THE COMPARISON / A CLOSER LOOK

Portkey vs Vellum

Compare the listed plans, useful capabilities, and practical tradeoffs.

Prices and features reflect editorial records. Check vendor terms before you buy; documentary checks do not imply hands-on testing.

Portkey

🔴Developer

AI Gateway

Portkey is an AI gateway for routing, observing, securing, and governing production traffic across model providers, agents, and MCP tools.

SOURCE-CHECK COVERAGE

No scoped source-check record shown. Check current pricing and vendor terms before buying.

Starting Price

Free

Vellum

🔴Developer

LLM Development Platforms

Development platform for production LLM apps: prompt engineering, evaluation, retrieval, and workflow orchestration in one workspace.

SOURCE-CHECK COVERAGE

No scoped source-check record shown. Check current pricing and vendor terms before buying.

Starting Price

Starting around $500/mo

Feature Comparison

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FeaturePortkeyVellum
CategoryAI GatewayLLM Development Platforms
Pricing Plans3 listed plans3 listed plans
Starting PriceFreeStarting around $500/mo
Key Features
  • • OpenAI-compatible AI gateway
  • • Multi-provider routing across 1,600+ models
  • • Automatic fallbacks, load balancing, retries, and request timeouts
  • • Visual workflow editor for multi-step LLM pipelines with branching, tool use, and RAG
  • • Collaborative prompt engineering with version control and diff tracking
  • • Automated evaluation pipelines with custom scoring, LLM-as-judge, and regression testing

Portkey - Pros & Cons

Pros

  • ✓Centralizes routing, fallbacks, observability, and policy
  • ✓Supports multi-provider model access and MCP governance
  • ✓Virtualized access can reduce direct credential distribution
  • ✓Request and cost metadata can support team-level attribution

Cons

  • ✗Fetched pricing page was script-heavy and prices need confirmation
  • ✗Adds another dependency and latency hop to model requests
  • ✗Retries and caching require careful side-effect and privacy controls
  • ✗Gateway fees do not include underlying model-provider charges

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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