Portkey vs Braintrust
Detailed side-by-side comparison to help you choose the right tool
Portkey
🔴DeveloperLLM Gateways & Infrastructure
AI gateway and control plane for production GenAI: routes calls across 250+ LLMs with one unified API, plus guardrails, prompt management, observability, budgets, and an MCP-aware agent runtime.
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CustomBraintrust
🔴DeveloperLLM Evaluation
End-to-end evaluation, prompt playground, and observability platform for teams shipping LLM products — the tool most AI teams pick when spreadsheets stop scaling and vibes stop being enough.
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FreeFeature Comparison
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Portkey - Pros & Cons
Pros
- ✓Provider-agnostic routing with declarative configs that an SRE can change without app deploys
- ✓Strong governance primitives (virtual keys, budgets, RBAC) that internal gateways usually skip
- ✓Built-in guardrails and observability remove the need for a separate vendor for each
- ✓Self-hosted and BYOC options make it viable for regulated and air-gapped deployments
Cons
- ✗Pricing page is JS-rendered and dollar amounts must be confirmed manually on site
- ✗Adds a network hop and latency overhead — small but non-zero next to direct provider calls
- ✗Overkill for single-provider single-app teams who do not need governance
- ✗Some advanced features (Agents, BYOC) require Enterprise contracts
Braintrust - Pros & Cons
Pros
- ✓Connects datasets, experiments, prompts, and production traces in one workflow
- ✓Python and TypeScript SDKs support code scorers and model-based judges
- ✓Side-by-side experiments make regressions visible before deployment
- ✓OpenTelemetry and major model-provider integrations reduce instrumentation work
Cons
- ✗The staged $249/month Pro price needs manual verification
- ✗LLM-as-judge scores still require calibration against human decisions
- ✗Teams must design representative datasets; the platform cannot supply product-specific truth
- ✗A full-stack platform can be more than a small prototype needs
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