UnstructuredvsGroq

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

Unstructured

Data & ETL

ETL for unstructured data — PDFs, images, HTML to LLM-ready

Full review →Website ↗

Groq

LLM APIs & Inference

The fastest AI inference platform — LPU-powered, 1000+ tokens/sec

Full review →Website ↗
FeatureUnstructuredGroq
CategoryData & ETLLLM APIs & Inference
PricingFree (open-source) + APIFree tier available, pay-per-token for production
GitHub Stars
✓ More stars
9k
—
PlatformsLinux, macOS, DockerWeb
Key Features
  • ✓ PDF parsing
  • ✓ Image extraction
  • ✓ HTML processing
  • ✓ Chunking
  • ✓ Multi-format
  • ✓ LPU hardware — custom chips for inference, not repurposed GPUs
  • ✓ GPT OSS 120B at 500 tok/s ($0.15/M input)
  • ✓ GPT OSS 20B at 1000 tok/s ($0.075/M input)
  • ✓ Llama 4 Scout 17B at 750 tok/s with 131K context + vision
  • ✓ Qwen3-32B at 400 tok/s with 131K context
  • ✓ Compound AI systems with web search + code execution
  • ✓ Whisper transcription ($0.04-0.11/hour)
  • ✓ OpenAI-compatible API — drop-in replacement
  • ✓ Free developer tier: 250-300K TPM, 1K RPM
Pros
  • + Best document parsing quality
  • + Supports every format
  • + RAG-optimized output
  • + Active development
  • + API + local options
  • + Fastest inference available (500-1000 tok/s)
  • + Free tier with generous limits (250K+ tokens/min)
  • + OpenAI-compatible API — swap one line of code
  • + Latest open-source models (GPT OSS, Llama 4, Qwen3)
  • + Compound AI for agentic workflows (search + code exec)
Cons
  • − Heavy dependencies
  • − Slow for large document sets
  • − API pricing per page
  • − Complex configuration
  • − Cloud-only — cannot self-host LPU hardware
  • − Rate limits on free tier (1K RPM)
  • − Smaller model catalog than running locally via Ollama
Tags
etldocumentsparsingopen-source
inferencefastfreehardware

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