Baseten vs Cerebrium
Detailed side-by-side comparison to help you choose the right tool
Baseten
🔴DeveloperModel Deployment
Production ML model serving platform focused on high-performance LLM and generative model inference — dedicated deployments, autoscaling, Model Library one-clicks, and enterprise-grade observability without the Kubernetes bill.
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Starting Price
$30 creditCerebrium
🔴DeveloperModel Deployment
Serverless GPU platform for AI workloads with sub-second cold starts across a wide GPU catalog (A10, L4, A100, H100), Python-first deploys, and a strong focus on real-time voice AI and inference apps.
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Starting Price
$30 creditFeature Comparison
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Baseten - Pros & Cons
Pros
- ✓Transparent per-token and per-minute examples help teams model costs
- ✓Strong fit for teams moving from notebooks to production APIs
- ✓Enterprise options cover data residency and security-sensitive deployments
Cons
- ✗Pro and Enterprise require quotes, so total cost depends on volume and commitments
- ✗GPU inference still requires performance testing per model and workload
- ✗Overkill for teams that only need hosted frontier model APIs
Cerebrium - Pros & Cons
Pros
- ✓Cerebrium combines its core workflow in one product rather than requiring several disconnected services.
- ✓The staged feature set is specific enough to evaluate in a small proof of concept.
Cons
- ✗Current pricing and plan limits need confirmation with the vendor before purchase.
- ✗Teams should test security, reliability, export, and support requirements with their own workload.
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