Cloud · head to head
Anyscale vs Beam Cloud

Anyscale
Cloud
Platform for scaling AI and data workloads on Ray, built by Ray's creators
- From
- Free
- Rated
- -

Beam Cloud
Cloud
Serverless GPU computing with sub-second cold starts and multi-cloud support
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Anyscale pricing for high-end H100/B200-class GPUs is not published and requires contacting sales.; Beam Cloud free tier limited to $30 monthly credits with 5 GPU containers
- They diverge on capability: Anyscale covers Distributed model training, Beam Cloud covers Sub-second cold starts.
Where they differ
Only the attributes on which Anyscale and Beam Cloud actually diverge.
| Attribute | Anyscale | Beam Cloud |
|---|---|---|
| Pricing model | usage-based | Freemium with pay-per-millisecond usage charges |
| Platforms | web, api | Cloud, Python |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Cloud).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Anyscale
- Distributed model training
- Multimodal data curation
- Batch embedding generation
- Multi-cloud orchestration
- Governance and security
- Observability
- Bring-your-own-cloud deployment
- Elastic GPU allocation
Only in Beam Cloud
- Sub-second cold starts
- Inference endpoints
- Task queues
- Sandboxes
- Multi-cloud support
- Python SDK
- Global distribution
- Massive parallelization
What people use each for
The jobs each tool is most often brought in to do.
Anyscale
- Training large models on distributed GPU clustersnot Beam Cloud
- Running batch inference and embedding jobsnot Beam Cloud
- Preparing multimodal datasets at scalenot Beam Cloud
- Post-training LLMs with reinforcement learning frameworksnot Beam Cloud
Beam Cloud
- Deploying ML models with minimal latency and setup timenot Anyscale
- Large-scale batch processing across thousands of concurrent tasksnot Anyscale
- Cost-effective inference serving with bursty workloadsnot Anyscale
- Multi-cloud AI deployments with global low-latency accessnot Anyscale
- Serverless AI development for rapid experimentationnot Anyscale
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Anyscale
- Pricing for high-end H100/B200-class GPUs is not published and requires contacting sales.
- Built around Ray, so teams not already using Ray face a steeper adoption curve than single-purpose inference APIs.
- No published fixed-fee subscription tier; all listed pricing is usage-based on-demand compute.
Beam Cloud
- Free tier limited to $30 monthly credits with 5 GPU containers
- Massive parallelization complexity may require DevOps expertise
- Per-millisecond pricing model requires careful cost monitoring
- Smaller team relative to established cloud providers
Pricing, plan by plan
Anyscale
Free- Pay-as-you-go$undefined/mo
- CPU only from $0.0135/hr
- NVIDIA T4 $0.5682/hr
- NVIDIA L4 $0.9542/hr
- Committed contract$undefined/mo
- Volume discounts
- Use of existing GPU reservations
Beam Cloud
Free- DeveloperFree
- $30 monthly free credits
- 5 GPU containers, 30 CPU containers
- Community support
- Team$89/month
- $30 monthly free credits included
- 50 GPU containers, 1,000 CPU containers
- 3 seats included, $25 per additional
- Growth$undefined/custom
- 1,000+ GPU containers
- Unlimited CPU containers
- Unlimited seats
- Serverless GPUs$undefined/per-millisecond
- RTX 4090: $0.00019/sec
Which should you pick?
Choose Anyscale if
- You need distributed model training.
- You want to start without paying.
- You work on web, api.
- You also want multimodal data curation.
Choose Beam Cloud if
- You need sub-second cold starts.
- You want to start without paying.
- You work on Cloud, Python.
- You also want inference endpoints.
Questions people ask
- Is Anyscale or Beam Cloud better?
- Neither clearly leads. Anyscale starts at Free and Beam Cloud at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Anyscale or Beam Cloud?
- Anyscale starts at Free and Beam Cloud at Free.
- Does Anyscale or Beam Cloud run on more platforms?
- Anyscale runs on web, api. Beam Cloud runs on Cloud, Python.
- Can I use Anyscale for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Anyscale best used for?
- Anyscale is most often used for training large models on distributed gpu clusters, running batch inference and embedding jobs, preparing multimodal datasets at scale, post-training llms with reinforcement learning frameworks. Of those, training large models on distributed gpu clusters and running batch inference and embedding jobs are not what Beam Cloud is typically brought in for.
- What can Anyscale do that Beam Cloud cannot?
- Anyscale covers Distributed model training, Multimodal data curation, Batch embedding generation, Multi-cloud orchestration. Beam Cloud covers Sub-second cold starts, Inference endpoints, Task queues, Sandboxes.
Answered from the vendors’ own pages
Anyscale: How much does Anyscale cost?
Anyscale bills on a pay-as-you-go basis: CPU compute starts at $0.0135/hr, NVIDIA T4 at $0.5682/hr, and NVIDIA A100 at $4.9591/hr, with committed contracts offering volume discounts for larger workloads.
SourceBeam Cloud: What is included in the Developer plan?
The Developer plan includes $30 monthly free credits, 5 GPU containers, 30 CPU containers, and community support. No upfront commitment is required.
SourceAnyscale: Is there a free trial or credit?
New users receive $100 in Anyscale credits to explore the platform, which can be applied toward starter templates and on-demand compute usage.
SourceBeam Cloud: How fast are the cold starts?
Beam Cloud achieves sub-second cold starts through memory snapshots that restore GPU containers 35x faster than traditional cold boots.
SourceAnyscale: How is usage billed?
Hosted usage is billed hourly per compute instance type and invoiced monthly by credit card; bring-your-own-cloud usage is invoiced through Anyscale or the customer's cloud marketplace account.
SourceBeam Cloud: Can I deploy across multiple cloud providers?
Yes, Beam Cloud supports multi-cloud deployment across AWS, GCP, Azure, Hetzner, and other providers with 30+ global regions available.
SourceAnyscale: What support is included?
Hosted plans include business-hours support with up to 5 case submissions, while bring-your-own-cloud deployments get 24x7 enterprise SLAs and unlimited case submissions.
SourceRelated pages
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