Vercel AI GatewayvsDocling

Full side-by-side comparison — features, pricing, platforms, and which one wins in 2026.

Vercel AI Gateway

LLM APIs & Inference

Unified API gateway for routing app calls across hundreds of AI models

Full review →Website ↗

Docling

Data & ETL

IBM's document conversion tool for AI pipelines

Full review →Website ↗
FeatureVercel AI GatewayDocling
CategoryLLM APIs & InferenceData & ETL
PricingFree monthly credits; pay-as-you-go at provider list price with no markupFree (open-source)
GitHub Stars—
✓ More stars
15k
PlatformsWeb, APILinux, macOS, Windows
Key Features
  • ✓ Single API key
  • ✓ Hundreds of models
  • ✓ Unified model API
  • ✓ Provider routing and fallbacks
  • ✓ Automatic retries
  • ✓ Usage and spend monitoring
  • ✓ Bring Your Own Key
  • ✓ AI SDK and OpenAI-compatible APIs
  • ✓ PDF conversion
  • ✓ Table extraction
  • ✓ OCR
  • ✓ Markdown output
  • ✓ LlamaIndex integration
Pros
  • + One endpoint for many model providers
  • + Centralized usage, spend, and observability
  • + Automatic retries and fallbacks improve production resilience
  • + No token markup according to Vercel docs
  • + Works with AI SDK and OpenAI-compatible API clients
  • + Excellent PDF parsing
  • + Table extraction
  • + OCR capability
  • + IBM Research quality
  • + LlamaIndex integration
Cons
  • − Best fit for teams already building web apps or using Vercel/AI SDK
  • − Underlying provider terms and model limits still apply
  • − BYOK fallback can still consume AI Gateway credits
  • − Exact model pricing should be checked in the current Gateway model list
  • − Heavy dependencies
  • − Can be slow on large docs
  • − Python only
  • − Complex output format
Tags
ai-gatewaymodel-routingvercelai-sdkllm-apibyokobservability
documentspdfconversionibm

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