Scalable AI compute
How to use
Anyscale.
Developing and operating distributed Python and AI workloads, including scalable Ray Serve endpoints for language and machine-learning models.
Anyscale is a managed Ray platform for distributed AI development, jobs, and production services. Developing and operating distributed Python and AI workloads, including scalable Ray Serve endpoints for language and machine-learning models. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to Anyscale
Get into the official Anyscale experience with the right account and a setup you understand.
- 01
Start at https://www.anyscale.com/ and confirm the domain before entering account or payment information.
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Availability: Anyscale is used through its web console, CLI, SDK, workspaces, jobs, and services in managed or customer cloud infrastructure.
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An Anyscale organization and project, CLI or SDK access, configured cloud infrastructure and identity permissions, and Ray-compatible Python application code.
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Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
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Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
First session
Your first useful Anyscale session
Learn the interaction loop using a small task with a clear outcome.
- 01
Choose a non-production project and confirm cloud, network, identity, storage, and cost ownership with an administrator.
- 02
Define a small Ray Serve application and a versioned service configuration with dependencies, resources, scaling, and secrets.
- 03
Deploy with bearer-token access enabled and test health, output, concurrency, logs, metrics, and node failure.
- 04
Promote only after load and security validation, retaining a known-good version and explicit rollback procedure.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
Quality control
Check the quality of Anyscale output
Establish that a model's output is reliable enough for the job you are handing it.
- 01
Restate the task, the acceptance criteria, and the failure cost before evaluating any response.
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Test with a fixed evaluation set rather than ad-hoc prompts, so changes are measurable across model versions.
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Check determinism and drift: record model version, parameters, and date, because providers update models underneath you.
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Probe the failure modes deliberately — ambiguous inputs, adversarial phrasing, and out-of-scope requests.
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Validate structure as well as content: confirm schemas, types, and required fields survive real inputs.
- Pin and record the model version with every result.
- Evaluate against a fixed set, not one-off prompts.
- Validate output structure before it reaches downstream code.
Privacy & permissions
Use Anyscale safely
Understand what leaves your system on every call, and what the provider keeps.
- 01
Classify the data in each request before it is sent to a hosted model.
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Check the provider's retention and training terms for your specific plan, not the marketing page.
- 03
Store API keys in a secret manager, scope them per project, and rotate them on a schedule.
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Strip or tokenise personal and regulated data before it reaches the request body.
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Set rate limits, spend caps, and monitoring so a runaway loop is bounded and visible.
- Anyscale can create and scale cloud compute in your account. Apply least-privilege cloud roles, private networking, authenticated endpoints, secret management, resource limits, cost monitoring, tenant isolation, and tested rollback for every service.
- Follow your organisation's approved-use policy.
- Never treat fluent output as authorization to act.
Core workflows
Step-by-step ways to use Anyscale for the work it does best.
Each workflow is a separate guide with its own steps and review checkpoints.
Package a Ray Serve application and release it through a reproducible service configuration.
↗ 02 WorkflowScale and update an Anyscale service safelyTune Ray Serve replicas and cloud workers from measurements, then roll out new versions with a verified rollback path.
↗Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.