GalileoEnterprise
An enterprise platform for evaluating, monitoring, and guarding AI agents and LLM applications, including real-time protection against unsafe outputs.
Analytics comparison
Galileo vs LangSmith
Pricing, pros, cons, and ideal use cases — side by side.
LangSmithFreemium
An observability and evaluation platform for LLM and agent applications — tracing, datasets, automated evals, and production monitoring in one place.
At a glance
| Galileo | LangSmith | |
|---|---|---|
| Pricing | EnterpriseEnterprise pricing, quoted per organization. | FreemiumFree developer tier. Paid Plus and Enterprise plans add seats, longer trace retention, and deployment options. |
| Category | Analytics | Analytics |
| Ideal for | Enterprises running agents and LLM apps at scaleRegulated organizations needing runtime safeguardsTeams needing both evaluation and live monitoring | Teams running agents in productionEnterprises needing eval and monitoringEngineering orgs standardizing AI observability |
Pros & cons
Galileo
Pros
- Evaluation, monitoring, and runtime guardrails in one platform
- Real-time detection of unsafe or off-policy outputs
- Metrics that work without ground-truth labels
- Built for enterprise scale
Cons
- Enterprise pricing and procurement
- More than small teams or simple apps need
- Onboarding is a real implementation effort
LangSmith
Pros
- End-to-end tracing for complex agent runs
- Datasets and automated evals in CI
- Production monitoring for quality and cost
- Framework-agnostic
Cons
- Most seamless within the LangChain ecosystem
- Trace volume can drive cost at scale
- Eval design is still real work
Which should you choose?
LangSmith is the lighter-weight option (Freemium), while Galileo sits higher on the pricing ladder (Enterprise). Galileo is built around enterprises running agents and llm apps at scale; LangSmith leans more toward teams running agents in production. Shortlist the one whose strengths line up with your biggest constraint.