Galileo (galileo.ai) vs LangSmith
Galileo (galileo.ai) vs LangSmith: side-by-side comparison of two memory observability & monitoring systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Galileo (galileo.ai) · LangSmith
Cost & capability
| Galileo (galileo.ai) | LangSmith | |
|---|---|---|
| Cost tier | premium | mid |
Where they differ (12)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Galileo (galileo.ai) | LangSmith | |
|---|---|---|
| Cost tier | premium | mid |
| Type | Real-time intent / belief drift detection | Memory mutations as distinct span types |
| Created | 2021 (founded 2021 by Atindriyo Sanyal Vikram Chatterji Yash Sheth; seed May 2022) | 2023 (LangSmith beta summer 2023; GA Feb 2024 with $25M Series A) |
| Pricing | free: 5K traces/mo; Pro: $100+/mo (50K traces); Enterprise: custom (VPC on-prem); runtime guardrails on Enterprise only | Developer: free (5K traces/mo); Plus: $39/seat/mo (10K traces); Enterprise: custom (SSO/SAML; dedicated support) |
| Funding | $68M total ($10M seed + $13M Series A + $45M Series B Oct 2024); Scale Venture Partners + Premji Invest led Series B | LangChain total ~$35M+ ($3M seed Benchmark 2023; $25M Series A Sequoia Feb 2024; $125M Oct 2025 unicorn round at $1… |
| Backend storage | custom | custom (LangChain-managed) |
| Deployment | SaaS cloud; Enterprise: VPC or on-prem | SaaS cloud (US); Enterprise: dedicated tenant |
| API surface | REST, SDK: Python | REST, SDK: Python, JS/TS |
| Multi-tenancy | hard-isolation | namespace (workspace/project) |
| MCP | Yes — Galileo MCP Server documented; integrates with traces, prompts, datasets | via LangChain MCP adapters |
| A2A | Yes — A2A is listed under Integrations Overview (alongside CrewAI, Google ADK, LangChain, etc.) | no data — searched langchain.com/langsmith, docs.smith.langchain.com, docs.smith.langchain.com/observability/concepts; A2A protocol not advertised (OTel-based traces, webhook automations) |
| OpenTelemetry | first-class — OTel-native ingestion | first-class — OTel ingestion + export |
At a glance
| Galileo (galileo.ai) | LangSmith | |
|---|---|---|
| Section | Memory observability & monitoring | Memory observability & monitoring |
| Tier | T1 | T1 |
| Type | Real-time intent / belief drift detection | Memory mutations as distinct span types |
| Created | 2021 (founded 2021 by Atindriyo Sanyal Vikram Chatterji Yash Sheth; seed May 2022) | 2023 (LangSmith beta summer 2023; GA Feb 2024 with $25M Series A) |
| Pricing | free: 5K traces/mo; Pro: $100+/mo (50K traces); Enterprise: custom (VPC on-prem); runtime guardrails on Enterprise only | Developer: free (5K traces/mo); Plus: $39/seat/mo (10K traces); Enterprise: custom (SSO/SAML; dedicated support) |
| Funding | $68M total ($10M seed + $13M Series A + $45M Series B Oct 2024); Scale Venture Partners + Premji Invest led Series B | LangChain total ~$35M+ ($3M seed Benchmark 2023; $25M Series A Sequoia Feb 2024; $125M Oct 2025 unicorn round at $1… |
| Backend storage | custom | custom (LangChain-managed) |
| Deployment | SaaS cloud; Enterprise: VPC or on-prem | SaaS cloud (US); Enterprise: dedicated tenant |
| API surface | REST, SDK: Python | REST, SDK: Python, JS/TS |
| Embedding | locked | — |
| Multi-tenancy | hard-isolation | namespace (workspace/project) |
| MCP | Yes — Galileo MCP Server documented; integrates with traces, prompts, datasets | via LangChain MCP adapters |
| A2A | Yes — A2A is listed under Integrations Overview (alongside CrewAI, Google ADK, LangChain, etc.) | no data — searched langchain.com/langsmith, docs.smith.langchain.com, docs.smith.langchain.com/observability/concepts; A2A protocol not advertised (OTel-based traces, webhook automations) |
| OpenTelemetry | first-class — OTel-native ingestion | first-class — OTel ingestion + export |
| 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) | LangSmith |
|---|---|---|
| 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.
LangSmith
Pros: Best integration with the LangChain / LangGraph stack — debug memory + chain + agent in one trace; the de-facto trace tool for that ecosystem.
Cons: LangChain-shaped — works less well outside that ecosystem; closed SaaS.