Cerebrium vs IBM Cloud: Pricing, Specs, Strengths & Weaknesses

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Cerebrium and IBM Cloud. Rates sourced from public APIs — verify current availability and billing terms before purchasing.

Overview

Attribute

Cerebrium

IBM Cloud

Provider type

Specialist

Hyperscaler

Founded

2022

2011

Headquarters

Cape Town, South Africa

Armonk, NY

Billing model

Per-second usage

On-demand, Reserved

Min commitment

None

None (on-demand)

Support tier

Standard

Standard → Enterprise

Regions

2 regions

3 regions

Strengths & Best For

Cerebrium

Cerebrium is a serverless ML infrastructure platform that deploys H100, A100, and T4 GPU workloads in seconds using custom containers, enabling real-time LLM inference and fine-tuned model serving without managing any infrastructure. Per-second billing and fast cold starts (typically under 5 seconds) make it highly cost-efficient for bursty AI inference APIs and model deployment pipelines. Supports any Python framework — PyTorch, TensorFlow, JAX, vLLM, TGI. Free tier available. A top choice for ML teams that want to ship production inference endpoints quickly with minimal DevOps overhead.

Strengths

Cold starts under 5 seconds — fast serverless GPU spin-up
Custom containers — any Python framework supported
Per-second billing with free tier
H100 and A100 availability
Supports vLLM, TGI, and custom inference servers

Weaknesses

Smaller provider — limited cluster scale
Fewer regions than major cloud providers
Not suitable for multi-node distributed training

Best For

Real-time LLM inference APIsFine-tuned model servingTeams wanting serverless GPU with custom containersBursty inference workloads with zero idle cost

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IBM Cloud

IBM Cloud provides H100 and A100 GPU instances with enterprise-grade compliance certifications including HIPAA, FedRAMP, and SOC 2, making it a strong GPU cloud option for regulated industries. Deep integration into the IBM watsonx AI ecosystem (watsonx.ai, watsonx.data, watsonx.governance) for enterprise AI workloads. IBM Granite 4.0 H Small is available on RunPod's Public Endpoints. IBM Cloud Bare Metal GPU instances offer dedicated hardware for HPC and AI training. A strong choice for healthcare, government, and financial services organizations that need GPU compute within a fully compliant cloud environment.

Strengths