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Cerebrium vs Groq

CerebriumGroq

Bottom line: Cerebrium for mL engineers; Groq for developers building latency-sensitive apps.

CerebriumCerebrium

Python-native serverless GPU platform for real-time AI inference and custom models

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GroqGroq

Very fast LLM inference on custom LPU hardware.

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Votes00
PricingFreemiumFreemium
CategoryAi InfrastructureAi Infrastructure
Tags
serverless-gpuinferencemlopsreal-time-aipython
inferencellm-apilow-latencyopen-sourcehardware
Best for
  • ML engineers
  • Startups shipping GPU APIs
  • Real-time AI products
  • Developers building latency-sensitive apps
  • Teams running AI agents
  • Voice and real-time product builders
Pros
  • Python-native, no container pipelines needed
  • Pay-per-second billing with no idle cost
  • Fast low single-digit second cold starts
  • 12+ GPU types including A100 and H100
  • Separate GPU/CPU/memory line items
  • Exceptional inference speed on supported models
  • Competitive per-token pricing
  • OpenAI-compatible API is easy to adopt
  • Free tier with no credit card
  • Good fit for agents and real-time apps
Cons
  • Smaller than major inference clouds
  • No self-hosting option
  • Cold starts still matter for ultra-low latency
  • Thinner ecosystem and enterprise tooling
  • Python-focused workflow only
  • Limited to a curated catalog of open models
  • No hosting of arbitrary custom weights
  • Model lineup changes over time
  • Corporate turbulence in 2026 (Nvidia deal, down round)
  • Free-tier rate limits are modest

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