Cerebrium
Rent GPU power by the second to run AI models in production, without reserving hardware or managing servers yourself.
🔗 Visit Cerebrium
Description
Running AI models — especially real-time ones like voice agents or video generation — needs serious GPU power, but buying or reserving GPUs is expensive and mostly sits idle between requests. Cerebrium lets you deploy your AI application and only pay for GPU time while it's actually running, scaling up automatically when traffic spikes and down to nothing when it's quiet, across a pool of GPUs spread over multiple cloud providers and regions.
Cerebrium offers per-second billing across a GPU range from budget options (L4, T4) up to high-end (H100, H200, B200), with claimed 2-4 second cold starts via GPU snapshotting, REST/WebSocket/streaming endpoints, and OpenTelemetry-based observability. The free Hobby tier covers up to 3 apps and 5 GPU concurrency; the Standard tier is $100/month plus compute (30 GPU concurrency); Enterprise is custom. It carries SOC 2, HIPAA, GDPR, and ISO compliance with a 99.999% uptime guarantee via multi-region failover, and has been operating since 2021.
💬 Our review
The short version: Cerebrium's pitch — rent GPUs like you'd rent serverless compute, pay only for what you use — is a genuinely useful model for anyone deploying real-time AI features without wanting to manage GPU infrastructure directly.
Against Modal (developer-experience-focused, Python-native) and RunPod (cheaper raw GPU rental but less managed tooling), Cerebrium sits closer to Modal in terms of polish, with a specific emphasis on low cold-start latency for real-time use cases like voice agents — a detail that matters a lot if you're building something interactive rather than batch processing. The per-second billing is fair and transparent, but the free tier's 5 GPU concurrency cap will be limiting fast for anything beyond testing, and per-second GPU costs can still add up quickly for sustained, high-traffic workloads. For teams building latency-sensitive AI products who want enterprise compliance (SOC 2, HIPAA) without running their own GPU fleet, it's a solid choice; teams just needing the cheapest possible raw GPU hours for batch jobs may find RunPod more economical.
📊 Global score
🤖 AI-enriched data
Hobby : gratuit + coût compute (3 apps, 5 GPU concurrents). Standard : $100/mois + compute (30 GPU concurrents). Enterprise : sur devis. Ex : H100 à $0.000944/s.
Pros
Cold starts de 2-4 secondes grâce au snapshotting GPU
Facturation à la seconde, aucun coût au repos
Large choix de GPU (T4 à B200) sur plusieurs régions/clouds
Conformité entreprise (SOC 2, HIPAA, GDPR, ISO)
Cons
Palier Standard limité à 30 GPU concurrents (Enterprise pour l'illimité)
Rétention des logs limitée à 7 jours en gratuit
Coûts GPU à la seconde peuvent grimper vite en usage soutenu