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AI · head to head

Galileo vs Kubeflow

Galileo logo

Galileo

AI

Evaluation and observability platform for GenAI applications and agents

From
Free
Free plan
Yes
Full reviewWebsite
Kubeflow logo

Kubeflow

Machine Learning

Machine learning toolkit for Kubernetes

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: Galileo the free plan is limited to 5,000 traces per month, which is quickly outgrown by production workloads.; Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
  • They diverge on capability: Galileo covers Pre-built evaluations, Kubeflow covers ML pipelines.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Galileo and Kubeflow actually diverge.

Attributes where Galileo and Kubeflow differ
AttributeGalileoKubeflow
Pricing modelfreemiumUnknown
Platformsweb, apiKubernetes
CategoryAIMachine Learning
FoundedUnknown2017

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 Galileo

  • Pre-built evaluations
  • Ground truth capture
  • Luna models
  • Agent behavior analysis
  • Production guardrails
  • Flexible deployment

Only in Kubeflow

  • ML pipelines
  • Training operators
  • Model serving
  • Jupyter notebooks
  • Hyperparameter tuning
  • Kubernetes
  • TensorFlow
  • PyTorch

What people use each for

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

Galileo

  • Evaluating RAG and agent applications before production releasenot Kubeflow
  • Monitoring live GenAI applications for failures and driftnot Kubeflow
  • Applying real-time guardrails without custom integration worknot Kubeflow
  • Reducing evaluation costs using distilled Luna judge modelsnot Kubeflow

Kubeflow

  • Machine learningnot Galileo
  • Data analysisnot Galileo
  • Model trainingnot Galileo
  • Predictive analyticsnot Galileo

Where each one falls short

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

Galileo

  • The free plan is limited to 5,000 traces per month, which is quickly outgrown by production workloads.
  • Real-time guardrails and unlimited trace capacity are reserved for the custom-priced Enterprise tier.
  • Pro plan pricing scales with trace volume, so costs can grow unpredictably as usage increases.
  • On-premises deployment requires an Enterprise contract rather than being available self-serve.

Kubeflow

  • Complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
  • Resource-intensive infrastructure with minimal installs consuming significant CPU and memory
  • Limited multi-tenancy support and multi-cloud setup leaves users largely on their own
  • No native CI/CD integration, requiring custom glue code for versioning and automated deployments
  • Debugging jobs and monitoring workloads often requires dropping down into raw Kubernetes commands

Pricing, plan by plan

Galileo

Free
  • FreeFree
    • 5,000 traces/month
    • Unlimited users
    • Unlimited custom evaluations
  • Pro$100/month
    • 50,000 traces/month
    • Standard role-based access control
    • Advanced analytics and insights
  • Enterprise$undefined/mo
    • Unlimited trace capacity
    • Custom rate limits
    • Hosted, VPC, or on-prem deployment

Kubeflow

Free

No published plan breakdown. See the Kubeflow review.

Which should you pick?

Choose Galileo if

  • You need pre-built evaluations.
  • You want to start without paying.
  • You work on web, api.
  • You also want ground truth capture.
Galileo review

Choose Kubeflow if

  • You need ml pipelines.
  • You want to start without paying.
  • You work on Kubernetes.
  • You also want training operators.
Kubeflow review

Questions people ask

Is Galileo or Kubeflow better?
Neither clearly leads. Galileo starts at Free and Kubeflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Galileo or Kubeflow?
Galileo starts at Free and Kubeflow at Free.
Does Galileo or Kubeflow run on more platforms?
Galileo runs on web, api. Kubeflow runs on Kubernetes.
Can I use Galileo for free?
Both have a free tier, so you can try either at no cost before committing.
What is Galileo best used for?
Galileo is most often used for evaluating rag and agent applications before production release, monitoring live genai applications for failures and drift, applying real-time guardrails without custom integration work, reducing evaluation costs using distilled luna judge models. Of those, evaluating rag and agent applications before production release and monitoring live genai applications for failures and drift are not what Kubeflow is typically brought in for.
What can Galileo do that Kubeflow cannot?
Galileo covers Pre-built evaluations, Ground truth capture, Luna models, Agent behavior analysis. Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks.

Answered from the vendors’ own pages

Galileo: What does Galileo cost?

Galileo offers a free plan, a Pro plan at $100/month billed yearly (with a 33% annual discount), and a custom-priced Enterprise plan for unlimited trace capacity.

Source
Kubeflow: Is Kubeflow free to use?

Yes, Kubeflow is free and open-source under Apache License 2.0. However, you pay for the underlying Kubernetes infrastructure, which typically costs $500 to $5,000 per month depending on scale and cloud provider.

Source
Galileo: Is there a free plan, and what are its limits?

The Free plan includes 5,000 traces per month with unlimited users and unlimited custom evaluations, aimed at developers and small teams experimenting with GenAI.

Source
Kubeflow: Do I need Kubernetes expertise to use Kubeflow?

Kubeflow requires significant Kubernetes and DevOps expertise. The installation deploys dozens of services and CRDs, often requiring manual configuration and troubleshooting. Data scientists typically need to convert scripts to containerized components.

Source
Galileo: How is usage metered?

Galileo's pricing scales based on the number of traces processed each month, with Free capped at 5,000, Pro at 50,000, and Enterprise offering unlimited trace capacity.

Source
Kubeflow: What platforms can Kubeflow run on?

Kubeflow runs on any Kubernetes-compliant cluster, including on-premise, AWS, Azure, Google Cloud, and hybrid environments. This multi-cloud portability is one of its key advantages over managed alternatives.

Source
Kubeflow: How does Kubeflow compare to managed services like SageMaker?

Kubeflow offers multi-cloud portability and lower long-term costs but requires more operational overhead. SageMaker provides a fully managed experience with better UI and less infrastructure work, but creates vendor lock-in to AWS.

Source
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