Cerebrium vs Daytona: Pricing, Specs, Strengths & Weaknesses

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

Overview

Attribute

Cerebrium

Daytona

Provider type

Specialist

Founded

2022

2023

Headquarters

Cape Town, South Africa

United States

Billing model

Per-second usage

Preemptible (spot)

Min commitment

None

Support tier

Standard

Regions

2 regions

1 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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Daytona

Daytona is a GPU cloud offering preemptible (spot) instances across a wide range of hardware including NVIDIA B300, B200, H200, H100, RTX PRO 6000, RTX 5090, RTX 4090, and AMD MI355X. All pricing is preemptible — no on-demand rates are published. Competitive spot pricing makes Daytona a cost-effective option for fault-tolerant AI training, batch inference, and research workloads that can tolerate interruption. Access to Blackwell-generation hardware (B300, B200) at preemptible rates is a notable differentiator for teams that need next-generation GPU performance at reduced cost.

Strengths

B300 and B200 Blackwell hardware at preemptible rates
AMD MI355X availability
Wide GPU catalog: H200, H100, RTX 5090, RTX 4090
Competitive spot pricing across all SKUs
RTX PRO 6000 Blackwell available

Weaknesses

Preemptible only — no on-demand pricing published
Workloads must handle interruption
Smaller provider — limited scale vs hyperscalers

Best For

Fault-tolerant AI training on latest hardwareBatch inference at reduced costResearch workloads that can tolerate interruptionTeams needing Blackwell GPUs at spot pricing

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