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Cerebrium

by Cerebrium

Serverless GPU compute platform running custom Python AI workloads with sub-second cold starts — enabling APAC AI teams to deploy custom inference functions, fine-tuning jobs, and batch ML workloads on H100/A100 infrastructure without provisioning or managing GPU servers.

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AIMenta verdict
Decent fit
4/5

"Serverless GPU cloud for APAC AI teams — Cerebrium runs custom Python functions and ML models on GPU infrastructure with sub-second cold starts, enabling APAC teams to run inference and training workloads without managing GPU servers."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Sub-second cold starts: APAC fast GPU VM startup for real-time inference workloads
  • H100/A100/T4: APAC GPU class selection per workload requirements
  • Serverless billing: APAC per-GPU-second charging; no idle time costs
  • Custom Python: APAC any Python library or ML framework in deployment functions
  • Persistent storage: APAC model weights cached across invocations for faster startup
  • CLI deploy: APAC one-command deployment from local development to production
When to reach for it

Best for

  • APAC AI teams with variable GPU workloads — real-time inference APIs, batch processing jobs, or fine-tuning runs — who need flexible access to high-end GPU hardware without committed instance costs, particularly APAC startups and research teams with unpredictable compute demand patterns.
Don't get burned

Limitations to know

  • ! APAC data residency primarily EU/US — review for APAC data sovereignty requirements
  • ! Less mature ecosystem than RunPod for APAC community templates and pre-configured images
  • ! APAC long-running jobs (24h+ training) better served by reserved instance providers
Context

About Cerebrium

Cerebrium is a serverless GPU compute platform providing APAC AI teams with on-demand GPU infrastructure for custom Python inference functions, model fine-tuning jobs, and batch ML workloads — combining RunPod-style GPU access with serverless scaling and sub-second cold starts optimized for AI application workloads. APAC teams needing flexible GPU compute for both inference and training without long-term GPU commitments use Cerebrium as their elastic AI compute layer.

Cerebrium's deployment model packages APAC Python functions as serverless GPU workers — teams define inference logic in a Python function, specify GPU requirements (H100, A100, T4), and deploy via the Cerebrium CLI. Requests trigger function execution on the requested GPU class; Cerebrium handles instance provisioning, scaling, and teardown. APAC teams deploying custom model inference, text-to-image generation, or specialized ML pipelines use Cerebrium to access high-end GPUs without purchasing or managing hardware.

Cerebrium's sub-second cold starts differentiate it from standard GPU cloud providers — while most cloud GPU instances take minutes to provision, Cerebrium keeps warm pools of pre-configured GPU VMs that start user functions in under one second. APAC real-time AI applications (live image processing, interactive generation, synchronous API calls) use Cerebrium's fast startup to maintain responsive user experiences even at zero-to-one scaling transitions.

Cerebrium's APAC pricing model charges per GPU-second of actual compute time — teams pay only for the seconds their functions execute on GPU hardware, not for idle time between requests. APAC AI startups with variable or unpredictable inference traffic use Cerebrium's granular billing to avoid committing to reserved GPU instances that would sit idle during off-peak periods.

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