Buying snapshot
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| Decision point | What Anyscale states |
|---|---|
Deployment | Hosted: fully managed Anyscale infrastructure with no setup, in limited regions, on VMs. Bring Your Own Cloud (BYOC): inside your own cloud or on-prem, any cloud or region, on VMs or Kubernetes. |
Data residency and compute | Hosted runs on Anyscale-managed infrastructure and Anyscale-hosted compute. BYOC keeps data in your VPC and can use your existing GPU reservations. |
Invoicing | Hosted: monthly credit card invoices. BYOC: invoice via Anyscale or your cloud marketplace (AWS, Azure, GCP). |
Support | Hosted: business hours only, 5 case submissions. BYOC: enterprise SLAs with 24x7 coverage and unlimited case submissions. |
Access control | SSO, SAML, SCIM and audit logs for multi-team governance. |
Open-source base | Ray is Apache-2.0 licensed, installs with pip install ray, and runs on any machine, cluster, cloud provider and Kubernetes. Anyscale describes its Anyscale Runtime as fully managed and Ray-compatible, "without vendor lock-in". |
Our take
Anyscale makes sense when Ray is already your compute layer, or you are ready to adopt it, and the hard part is running Ray for several teams or across clouds. If one team runs a few Ray jobs on a single cloud, open-source Ray on its own may be enough.
Evaluate Anyscale
Comparisons
Where it stands out
One control plane across clouds. Anyscale deploys and manages workloads in any region or cloud, on Kubernetes or VMs, from a single control plane. It names AWS, GCP, Azure, Nebius and CoreWeave and says the same code runs across them without cloud-specific rewrites, which helps teams that look for GPU capacity in several places.
Managed production clusters. Jobs & Services are managed Ray clusters for data, training and serving, with head node resilience, autoscaling and A/B rollouts. Priority-aware scheduling across clouds runs critical workloads first.
Development close to production. Workspaces give cluster-backed VS Code and Jupyter on Ray clusters, with under 1 minute startup according to Anyscale. Workload-specific dashboards with persistent logs cover Ray Data, Train and Serve, and cluster health, CPU/GPU utilization and memory can be viewed in the Anyscale UI or in your own monitoring tool.
Cost controls for shared GPUs. Budgets add usage attribution and spend quotas to control GPU cost as teams grow. GPUs can be pooled across clouds, regions and Kubernetes clusters, with capacity reallocated as workload demand shifts.
Product screenshot
Trade-offs to weigh
Plan the move to BYOC early. Anyscale presents Hosted as the fastest way to start a first project and BYOC as ready for Ray production workloads. A team that starts on Hosted should expect to set up Anyscale inside its own cloud or on-prem before it gets production support terms.
H, B and GB family GPUs need a sales conversation. Instances with NVIDIA H, B or GB family GPUs have no self-serve hourly rate, so teams planning training on those GPUs need a quote before they can size the bill.
Pricing and plan conditions
Anyscale is pay-as-you-go: you pay only for the compute you use on demand, with no monthly fixed fees. New accounts get started with $100 in Anyscale credits. Committed contracts, arranged through sales, unlock volume discounts and let you use existing GPU reservations. Anyscale also offers starter projects: an end-to-end multimodal AI workload with the label "Launch project with $3" and an LLM fine-tuning, batch inference and serving project labelled "Launch project with $5".
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| Instance containing | Published rate |
|---|---|
CPU only | AC 0.0135 /hr |
NVIDIA T4 | AC 0.5682 /hr |
NVIDIA L4 | AC 0.9542 /hr |
NVIDIA A10G | AC 1.3635 /hr |
NVIDIA A100 | AC 4.9591 /hr |
NVIDIA H/B/GB GPU families | Contact Us |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
1.2k GitHub commits 90d44.2k GitHub stars0 vulnerabilities across 1 package
See all signals from 5 sources
PyPI · anyscale@0.26.116
Alternatives to Anyscale
The reviewed substitutes for Anyscale among the model hosting platforms, and what would make each one the better answer.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Replicate
- Both host and serve open models by API; Replicate is pay-per-second inference, Anyscale is a managed Ray cluster.Applies to: Serving open models by API without operating the GPU fleet.
- Together AI
- Both offer serverless inference plus dedicated GPU clusters for open models.Applies to: Serverless inference and fine-tuning of open models, with dedicated GPU capacity when needed.
- Modal
- Both run GPU AI workloads as managed infrastructure; Anyscale is Ray-based, Modal is serverless containers.Applies to: Running GPU training and inference jobs on managed infrastructure.
- Snowflake Cortex
- Cortex's pitch is not model quality -- it hosts other vendors' models -- but the perimeter: unified governance, permissions, telemetry and spend controls, with the text you pass in never leaving the Snowflake account boundary. A hosting platform is the other answer: send the data out to a managed endpoint and get a wider model catalogue and independent scaling. For the enrichment and analytics workload that sits on warehouse data, a team picks one. Anyscale is the Ray-based end of it: managed infrastructure for training, fine-tuning and serving at scale, which is what a team chooses when the AI workload has outgrown what a warehouse function is meant to do.
- Amazon SageMaker
- A managed ML platform and a managed Ray platform both run distributed training and inference, and differ on whether the workflow is platform-shaped or Ray-shaped. SageMaker can host Ray, so the decision is architectural rather than a straight substitution.Applies to: Running distributed training and inference, with or without a full ML platform around it.
- Runcrate
- Both can run Ray workloads across several GPU machines. Anyscale is a managed Ray platform for training, fine-tuning and serving; Runcrate rents self-serve clusters that can come with Ray installed across the machines, which it describes as suited to independent jobs and Ray tasks rather than training one model across machines (Runcrate CLI documentation).Applies to: Running Ray jobs on rented multi-GPU clusters.