Portkey vs Vellum
Detailed side-by-side comparison to help you choose the right tool
Portkey
🔴DeveloperLLM Gateway & Observability
Production AI control plane: AI gateway, prompt management, observability, guardrails, and MCP gateway in front of 1,600+ LLM providers.
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FreeVellum
🔴DeveloperLLM Development Platforms
Development platform for production LLM apps: prompt engineering, evaluation, retrieval, and workflow orchestration in one workspace.
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CustomFeature Comparison
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Portkey - Pros & Cons
Pros
- ✓OpenAI-compatible API gives teams one integration point while still routing to 1,600+ models across providers such as OpenAI, Anthropic, Google, Mistral, AWS Bedrock, Azure OpenAI, Cohere, Together, Fireworks, and Groq.
- ✓Fallback and load-balancing are built into the gateway layer, so reliability policies can be configured centrally instead of duplicated across each application service.
- ✓Combines 5 production AI functions in one platform: AI gateway, prompt management, observability, guardrails, and MCP Gateway.
- ✓Prompt versioning and A/B testing help teams change production prompts with more control than hard-coded prompt strings in application code.
- ✓Observability includes per-request tracing and cost analytics, which is especially useful when several teams or products share model providers.
- ✓Enterprise options mentioned in the available content include VPC deployment, SSO, audit logs, and SOC 2 / HIPAA support.
Cons
- ✗Adds a hosted gateway hop between the application and the LLM provider, so teams must evaluate added latency and dependency risk.
- ✗The main paid self-serve plan is $49/month for 100k recorded logs, with overage fees beyond that included quota.
- ✗May be more platform than needed for teams that only want basic LLM request logging or tracing.
- ✗Advanced enterprise controls such as VPC deployment, SSO, audit logs, and compliance support appear oriented toward Enterprise contracts rather than small self-serve users.
- ✗Teams must learn Portkey-specific routing, guardrail, prompt, and gateway configuration concepts before they get full value.
Vellum - Pros & Cons
Pros
- ✓Prompts + evals + workflows + observability in one product reduces tool sprawl
- ✓Versioning/rollback story is stronger than most LLM platforms
- ✓LLM-as-judge + custom Python scorers cover most eval needs
- ✓Built-in RAG removes the need for a separate vector DB integration
- ✓Engineering-friendly UX — feels like a real IDE, not a chat playground
Cons
- ✗$500/mo entry tier is steep for solo developers
- ✗Visual workflows hit limits when logic gets very complex
- ✗Eval suite is solid but Braintrust goes deeper on eval-specific UX
- ✗Vendor-managed RAG limits some advanced retrieval setups
- ✗Pricing was reported recently — verify with sales before purchase
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