LLM-powered products fail in new and surprising ways: a prompt change quietly degrades accuracy, a retrieval step returns irrelevant sources, latency spikes only for certain user segments, or “helpful” answers become confidently wrong. If you’re shipping AI features into production, LLM observability isn’t a nice-to-have-it’s how you keep quality, cost, and reliability under control.

This guide compares three popular options-Langfuse, Galileo, and Logfire-through the practical lens of what teams actually need: tracing, evaluation, debugging, prompt/version management, and production monitoring. You’ll also get a decision framework, example workflows, and a quick FAQ optimized for featured snippets.

What “LLM Observability” Really Means (and Why Traditional APM Isn’t Enough)

In a classic web app, observability focuses on metrics, logs, traces, and errors. In an LLM app, you still need those-but you also need AI-native signals:

• Prompt and response capture (with redaction controls)
• Token usage and cost per request, user, model, and environment
• Traceability across chains/agents/tools (RAG steps, tool calls, rerankers, function calling)
• Quality evaluation (accuracy, groundedness, relevance, hallucination rate)
• Dataset-based regression testing (before shipping prompt/model changes)
• Human feedback loops (labeling, review queues)

Bottom line: LLM observability combines APM-style tracing with evaluation and AI debugging.

Quick Comparison: Langfuse vs. Galileo vs. Logfire

At-a-Glance Table (Best for What?)

| Tool | Best for | Strengths | Watch-outs |

|——|———-|———–|————|

| Langfuse | End-to-end LLM tracing + prompt management + evaluations | Strong LLM-native workflows (traces, prompts, datasets, eval pipelines) | You still need to define “what good looks like” and implement eval strategy |

| Galileo | LLM evaluation & quality analytics (especially for RAG) | Deep evaluation focus, quality metrics, debugging around correctness/grounding | Can be heavier than teams need if they only want tracing |

| Logfire | Application observability with strong developer ergonomics | Great for broader system-level logs/metrics/traces; pairs well with OpenTelemetry-style monitoring | Not as LLM-specialized for prompt/versioning and eval out of the box |

Tool 1: Langfuse (LLM Tracing + Prompt & Evaluation Workflows)

What Langfuse is designed to do

Langfuse is typically chosen when a team wants a single place to trace LLM requests, track costs, manage prompts, and run evaluation workflows tied to real production data.

Where Langfuse shines

1) Tracing built around LLM concepts

Langfuse generally fits well when you need:

• Multi-step chain traces (RAG pipeline steps, tool usage, agent actions)
• Prompt + response logging (with metadata)
• Token usage and latency breakdowns
• Correlation across user/session/feature flags

2) Prompt versioning and iteration

Teams love tools that reduce “prompt guesswork.” A prompt registry with versions helps you:

• Roll out prompt variants safely
• Compare performance across versions
• Revert quickly when quality drops

3) Evaluation connected to production traces

A practical evaluation workflow looks like:

• Sample production traces (filtered by intent, user type, language, etc.)
• Create datasets from real traffic
• Run offline eval (LLM-as-judge + heuristic checks + human review)
• Gate deployments based on score thresholds

Best-fit use cases

• Customer support copilots
• RAG-based search/knowledge assistants
• Internal productivity agents with tool calling
• Teams that need trace-to-eval workflows in one platform

Tool 2: Galileo (Evaluation-First LLM Observability)

What Galileo is designed to do

Galileo is most attractive when your primary pain is measuring and improving quality-not just seeing traces. If your stakeholders ask, “Is it correct?” and “Is it grounded?” more than “Why did latency spike?”, an evaluation-first platform often wins.

Where Galileo shines

1) Quality analytics and failure mode detection

Evaluation-first tooling typically helps you:

• Detect hallucinations and unsupported claims
• Measure groundedness for RAG outputs
• Diagnose “answer is fluent but wrong”
• Segment failures by topic, source quality, or query type

2) RAG-specific evaluation workflows

For retrieval-augmented generation, debugging isn’t just about the final answer:

• Was the retrieved context relevant?
• Was key information missing?
• Did the model ignore correct context?
• Did reranking degrade results?

Galileo-style evaluation workflows are built around those questions.

Best-fit use cases

• High-stakes domains (fintech, healthcare, legal-where “almost correct” is unacceptable)
• Complex RAG systems with multiple retrieval stages
• Teams prioritizing trust, correctness, and governance

Tool 3: Logfire (Developer-Friendly App Observability That Complements LLM Tooling)

What Logfire is designed to do

Logfire is often approached as general-purpose observability-the kind you’d use across services-rather than a dedicated LLM platform. It’s useful when you want:

• Consistent logs/metrics/traces across the whole app
• Strong debugging for Python services
• A simpler path to instrumenting non-LLM components (APIs, queues, DB calls)

Where Logfire shines

1) Full-stack visibility beyond the LLM call

LLM issues often originate upstream:

• A slow vector database query creates timeouts
• A caching layer fails and doubles token usage
• A feature flag routes the wrong prompt template
• A rate limit triggers retries and cost spikes

APM-style observability helps connect those dots.

2) Complements LLM-native platforms

Many teams pair:

• LLM platform (for prompts, evals, dataset testing)
• APM/observability (for infra, services, system reliability)

This “two-layer” approach is common and effective.

Best-fit use cases

• Mature engineering orgs standardizing observability across services
• Teams that already rely on OpenTelemetry-style instrumentation patterns
• Apps where LLM calls are only one part of a broader distributed system

How to Choose: A Practical Decision Framework

Choose Langfuse if you need…

• LLM-native tracing across chains/agents/tools
• Prompt registry/versioning
• Built-in workflow from traces → datasets → evaluations
• Cost tracking per model, feature, or user segment

Choose Galileo if you need…

• Evaluation-first focus with deep quality analytics
• Strong RAG evaluation and failure mode diagnosis
• A system designed around “Is this answer correct and grounded?”

Choose Logfire if you need…

• Broad application observability for your Python/backend stack
• Uniform logging/metrics/tracing across services
• A complement to LLM-specific platforms (rather than a replacement)

Real-World Example Workflows (What Teams Actually Do)

Workflow A: Prevent prompt regressions before deployment

1. Pull a dataset from production queries (last 7 days, filtered by intent)
2. Run evaluations on:

• current prompt version (baseline)
• candidate prompt version (new)

1. Compare:

• quality score deltas (accuracy/groundedness)
• latency and token costs

1. Promote only if quality improves and cost stays within budget

Best match: Langfuse or Galileo (depending on how evaluation-heavy you want to be)

Workflow B: Debug a “hallucination spike” after a model change

1. Segment failing sessions by model/version
2. Inspect traces:

• retrieved documents
• reranker outputs
• final prompt composition

1. Identify root cause:

• retrieval returning irrelevant context
• prompt accidentally removed citation instructions
• tool calling failed silently

1. Fix and validate with an eval suite

Best match: Galileo for quality analytics + either Langfuse for trace context or Logfire for system-level errors

Workflow C: Reduce cost without hurting quality

1. Track token usage by endpoint and prompt version
2. Identify outliers (long contexts, repeated tool calls, verbose answers)
3. Implement:

• context truncation policies
• caching for retrieval
• smaller model for low-risk intents

1. Verify quality via targeted eval sets

Best match: Langfuse for LLM cost/trace analysis + Logfire for infra bottlenecks

Implementation Tips (So Observability Actually Works)

1) Decide what to log-and what to redact

LLM logs can contain sensitive user data. Implement:

• PII redaction (emails, phone numbers, addresses)
• hashing for identifiers
• environment separation (dev/staging/prod)
• role-based access control for trace viewing

For a deeper look at securing access in production systems, see JWT done right for secure authentication.

2) Standardize your trace metadata

Add consistent tags:

• model, prompt_version, feature_name
• tenant_id, user_segment, language
• retriever, vector_db, index_version

This turns “searching for needles” into “filtering dashboards.”

3) Don’t rely on one metric

Quality is multi-dimensional. Combine:

• automated evals (LLM judge + heuristics)
• human review for edge cases
• production signals (CSAT, containment rate, deflection rate)

Featured Snippet FAQ

What is the best observability tool for LLM applications?

The best tool depends on your goal: Langfuse is strong for LLM-native tracing and prompt/eval workflows, Galileo is ideal for evaluation-first quality analytics (especially RAG), and Logfire works well for broader application observability that complements LLM tooling.

Do I need both tracing and evaluation for LLM apps?

Yes. Tracing helps you understand what happened (prompt, context, tool calls, latency, cost), while evaluation tells you whether the output was good (accuracy, groundedness, relevance). Production-grade LLM apps typically need both.

What should I track in LLM observability?

Track latency, token usage/cost, prompt versions, retrieval quality (for RAG), error rates, and quality scores (accuracy/groundedness). Also capture metadata for filtering by user segment, tenant, and feature.

Is general APM enough for LLM systems?

General APM is helpful but not sufficient. It’s great for infrastructure, services, and performance bottlenecks, but LLM apps also need prompt/version tracking, dataset testing, and output quality evaluation, which are AI-specific.

If you’re instrumenting distributed services end-to-end, distributed observability for data pipelines with OpenTelemetry provides a practical blueprint that maps well to LLM systems too.