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Cloud · head to head

Anyscale vs Okteto

Anyscale logo

Anyscale

Cloud

Platform for scaling AI and data workloads on Ray, built by Ray's creators

From
Free
Free plan
Yes
Okteto logo

Okteto

Developer Tools

Runs production-like development environments on your own Kubernetes cluster

From
Free
Free plan
Yes

The short version

  • Each has a real cost: Anyscale pricing for high-end H100/B200-class GPUs is not published and requires contacting sales.; Okteto two of the three plans publish no price at all, with no currency and no billing period stated anywhere, so any budget above five seats requires a sales conversation before you can even compare it with an alternative.
  • They diverge on capability: Anyscale covers Distributed model training, Okteto covers Cluster development environments.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Anyscale and Okteto actually diverge.

Attributes where Anyscale and Okteto differ
AttributeAnyscaleOkteto
Pricing modelusage-basedquote
Platformsweb, apiLinux, macOS, Windows, CLI, Kubernetes, Web
CategoryCloudDeveloper Tools

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 Okteto

  • Cluster development environments
  • File synchronisation
  • Development containers
  • Environment catalogue
  • Preview environments
  • Bring your own cloud

What people use each for

The jobs each tool is most often brought in to do.

Anyscale

  • Training large models on distributed GPU clustersnot Okteto
  • Running batch inference and embedding jobsnot Okteto
  • Preparing multimodal datasets at scalenot Okteto
  • Post-training LLMs with reinforcement learning frameworksnot Okteto

Okteto

  • A microservices estate where no developer can run the full dependency graph on a laptopnot Anyscale
  • Giving each pull request a working environment reviewers can click rather than describenot Anyscale
  • Letting coding agents build and test against real databases and queues instead of mocksnot Anyscale
  • Removing a fleet of local Kubernetes installations that drift from production and produce false test resultsnot 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.

Okteto

  • Two of the three plans publish no price at all, with no currency and no billing period stated anywhere, so any budget above five seats requires a sales conversation before you can even compare it with an alternative.
  • The Apache-2.0 CLI is restricted to the development section of the manifest, so build services, user management and observability all require the commercial platform and the open licence provides very little continuity insurance.
  • The last disclosed funding round is a 15 million US dollar Series A from February 2022 with nothing announced since, and the contributor base is dominated by company employees, which is concentrated risk for something embedded in every developer daily loop.
  • You must bring and pay for the Kubernetes cluster, so node cost, capacity planning and version upgrades stay with your platform team on top of the licence fee.
  • The 2026 repositioning towards AI agents means the classic developer inner loop is no longer what the marketing leads with, so a buyer purchasing that use case should confirm roadmap commitment rather than assume it.

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

Okteto

Free
  • StarterFree
    • 5 seats included
    • Self-hosted deployment into your own cluster
    • Community support
  • Scale$undefined/year
    • Contact sales, no price, currency or billing period published
    • More seats and environments than Starter
    • Self-hosted deployment
  • Enterprise$undefined/year
    • Contact sales, no price published
    • Bring your own cloud deployment
    • Available through Google Cloud and AWS marketplaces

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 Okteto if

  • You need cluster development environments.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, CLI, Kubernetes, Web.
  • You also want file synchronisation.

Questions people ask

Is Anyscale or Okteto better?
Neither clearly leads. Anyscale starts at Free and Okteto at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Anyscale or Okteto?
Anyscale starts at Free and Okteto at Free.
Does Anyscale or Okteto run on more platforms?
Anyscale runs on web, api. Okteto runs on Linux, macOS, Windows, CLI, Kubernetes, Web.
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 Okteto is typically brought in for.
What can Anyscale do that Okteto cannot?
Anyscale covers Distributed model training, Multimodal data curation, Batch embedding generation, Multi-cloud orchestration. Okteto covers Cluster development environments, File synchronisation, Development containers, Environment catalogue.

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.

Source
Okteto: Is Okteto open source?

The CLI is Apache-2.0, but the README states it supports only the development section of the manifest. Everything operationally interesting, including builds, user management and observability, is in the commercial platform.

Anyscale: 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.

Source
Okteto: What does it cost?

Only the free Starter tier with five seats is published. Scale and Enterprise are both contact sales, with no price, currency or billing period disclosed on the pricing page.

Anyscale: 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.

Source
Okteto: Do I need my own Kubernetes cluster?

Yes. Okteto installs into a cluster you provide and pay for, so capacity, upgrades and node costs remain your responsibility in addition to the licence.

Anyscale: 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.

Source
Okteto: How is this different from running Kubernetes locally?

Environments run against the same managed databases, queues and service mesh as production, so a class of failures that only appears in a real cluster is caught before merge rather than after deploy.

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