Galileo vs Langfuse: how hard is migration/instrumentation, and do both support OpenTelemetry end-to-end?

Most teams don’t compare Galileo and Langfuse until they hit the same wall: you’ve instrumented traces, shipped a v1 agent, and now you’re trying to add real evals, guardrails, and OpenTelemetry (OTel) integration without breaking everything. The questions quickly become practical: how hard is migration and instrumentation, and can you get true end-to-end OTel without duct-taping three systems together?

This explainer walks through those questions from a builder’s perspective—what it takes to instrument, what migration actually looks like, and how OpenTelemetry support differs.

Quick Answer: Galileo and Langfuse both help you instrument LLM/agent traces. Langfuse focuses on observability and analytics; Galileo extends that into an eval-to-guardrail system that runs evaluators in production at sub-200ms latency. Instrumentation effort is similar at the trace level, but migration complexity depends on how deeply you use Langfuse-specific constructs and whether you need live guardrails and 100% OTel coverage. Galileo is designed to plug into OTel-style traces (sessions → traces → spans) and can act as the eval and protection layer on top of your existing telemetry.

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The Quick Overview

• What It Is:
  A side-by-side look at Galileo vs Langfuse specifically around migration effort, instrumentation model, and OpenTelemetry end-to-end support for LLM apps, RAG systems, and agents.

• Who It Is For:
  Teams already shipping LLM apps or agents who have some tracing in place (maybe in Langfuse or homegrown) and now need deeper evaluation, real-time guardrails, and OTel-compliant observability without a painful rewrite.

• Core Problem Solved:
  You don’t just need “LLM logs.” You need a system that (1) ingests your existing traces, (2) runs domain-specific evals on 100% of traffic, and (3) turns those evals into production guardrails—without breaking your telemetry stack or blowing your latency and cost budget.

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How It Works

At a high level:

• Langfuse:
  A developer-focused tracing and analytics layer. You instrument your LLM calls, prompts, and spans using their SDK. You get dashboards, metrics, and prompt versions, plus some scoring/evals tied to traces. It’s strong on “see what happened” and “analyze performance,” lighter on “turn evals into guardrails that intercept traffic automatically.”

• Galileo:
  An AI reliability platform that starts from the same tracing primitives—sessions → traces → spans—but adds an Evaluation Engine, Signals (pattern detection), and Protect (real-time guardrails). Galileo treats evals as operational primitives: they’re trained/tuned, distilled into small models (Luna / Luna-2), and run on a low-latency inference stack so you can evaluate 100% of production traffic and block/redact/override in <200ms.

Instrumentation flows look like this:

1. Instrumentation / Trace Capture

   • You instrument your app using either:
     • Langfuse SDKs (if you’re on Langfuse), or
     • Galileo SDKs/APIs or OTel-compatible traces (if you’re on Galileo).
   • In both cases, you record sessions, traces, spans, prompts, completions, and tool calls.

2. Evaluation & Analysis

   • Langfuse:
     • Attach scores to traces. Often powered by external LLM-as-judge or custom scoring logic you host.
     • Use dashboards/analytics to inspect model quality, latency, and cost.
   • Galileo:
     • Use the Evaluation Engine with 20+ out-of-the-box evaluators (RAG quality, hallucination detection, safety/security, agent tool correctness) plus custom evaluators.
     • Capture SME labels and live feedback, then tune evaluators via CLHF.
     • Distill evaluators into Luna / Luna-2 small language models so they run cheaply and quickly at production scale.

3. Production Guardrails & OTel Integration

   • Langfuse:
     • Primarily observability and analytics. Guardrail-like behavior typically lives in your own app code calling out to LLM evaluators, then writing results back.
   • Galileo:
     • Protect runs evaluations inline on every request with sub-200ms budget, 10–20 guardrail metrics at once, at ~97% lower cost than GPT-style judges.
     • Guardrail policies trigger actions (block, redact, override, webhook) in real time.
     • Signals analyzes 100% of traces to surface unknown failure patterns, which you can turn into new evaluators with a click.
     • Apollo/OTel-compatible structure means you can wire it into your telemetry backend (Datadog, Grafana, etc.) and keep a coherent OTel story.

From an implementation standpoint, instrumentation is about mapping your current trace model to sessions → traces → spans, then deciding where evals run and how their outputs feed guardrails and OTel.

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Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Agent & LLM Tracing (Both)	Capture user sessions, traces, spans, prompts, completions, and tool calls.	Establishes a consistent view of agent behavior for debugging and analytics.
Evaluation Engine & Evals in Production (Galileo)	Runs 20+ out-of-box evals + custom evaluators offline and online, distills them into Luna / Luna-2 SLMs for sub-200ms, low-cost scoring at 100% traffic.	Turns evaluation into a continuous, production-safe guardrail layer instead of a one-off offline test.
Signals vs Manual Exploration	Galileo Signals scans all traces to detect unknown failure patterns; Langfuse emphasizes manual analysis and dashboarding.	Lets you find “unknown unknowns” (prompt injections, policy drift, cascading tool failures) without relying on someone to search logs.
Protect Guardrails vs DIY Logic	Galileo Protect intercepts requests/responses and executes guardrail policies: block, redact, override, or webhook based on eval scores. Langfuse usually relies on your own code for runtime decisions.	Reduces custom glue-code and ensures eval results directly control agent actions, with versioning and rollbacks.
OpenTelemetry Integration	Both can integrate with OTel-style tracing; Galileo is explicitly built around sessions → traces → spans and log streams that fit into existing observability stacks.	Preserves your existing telemetry investments and lets you treat AI evaluations as first-class signals in your SRE/observability pipeline.

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Ideal Use Cases

• Best for teams deeply invested in observability & dashboards (Langfuse-first):
  Because Langfuse has a strong UX for tracing, debugging, and analytics if your primary need is “see what my agents and LLM calls are doing,” and you’re comfortable running your own evaluation and guardrail logic in app code.

• Best for teams that need eval-to-guardrail and OTel-aligned governance (Galileo-first):
  Because Galileo doesn’t stop at trace visibility; it operationalizes evaluation as governance. You can run 10–20 guardrail metrics per request at sub-200ms with ~97% lower cost than GPT-style judges, integrate results into OTel, and actually block/redact/override before users see failures.

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Limitations & Considerations

• Migration Complexity Depends on Your Current Depth of Usage:

  • If you only use Langfuse for basic tracing and a few scores, migrating to Galileo is mostly about remapping your instrumentation and replaying or streaming traces.
  • If you rely heavily on Langfuse-specific constructs (custom dashboards, query languages, tightly coupled analytics workflows), you’ll need to plan a staged migration: first mirror traces to Galileo, then reconstruct critical dashboards and evaluators.

• OpenTelemetry Coverage is a Stack-Level Question, Not Just a Tool Feature:

  • Both platforms can participate in an OTel-based stack, but they play different roles.
  • Langfuse: OTel-friendly tracing and logging for LLM/agent calls.
  • Galileo: evaluation and guardrail engine that can consume OTel-like traces and emit metrics/events back into your observability pipeline.
  • Full “end-to-end” OTel usually means: app → OTel SDK → trace backend (e.g., Tempo/Jaeger) + metrics backend, with Galileo or Langfuse providing AI-specific spans and metrics. You’ll still need to wire exporters appropriately.

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Pricing & Plans

Specific pricing structures change over time, but the operational question is: how much does it cost to run real evaluations at 100% of traffic?

• Galileo:

  • Built for production-scale evaluation and guardrails.
  • Uses Luna / Luna-2 small models and a purpose-built inference stack to achieve ~97% lower cost than GPT-style LLM judges while scoring 10–20 guardrail metrics in sub-200ms.
  • Plans typically scale with traces, evaluator usage, and deployment model (SaaS, VPC, on-prem).
  • Best for teams who need continuous evaluations on every trace, with predictable latency and cost, and enterprise controls (SOC 2 Type II, HIPAA-ready with BAAs, SSO, dedicated inference).

• Langfuse:

  • More aligned to usage-based pricing on traces and features like data retention, team seats, and possibly scoring features.
  • Evaluations are usually external (e.g., your own GPT-style judge or heuristics), so your evaluation spend is primarily whatever you pay your model provider plus Langfuse storage/analytics.
  • Best for teams who want cost-effective tracing and analytics, and are comfortable managing evaluation model costs separately.

For both, you’ll want to model cost at your expected QPS, including how often you will evaluate and what models you’ll use.

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Frequently Asked Questions

How hard is it to migrate from Langfuse to Galileo?

Short Answer:
If you’re using Langfuse mainly for tracing and basic scores, migration is straightforward. If you’ve built complex dashboards and workflows around Langfuse, plan a staged migration where you mirror traces into Galileo and gradually move eval and guardrail logic over.

Details:
A typical migration path looks like:

1. Inventory your current usage

   • What do you rely on Langfuse for today?
   • Traces only? Dashboards? Prompt version tracking? Custom scores?

2. Map your trace model to Galileo’s sessions → traces → spans

   • Galileo already thinks in these terms and can ingest via SDKs, APIs, or log streams.
   • For many teams, the structure is similar: sessions map to user conversations, traces to agent runs, spans to individual LLM/tool calls or workflow steps.

3. Start dual-writing traces

   • For a transition period, write traces to both Langfuse and Galileo.
   • This gives you confidence that Galileo’s view of sessions, cost, and latency matches reality before you turn Langfuse off.

4. Move evaluations into Galileo’s Evaluation Engine

   • Recreate existing scoring logic as Galileo evaluators (some may map directly to the 20+ out-of-box evals; others become custom evaluators).
   • Where you previously used GPT-style judges per trace, you can distill that logic into Luna / Luna-2 for lower cost and latency.

5. Turn evals into guardrails with Protect

   • Define guardrail policies that trigger actions based on evaluator scores (block unsafe outputs, redact PII, override hallucinated answers, notify via webhook).
   • Run them in shadow mode first, then enforce.

6. Decommission or repurpose Langfuse

   • Once you’re confident Galileo covers your observability and eval needs, you can reduce or eliminate Langfuse usage—or keep it as a secondary analytics layer if needed.

In practice, teams that haven’t deeply customized Langfuse can complete a migration in days to a few weeks, depending on how many evaluators and guardrail policies they want to set up.

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Do Galileo and Langfuse both support OpenTelemetry end-to-end?

Short Answer:
Both can participate in an OTel-based observability stack, but neither is a complete OTel backend by itself. Langfuse focuses on OTel-style tracing and analytics; Galileo layers evaluation and guardrails on top of traces and can feed metrics and events back into your OTel ecosystem.

Details:
End-to-end OpenTelemetry is more about how your whole stack is wired than a single tool’s feature:

• Instrumentation Layer

  • You can use OTel SDKs in your app to create spans for HTTP calls, DB queries, and LLM/agent operations.
  • Both Galileo and Langfuse can align with that model: sessions → traces → spans, with LLM/tool spans carrying prompts, completions, and metadata.

• AI-Specific Tracing

  • Langfuse: Offers SDKs that generate LLM/agent traces; you can bridge them into OTel by mapping or exporting spans.
  • Galileo: Ingests sessions/traces/spans via APIs/log streams and exposes a single view across them, aligned with your existing observability stack.

• Evaluation & Guardrail Signals

  • Langfuse: Stores scores as attributes/metrics associated with traces. To use them in OTel backends, you usually export or replicate them.
  • Galileo: Evaluations (hallucination risk, safety, PII, tool correctness, etc.) are first-class metrics. They can be emitted as logs/metrics that your OTel exporters forward to Datadog, Prometheus, or other backends, where they appear alongside traditional SRE metrics.

• Backend & Visualization

  • Neither Galileo nor Langfuse replaces your OTel backend (Tempo/Jaeger, Prometheus, etc.). Instead, they act as specialized layers for AI traces and evaluations.
  • “End-to-end OTel” often means:
    • OTel SDKs in services → OTel collectors → OTel backend
    • Galileo or Langfuse as an AI-aware layer that ingests trace data and returns evals/metadata.
    • Dashboards in your usual tools plus Galileo’s/ Langfuse’s UIs.

The practical distinction:

• If you want AI observability only, with OTel-style traces, Langfuse can fit well.
• If you want AI observability plus evaluation and real-time guardrails integrated into your OTel telemetry and incident workflows, Galileo is built to serve that role at 100% traffic coverage.

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Summary

If you’re choosing between Galileo and Langfuse—or considering migrating—the key questions aren’t just “who has traces?” Both do. The real questions are:

• Can you run your best evaluators continuously in production at 100% of traffic without blowing your latency and cost budget?
• Can those evaluations become real guardrails—blocking, redacting, or overriding behavior—rather than just numbers on a dashboard?
• Can you keep an OTel-consistent view of your system, where AI metrics sit alongside standard SRE signals?

Langfuse gives you strong observability for LLM apps: traces, analytics, and basic scoring. Galileo goes further by turning traces into a complete eval-to-guardrail system: 20+ out-of-box evaluators, Signals to detect unknown failure patterns, and Protect to enforce guardrails in sub-200ms with ~97% lower cost than GPT-style judges, across 100% of your traffic.

If your goal is reliable, governed agents in production—not just better screenshots of traces—Galileo is designed to be the evaluation and protection layer of your OTel stack.

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