HeliconevsGroq

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

Helicone

MLOps & Monitoring

Open-source LLM observability platform

Full review →Website ↗

Groq

LLM APIs & Inference

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

Full review →Website ↗
FeatureHeliconeGroq
CategoryMLOps & MonitoringLLM APIs & Inference
PricingFree + Pro plansFree tier available, pay-per-token for production
GitHub Stars
✓ More stars
3k
—
PlatformsWebWeb
Key Features
  • ✓ Request logging
  • ✓ Cost tracking
  • ✓ Latency monitoring
  • ✓ Prompt management
  • ✓ User tracking
  • ✓ 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
  • + One-line integration
  • + Cost tracking
  • + Latency monitoring
  • + Prompt management
  • + Open-source
  • + 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
  • − Limited free tier
  • − Cloud-focused
  • − Smaller feature set vs W&B
  • − Less mature
  • − Cloud-only — cannot self-host LPU hardware
  • − Rate limits on free tier (1K RPM)
  • − Smaller model catalog than running locally via Ollama
Tags
observabilitymonitoringcostsopen-source
inferencefastfreehardware

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