLM, HuggingFace, AI21, ElevenLabs, AssemblyAI, LangChain, LangGraph, LlamaIndex, CrewAI, Pydantic AI, OpenAI Agents, AG2, Haystack, Browser Use, MCP, Mem0, Agno, Dynamiq, Pinecone, Chroma, Qdrant, Milvus, Astra, PostgreSQL, FastAPI, Flask, Django, and more.

Total: 44+ auto-instrumented libraries, all activated with one init() call.

Langfuse supports most major providers but typically requires manual wrapping or decorator-based instrumentation for each integration.

Arize Phoenix covers major providers and has strong LangChain/LlamaIndex support, but the integration list is smaller than OpenLIT's.

Self-Hosting Comparison

OpenLIT's ClickHouse backend is designed for high-throughput telemetry from day one. Langfuse's recent migration from PostgreSQL to ClickHouse (v3 → v4) reflects the same realization.

What About Lock-In?

This is the question that matters most long-term.

OpenLIT: Your data is OTLP. If you stop using OpenLIT's platform, you can point the same openlit.init() to any OTLP backend. Your instrumentation code doesn't change. If you stop using the OpenLIT SDK, your OTel data (from other sources) still flows to whatever backend you use.

Langfuse: Your data is in Langfuse's format. Migrating to another tool means re-instrumenting your code and potentially exporting/transforming historical data.

Phoenix: Similar to Langfuse — proprietary data format with some OTel interoperability.

If vendor independence is important to your organization, OTel-native is a meaningful advantage.

The Honest Gaps

We said we'd be honest. Here's where OpenLIT has room to improve:

• No managed cloud. You have to self-host. If you don't want to run ClickHouse, this is a real barrier.

• Smaller community. Langfuse has more GitHub stars and a larger user base. More community content, more Stack Overflow answers.

• Evaluation depth. Arize Phoenix's eval library is deeper and more configurable than OpenLIT's current offering.

• Manual annotation UI. Langfuse's UI for human-in-the-loop annotation and labeling is more polished.

Decision Framework

Here's a simple way to decide:

Do you already use OpenTelemetry?
├── Yes → OpenLIT (extends your existing pipeline)
└── No
    ├── Do you want a managed cloud service?
    │   ├── Yes → Langfuse Cloud or Arize AX
    │   └── No
    │       ├── Is GPU monitoring important?
    │       │   ├── Yes → OpenLIT
    │       │   └── No
    │       │       ├── Is deep evaluation your priority?
    │       │       │   ├── Yes → Arize Phoenix
    │       │       │   └── No → OpenLIT or Langfuse (try both)
    │       └── Do you run Kubernetes?
    │           ├── Yes → OpenLIT (K8s operator)
    │           └── No → Any of the three

Getting Started with OpenLIT

If you want to try OpenLIT:

# Start the platform
git clone https://github.com/openlit/openlit.git && cd openlit
docker compose up -d

# Instrument your app
pip install openlit

import openlit
openlit.init()  # sends to localhost:4318 by default

Dashboard at http://localhost:3000. Traces start appearing within seconds.

FAQ

Can I migrate from Langfuse to OpenLIT?

For new data, yes — switch the SDK from Langfuse to OpenLIT and traces start flowing to OpenLIT. Historical data migration would require exporting from Langfuse and importing into ClickHouse, which is not currently automated.

Do they all support OpenTelemetry?

OpenLIT is OTel-native (data format is OTLP). Langfuse has announced OTel export support on their roadmap. Arize Phoenix has some OTel interoperability but isn't OTel-native.

Can I use OpenLIT's SDK with Langfuse's backend?

Not directly — Langfuse expects its own API format. However, since OpenLIT outputs OTLP, you can route OpenLIT data to any OTLP-compatible backend. Langfuse has been listed as a destination in OpenLIT's docs for specific bridge configurations.

Which one has the best UI?

This is subjective. Langfuse has the most polished trace exploration UI. Arize Phoenix has unique embedding visualizations. OpenLIT has the broadest feature set (dashboards, Prompt Hub, OpenGround, Fleet Hub) in a single UI. Try the demos and decide for yourself.