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Cloud · head to head

Cerebrium vs Dokku

Cerebrium logo

Cerebrium

Cloud

Serverless GPU infrastructure for real-time AI inference and applications

From
Free
Rated
-
Full reviewWebsite
Dokku logo

Dokku

Cloud

Single-server platform that accepts a git push and runs Heroku buildpacks on Docker

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Cerebrium free Hobby tier limited to 3 apps and 5 GPU concurrency; Dokku one maintainer accounts for nearly all human commit activity and the project records roughly 10,500 US dollars of annual income on Open Collective, which is not enough to fund a maintainer, so continuity rests on one person continuing to volunteer.
  • They diverge on capability: Cerebrium covers Ultra-fast cold starts, Dokku covers Git push deploy.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Cerebrium and Dokku actually diverge.

Attributes where Cerebrium and Dokku differ
AttributeCerebriumDokku
Pricing modelFreemium with monthly plans and per-second compute chargesOpen source, no licence fee
PlatformsCloud, DockerLinux, Docker, CLI, Self-hosted

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

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 Cerebrium

  • Ultra-fast cold starts
  • Elastic scaling
  • Bring your own code
  • Multi-region failover
  • WebSocket and streaming
  • Asynchronous jobs
  • CI/CD with gradual rollouts
  • OpenTelemetry integration

Only in Dokku

  • Git push deploy
  • Heroku buildpacks
  • Datastore plugins
  • Automatic certificates
  • Zero downtime deploys
  • Pluggable schedulers

What people use each for

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

Cerebrium

  • Deploying voice agents and conversational AI applicationsnot Dokku
  • Video and image model serving with low latencynot Dokku
  • LLM inference and completion endpointsnot Dokku
  • Real-time embeddings and vector database operationsnot Dokku
  • Distributed model training with hyperparameter sweepsnot Dokku

Dokku

  • Running a dozen side projects and client applications on one virtual private server with Heroku style deploymentnot Cerebrium
  • Moving off a managed platform when the monthly bill has grown faster than the traffic hasnot Cerebrium
  • A consultancy that wants buildpack deployments without teaching every client team Kubernetesnot Cerebrium
  • Keeping a legacy Procfile application alive on hardware you control after a managed platform deprecates its stacknot Cerebrium

Where each one falls short

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

Cerebrium

  • Free Hobby tier limited to 3 apps and 5 GPU concurrency
  • Standard plan at $100/month required for production deployments
  • Per-second compute pricing requires continuous cost monitoring
  • Storage costs add up for large model files

Dokku

  • One maintainer accounts for nearly all human commit activity and the project records roughly 10,500 US dollars of annual income on Open Collective, which is not enough to fund a maintainer, so continuity rests on one person continuing to volunteer.
  • Dokku Pro asks 849 US dollars for a lifetime licence from a project with no disclosed legal entity behind the promise and no published refund terms, while the product is still described as in development at early bird pricing.
  • Dokku is single-server by design, and the k3s scheduler that provides multi-node support is missing log retrieval, process inspection and content-based health checks, with a post-run hook the documentation says does not consistently fire.
  • Supported operating systems are limited to Ubuntu 22.04 or 24.04 and Debian 11 or later, so organisations standardised on Red Hat, Rocky or Alma Linux cannot run it on their approved base image.
  • Backups are separate commands per datastore plugin that you schedule yourself, with no unified snapshot, no point-in-time recovery and no tested restore path, so verifying that a restore works is entirely your responsibility.

Pricing, plan by plan

Cerebrium

Free
  • HobbyFree
    • 3 user seats
    • Up to 3 deployed apps
    • 5 GPU concurrency
  • Standard$100/month
    • Unlimited seats and apps
    • 30 GPU concurrency
    • Custom domains
  • Enterprise$undefined/custom
    • Unlimited resources
    • Volume discounts
    • Dedicated support
  • GPU Compute$undefined/per-second
    • T4: $0.000164/s
    • H100: $0.00167/s

Dokku

Free
  • DokkuFree
    • MIT licensed, no usage limits
    • Command line and SSH only
    • All datastore and certificate plugins
  • Dokku Pro$849/one-time
    • Lifetime licence with free upgrades and no subscription
    • Covers 1 production and 2 pre-production servers
    • Web dashboard and JSON REST API

Which should you pick?

Choose Cerebrium if

  • You need ultra-fast cold starts.
  • You want to start without paying.
  • You work on Cloud, Docker.
  • You also want elastic scaling.
Cerebrium review

Choose Dokku if

  • You need git push deploy.
  • You want to start without paying.
  • You work on Linux, Docker, CLI, Self-hosted.
  • You also want heroku buildpacks.
Dokku review

Questions people ask

Is Cerebrium or Dokku better?
Neither clearly leads. Cerebrium starts at Free and Dokku at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Cerebrium or Dokku?
Cerebrium starts at Free and Dokku at Free.
Does Cerebrium or Dokku run on more platforms?
Cerebrium runs on Cloud, Docker. Dokku runs on Linux, Docker, CLI, Self-hosted.
Can I use Cerebrium for free?
Both have a free tier, so you can try either at no cost before committing.
What is Cerebrium best used for?
Cerebrium is most often used for deploying voice agents and conversational ai applications, video and image model serving with low latency, llm inference and completion endpoints, real-time embeddings and vector database operations. Of those, deploying voice agents and conversational ai applications and video and image model serving with low latency are not what Dokku is typically brought in for.
What can Cerebrium do that Dokku cannot?
Cerebrium covers Ultra-fast cold starts, Elastic scaling, Bring your own code, Multi-region failover. Dokku covers Git push deploy, Heroku buildpacks, Datastore plugins, Automatic certificates.

Answered from the vendors’ own pages

Cerebrium: Is Cerebrium only for inference or can it train models?

Cerebrium supports both inference serving and model training with hyperparameter sweeps. It enables deployment of voice agents, LLMs, video models, and other AI applications.

Source
Dokku: Is Dokku still maintained?

Yes. Version 0.38.27 shipped in August 2026 with roughly six releases in the preceding two months. The caveat is that one maintainer writes nearly all of it.

Cerebrium: How do the cold starts compare to other platforms?

Cerebrium achieves 2-4 second cold starts through memory and GPU snapshotting, significantly faster than traditional 30+ second cold boots. This is competitive with platforms like Beam Cloud.

Source
Dokku: What does Dokku Pro give me over the free edition?

A web dashboard, a JSON REST API, git push over HTTPS, browser log tailing, team management and email support from the maintainers. It costs 849 US dollars once for one production and two pre-production servers.

Cerebrium: What compliance certifications does Cerebrium have?

Cerebrium maintains SOC 2 Type II compliance, HIPAA certification, GDPR compliance, and ISO certification. It provides gVisor container isolation and configurable data residency for regulated workloads.

Source
Dokku: Can Dokku run across multiple servers?

Only through the k3s or Nomad schedulers. The k3s path is missing several commands and health check types, and the older Kubernetes scheduler is deprecated and no longer developed.

Dokku: Will my Heroku application run on it unchanged?

Usually. Dokku runs the same buildpacks and reads a Procfile, so the application layer normally moves across. Add-ons, scaling behaviour and backups are the parts you have to rebuild.

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