Galileo vs Helicone
Detailed side-by-side comparison to help you choose the right tool
Galileo
🔴DeveloperAI Evaluation
Galileo review 2026: enterprise AI evals, observability, guardrails, and Luna evaluator models for RAG and agents — features, pricing, pros, cons.
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CustomHelicone
🔴DeveloperLLM Observability
Open-source LLM observability and AI gateway — logs every prompt, response, cost, and latency across 20+ providers with a one-line proxy or async SDK, plus caching, retries, and prompt experiments.
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FreeFeature Comparison
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Galileo - Pros & Cons
Pros
- ✓Luna evaluators are dramatically cheaper than LLM-as-judge — eval coverage can stay on in production
- ✓End-to-end coverage: evals + traces + guardrails + agent root-cause from one vendor
- ✓Strong enterprise compliance posture (VPC, audit, SSO) suitable for regulated industries
Cons
- ✗No public pricing — every conversation starts with sales, which slows POC adoption
- ✗Heavier and more opinionated than open-source [/tools/langfuse](/tools/langfuse) or [/tools/arize-phoenix](/tools/arize-phoenix) — early-stage teams may find it overkill
- ✗Luna evaluators are proprietary — verify quality on your domain before assuming they replace LLM-judge in your stack
Helicone - Pros & Cons
Pros
- ✓5-minute proxy integration captures full traces, cost, and latency across 20+ providers
- ✓Real AI gateway features (caching, retries, fallback, key vault) replace a custom proxy
- ✓MIT-licensed and self-hostable on Postgres + ClickHouse — passes regulated procurement
Cons
- ✗Proxy mode adds a network hop unless self-hosted in your own region
- ✗Prompt experiment UX is less mature than dedicated eval platforms like Braintrust
- ✗Self-hosting requires running ClickHouse, which is an extra ops surface
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