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Cloud · head to head

Anyscale vs Dagger

Anyscale logo

Anyscale

Cloud

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

From
Free
Free plan
Yes
Full reviewWebsite
Dagger logo

Dagger

Developer Tools

Programmable CI/CD engine that runs your pipeline as containers on any runner

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: Anyscale pricing for high-end H100/B200-class GPUs is not published and requires contacting sales.; Dagger dagger is not a CI provider, so you keep paying for GitHub Actions or GitLab runners underneath it; the Dagger Cloud subscription is an addition to your CI bill, not a replacement for it.
  • They diverge on capability: Anyscale covers Distributed model training, Dagger covers Pipelines as code.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Anyscale and Dagger actually diverge.

Attributes where Anyscale and Dagger differ
AttributeAnyscaleDagger
Pricing modelusage-basedPer month per team for Dagger Cloud
Platformsweb, apiLinux, macOS, Windows, Self-hosted
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 Dagger

  • Pipelines as code
  • Local and CI parity
  • Content-addressed caching
  • Dagger Functions and modules
  • CI-provider agnostic
  • Dagger Cloud traces
  • GitHub Checks integration
  • Container-native execution

What people use each for

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

Anyscale

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

Dagger

  • A platform team maintaining near-identical build YAML across forty repositories that wants one versioned module every repository calls insteadnot Anyscale
  • An engineering organisation migrating off a CI provider that does not want to rewrite every pipeline as part of the migrationnot Anyscale
  • A team whose engineers waste hours pushing commits to debug CI-only failures and needs to reproduce the exact run locallynot Anyscale
  • A monorepo where most commits touch a small subset of services and cache-accurate step skipping cuts build minutes materiallynot 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.

Dagger

  • Dagger is not a CI provider, so you keep paying for GitHub Actions or GitLab runners underneath it; the Dagger Cloud subscription is an addition to your CI bill, not a replacement for it.
  • Writing pipelines in Go or TypeScript raises the barrier to entry: a release engineer who could edit a YAML step now needs to read code, and the people who can fix a broken build shrink to those comfortable with the SDK.
  • The engine runs as a container on the runner and needs a persistent cache volume to deliver its main benefit, so ephemeral CI runners give you correctness without the speed, and provisioning durable cache storage is extra platform work.
  • The Team plan caps at ten users and ten million events per month; an organisation of any size crosses that quickly and moves to Enterprise pricing that is not published, so cost at scale is unknowable up front.
  • The module ecosystem is young and thinly maintained relative to the marketplace of a mature CI provider, so integrations that exist as a one-line Action often have to be written yourself as a Dagger function.

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

Dagger

Free
  • IndividualFree
    • One user
    • One million events per month
    • Workflow logs and function call traces
  • Team$50/month
    • Up to ten users
    • Ten million events per month
    • Module insights and module catalogue
  • Enterprise$undefined/year
    • Custom user and event limits
    • SSO
    • Managed single-tenant deployment

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.
Anyscale review

Choose Dagger if

  • You need pipelines as code.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Self-hosted.
  • You also want local and ci parity.
Dagger review

Questions people ask

Is Anyscale or Dagger better?
Neither clearly leads. Anyscale starts at Free and Dagger at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Anyscale or Dagger?
Anyscale starts at Free and Dagger at Free.
Does Anyscale or Dagger run on more platforms?
Anyscale runs on web, api. Dagger runs on Linux, macOS, Windows, Self-hosted.
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 Dagger is typically brought in for.
What can Anyscale do that Dagger cannot?
Anyscale covers Distributed model training, Multimodal data curation, Batch embedding generation, Multi-cloud orchestration. Dagger covers Pipelines as code, Local and CI parity, Content-addressed caching, Dagger Functions and modules.

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
Dagger: Does Dagger replace GitHub Actions?

No. Dagger runs inside GitHub Actions or any other CI. It replaces the YAML that describes your pipeline steps, not the trigger and runner layer.

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
Dagger: Is the engine open source?

Yes, the Dagger engine and SDKs are open source and free. Dagger Cloud, the observability product, is what costs money.

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
Dagger: What is an event in the pricing?

Dagger Cloud meters pipeline telemetry events. One million per month is free; the Team plan at 50 USD per month allows ten million.

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
Dagger: Can I run the same pipeline on my laptop?

Yes, that is the main reason teams adopt it. The dagger CLI executes the identical graph locally, so CI-only failures become reproducible.

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