Cerebrium

Cerebrium GPU pricing overview

Cerebrium is a specialist GPU cloud provider headquartered in Cape Town, South Africa. 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. Billing is Per-second usage with a minimum commitment of None. Available regions include US, EU. GPU instances can be provisioned on-demand, making Cerebrium suitable for both short-duration experiments and sustained production workloads.

Cerebrium vs other GPU providers

Cerebrium competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: Cold starts under 5 seconds — fast serverless GPU spin-up; Custom containers — any Python framework supported; Per-second billing with free tier. Use the side-by-side comparison tool above to see Cerebrium pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 5 Cerebrium listings alongside 91+ providers in a single sortable view.

Best use cases for Cerebrium

Cerebrium is best suited for: Real-time LLM inference APIs, Fine-tuned model serving, Teams wanting serverless GPU with custom containers, Bursty inference workloads with zero idle cost. Support tiers range from Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 5 active GPU listings on Cerebrium, covering H100 80GB, A100 80GB, A10G, T4 and more. H100 SXM5 instances with NVLink interconnect are commonly used for large-scale training workloads. A100 40GB and RTX 4090 instances are frequently used for fine-tuning and inference of smaller models.

Cerebrium billing model and cost structure

Cerebrium uses Per-second usage pricing. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing may be available for predictable long-running workloads — check the provider's pricing page for current committed-use rates. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on Cerebrium

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — high HBM3 bandwidth and FP16 throughput make them well-suited for large transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB is a common choice on many providers. RTX 4090 instances are widely used for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How Cerebrium pricing data is collected

Prices shown are sourced from Cerebrium's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are kept in our history archive and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and many other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.