Beam vs Cerebrium
A side-by-side comparison of Beam and Cerebrium, two Infra tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
Cerebrium
InfraServerless GPU infrastructure for real-time AI — voice, video, and LLM workloads.
View CerebriumAt a glance
| Attribute | Beam | Cerebrium |
|---|---|---|
| Category | Infra | Infra |
| Pricing (differs) | FREEMIUM | PAID |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms (differs) | CLI, API, Linux | API, CLI |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Beam | Cerebrium |
| Capabilities (differs) |
|
|
The honest brief
Beam
Deploy GPU endpoints, sandboxes, and queues from a few lines of Python — open-core runtime (beta9) you can self-host.
- Define GPU workloads in pure Python
- Open-source runtime (beta9)
- Fast cold starts and autoscaling
- Free dev tier with monthly credit
- Smaller ecosystem than hyperscalers
- Python-centric; less polyglot
- Newer platform, maturing tooling
Cerebrium
Tuned for real-time voice and video agents, where its fast cold starts and multi-region failover beat general-purpose GPU clouds.
- 2–4s cold starts, scale-to-zero
- 12+ GPU types up to B200
- Multi-region deploys + failover
- SOC 2, HIPAA, GDPR compliant
- $100/mo base on the Standard tier
- Hobby tier capped at 3 apps, 5 GPUs
- Younger platform, smaller community
When to pick which
Both cover GPU compute, Model inference / serving, and App / agent deployment.
Pick Beam if you need Sandboxed code execution.
- Sandboxed code execution (secondary capability)
Beam leans on GPU compute as a headline capability; Cerebrium treats it as secondary.
- GPU compute (primary capability)
They also differ on:
- Pricing
- FREEMIUM · PAID
- Platforms
- CLI, API, Linux · API, CLI