Galileo (galileo.ai) vs Langfuse
Galileo (galileo.ai) vs Langfuse: side-by-side comparison of two memory observability & monitoring systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Galileo (galileo.ai) · Langfuse
Cost & capability
| Galileo (galileo.ai) | Langfuse | |
|---|---|---|
| Cost tier | premium | premium |
Where they differ (11)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Galileo (galileo.ai) | Langfuse | |
|---|---|---|
| Type | Real-time intent / belief drift detection | Memory ops as named spans + Agent Graphs |
| Created | 2021 (founded 2021 by Atindriyo Sanyal Vikram Chatterji Yash Sheth; seed May 2022) | 2023-03 (Langfuse founded March 2023; LLM observability beta summer 2023; GA Feb 2024) |
| Pricing | free: 5K traces/mo; Pro: $100+/mo (50K traces); Enterprise: custom (VPC on-prem); runtime guardrails on Enterprise only | free: 50K observations/mo; Pro: $59/mo; Team: $199/mo; Enterprise: custom; acquired by ClickHouse so pricing evolut… |
| Funding | $68M total ($10M seed + $13M Series A + $45M Series B Oct 2024); Scale Venture Partners + Premji Invest led Series B | acquired · 2026-01 |
| Backend storage | custom | Postgres + ClickHouse |
| Deployment | SaaS cloud; Enterprise: VPC or on-prem | SaaS cloud (Berlin + SF) or self-hosted Docker/K8s; ClickHouse native integration post-acquisition |
| API surface | REST, SDK: Python | REST, SDK: Python, JS/TS |
| Multi-tenancy | hard-isolation | namespace (org/project) |
| MCP | Yes — Galileo MCP Server documented; integrates with traces, prompts, datasets | via official adapter — langfuse-mcp |
| A2A | Yes — A2A is listed under Integrations Overview (alongside CrewAI, Google ADK, LangChain, etc.) | no data — searched langfuse.com, langfuse.com/docs, langfuse.com/integrations; A2A protocol not advertised (extensive framework integrations, MCP server present, OTel-native) |
| OpenTelemetry | first-class — OTel-native ingestion | first-class — native OTel SDK |
At a glance
| Galileo (galileo.ai) | Langfuse | |
|---|---|---|
| Section | Memory observability & monitoring | Memory observability & monitoring |
| Tier | T1 | T1 |
| Type | Real-time intent / belief drift detection | Memory ops as named spans + Agent Graphs |
| Created | 2021 (founded 2021 by Atindriyo Sanyal Vikram Chatterji Yash Sheth; seed May 2022) | 2023-03 (Langfuse founded March 2023; LLM observability beta summer 2023; GA Feb 2024) |
| Pricing | free: 5K traces/mo; Pro: $100+/mo (50K traces); Enterprise: custom (VPC on-prem); runtime guardrails on Enterprise only | free: 50K observations/mo; Pro: $59/mo; Team: $199/mo; Enterprise: custom; acquired by ClickHouse so pricing evolut… |
| Funding | $68M total ($10M seed + $13M Series A + $45M Series B Oct 2024); Scale Venture Partners + Premji Invest led Series B | acquired · 2026-01 |
| Backend storage | custom | Postgres + ClickHouse |
| Deployment | SaaS cloud; Enterprise: VPC or on-prem | SaaS cloud (Berlin + SF) or self-hosted Docker/K8s; ClickHouse native integration post-acquisition |
| API surface | REST, SDK: Python | REST, SDK: Python, JS/TS |
| Embedding | locked | — |
| Multi-tenancy | hard-isolation | namespace (org/project) |
| MCP | Yes — Galileo MCP Server documented; integrates with traces, prompts, datasets | via official adapter — langfuse-mcp |
| A2A | Yes — A2A is listed under Integrations Overview (alongside CrewAI, Google ADK, LangChain, etc.) | no data — searched langfuse.com, langfuse.com/docs, langfuse.com/integrations; A2A protocol not advertised (extensive framework integrations, MCP server present, OTel-native) |
| OpenTelemetry | first-class — OTel-native ingestion | first-class — native OTel SDK |
| Optimised for | memory operation tracing + drift / poisoning detection | memory operation tracing + drift / poisoning detection |
| Anti-fit | not for use cases that don't run agent workloads in production | not for use cases that don't run agent workloads in production |
Taxonomy
| Axis | Galileo (galileo.ai) | Langfuse |
|---|---|---|
| storage | vector | relational |
| retrieval | similarity | exact-match |
| persistence | cross-session | long-term |
| update | append-only | append-only |
| unit | episode | episode |
| governance | auditable | auditable |
| conflict | n/a | n/a |
Pros & cons
Galileo (galileo.ai)
Pros: First-mover in LLM observability; covers retrieval drift, hallucination, factuality alongside generic latency / cost — purpose-built for memory-driven agents.
Cons: Closed SaaS; pricing scales with traces; less open than Langfuse.
Langfuse
Pros: OSS-first LLM observability with a polished cloud option — self-hostable for compliance-sensitive shops; large open-source community.
Cons: Memory-specific observability is improving but less domain-specialized than Galileo's purpose-built memory views.