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Cloud · head to head

Beam Cloud vs Dagger

Beam Cloud logo

Beam Cloud

Cloud

Serverless GPU computing with sub-second cold starts and multi-cloud support

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: Beam Cloud free tier limited to $30 monthly credits with 5 GPU containers; 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: Beam Cloud covers Sub-second cold starts, Dagger covers Pipelines as code.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Beam Cloud and Dagger actually diverge.

Attributes where Beam Cloud and Dagger differ
AttributeBeam CloudDagger
Pricing modelFreemium with pay-per-millisecond usage chargesPer month per team for Dagger Cloud
PlatformsCloud, PythonLinux, 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 Beam Cloud

  • Sub-second cold starts
  • Inference endpoints
  • Task queues
  • Sandboxes
  • Multi-cloud support
  • Python SDK
  • Global distribution
  • Massive parallelization

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.

Beam Cloud

  • Deploying ML models with minimal latency and setup timenot Dagger
  • Large-scale batch processing across thousands of concurrent tasksnot Dagger
  • Cost-effective inference serving with bursty workloadsnot Dagger
  • Multi-cloud AI deployments with global low-latency accessnot Dagger
  • Serverless AI development for rapid experimentationnot Dagger

Dagger

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

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

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

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

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

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 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.
Beam Cloud 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 Beam Cloud or Dagger better?
Neither clearly leads. Beam Cloud 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, Beam Cloud or Dagger?
Beam Cloud starts at Free and Dagger at Free.
Does Beam Cloud or Dagger run on more platforms?
Beam Cloud runs on Cloud, Python. Dagger runs on Linux, macOS, Windows, Self-hosted.
Can I use Beam Cloud for free?
Both have a free tier, so you can try either at no cost before committing.
What is Beam Cloud best used for?
Beam Cloud is most often used for deploying ml models with minimal latency and setup time, large-scale batch processing across thousands of concurrent tasks, cost-effective inference serving with bursty workloads, multi-cloud ai deployments with global low-latency access. Of those, deploying ml models with minimal latency and setup time and large-scale batch processing across thousands of concurrent tasks are not what Dagger is typically brought in for.
What can Beam Cloud do that Dagger cannot?
Beam Cloud covers Sub-second cold starts, Inference endpoints, Task queues, Sandboxes. Dagger covers Pipelines as code, Local and CI parity, Content-addressed caching, Dagger Functions and modules.

Answered from the vendors’ own pages

Beam 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.

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.

Beam 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.

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.

Beam 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.

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.

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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