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Machine Learning · head to head

Azure Machine Learning vs Poolside

Azure Machine Learning logo

Azure Machine Learning

Machine Learning

Microsoft's managed platform for training, tracking and deploying models on Azure

From
Free
Free plan
Yes
Full reviewWebsite
Poolside logo

Poolside

AI

Open-weight foundation models built for agentic software engineering

From
$0.06/month
Free plan
No
Full reviewWebsite

The short version

  • Only Azure Machine Learning has a free tier, so it costs nothing to try first.
  • Each has a real cost: Azure Machine Learning managed online endpoints are billed per underlying virtual machine for as long as the deployment exists, with no scale to zero, so a model answering a handful of requests a day costs the same as one answering thousands.; Poolside no self-serve public application; access is largely through enterprise sales or third-party model gateways
  • They diverge on capability: Azure Machine Learning covers Workspace, Poolside covers Laguna S 2.1.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Azure Machine Learning and Poolside actually diverge.

Attributes where Azure Machine Learning and Poolside differ
AttributeAzure Machine LearningPoolside
Starting priceFree$0.06/month
Free tierYesNo
PlatformsAzure Cloudweb, api, mac, windows, linux
CategoryMachine LearningAI
Founded19752023

Identical on both: pricing model (usage-based), 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 Azure Machine Learning

  • Workspace
  • Compute clusters
  • MLflow-compatible tracking
  • Model registry
  • Managed online endpoints
  • Batch endpoints
  • Automated machine learning
  • Pipelines

Only in Poolside

  • Laguna S 2.1
  • Laguna XS 2.1
  • Desktop and CLI tools
  • Open-weight models
  • Third-party gateway access

What people use each for

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

Azure Machine Learning

  • Enterprises standardised on Azure where using a different cloud for machine learning would mean a fresh security and compliance reviewnot Poolside
  • Training that needs to burst onto a GPU cluster occasionally without buying hardware, with the cluster scaling back to zero afterwardsnot Poolside
  • Regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based accessnot Poolside
  • Teams already using MLflow who want the tracking interface they know backed by a managed service and enterprise identitynot Poolside

Poolside

  • Enterprises deploying AI coding assistance across large developer organizationsnot Azure Machine Learning
  • Government and defense agencies needing on-premise or sovereign coding modelsnot Azure Machine Learning
  • Teams building custom coding agents on open-weight foundation modelsnot Azure Machine Learning
  • Organizations wanting on-device lightweight coding modelsnot Azure Machine Learning

Where each one falls short

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

Azure Machine Learning

  • Managed online endpoints are billed per underlying virtual machine for as long as the deployment exists, with no scale to zero, so a model answering a handful of requests a day costs the same as one answering thousands.
  • GPU capacity is governed by per-region, per-family quota that must be requested and approved, so a training plan can be blocked by an administrative ticket rather than by budget, and the newest accelerators are often unavailable in the region your data is required to stay in.
  • The v2 Python SDK and command line use a different object model from v1 and code, pipelines and examples written for v1 do not port mechanically, which has left teams maintaining two ways of doing the same thing and searching documentation that mixes both.
  • The workspace binds storage, key vault, container registry and compute together, so recreating or moving one is not a light operation, and configuring it properly with private endpoints and a managed virtual network is a multi-day job for somebody who already knows Azure networking.
  • Experiment history, registered models, environments, endpoints and pipeline definitions live inside the workspace, and although the tracking interface is MLflow-compatible, moving the accumulated lineage and orchestration elsewhere is a rebuild, so the cost of leaving grows every month the team uses it.

Poolside

  • No self-serve public application; access is largely through enterprise sales or third-party model gateways
  • Pricing for the flagship enterprise and government offering is not public and requires a sales conversation
  • Targets organizations with large (5,000+) developer headcounts, making it a poor fit for individuals or small teams
  • Documentation and product surface area are thin compared to consumer-facing coding assistants

Pricing, plan by plan

Azure Machine Learning

Free
  • Free TierFree
    • Limited compute
    • Basic features
  • Pay-as-you-go$0.05/hour
    • Full platform
    • All compute options
    • Enterprise features

Poolside

$0.06/month
  • Laguna XS 2.1$undefined/month
    • $0.06 per 1M input tokens
    • $0.12 per 1M output tokens
    • Available via OpenRouter and Vercel AI Gateway
  • Laguna S 2.1$undefined/month
    • $0.10 per 1M input tokens
    • $0.20 per 1M output tokens
    • 1M token context window
  • Enterprise / Government$undefined/month
    • Custom contracts for large engineering organizations
    • Sales-quoted pricing
    • On-premise or dedicated deployment options

Which should you pick?

Choose Azure Machine Learning if

  • You need workspace.
  • You want to start without paying.
  • You work on Azure Cloud.
  • You also want compute clusters.
Azure Machine Learning review

Choose Poolside if

  • You need laguna s 2.1.
  • You work on web, api, mac, windows, linux.
  • You also want laguna xs 2.1.
Poolside review

Questions people ask

Is Azure Machine Learning or Poolside better?
Neither clearly leads. Azure Machine Learning starts at Free and Poolside at $0.06/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Azure Machine Learning or Poolside?
Azure Machine Learning has a free tier; the other does not. Paid plans start at Free for Azure Machine Learning and $0.06/month for Poolside.
Does Azure Machine Learning or Poolside run on more platforms?
Azure Machine Learning runs on Azure Cloud. Poolside runs on web, api, mac, windows, linux.
Can I use Azure Machine Learning for free?
Yes. Azure Machine Learning has a free tier, so you can try it without paying. Poolside starts at $0.06/month.
What is Azure Machine Learning best used for?
Azure Machine Learning is most often used for enterprises standardised on azure where using a different cloud for machine learning would mean a fresh security and compliance review, training that needs to burst onto a gpu cluster occasionally without buying hardware, with the cluster scaling back to zero afterwards, regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based access, teams already using mlflow who want the tracking interface they know backed by a managed service and enterprise identity. Of those, enterprises standardised on azure where using a different cloud for machine learning would mean a fresh security and compliance review and training that needs to burst onto a gpu cluster occasionally without buying hardware, with the cluster scaling back to zero afterwards are not what Poolside is typically brought in for.
What can Azure Machine Learning do that Poolside cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. Poolside covers Laguna S 2.1, Laguna XS 2.1, Desktop and CLI tools, Open-weight models.

Answered from the vendors’ own pages

Azure Machine Learning: Is there a charge for the workspace itself?

No charge for the workspace resource. You pay for the compute it runs, the storage it uses, the container registry, key vault and any endpoints left running, which is where essentially the whole bill comes from.

Poolside: How is Poolside's Laguna model usage priced?

Laguna XS 2.1 is priced at $0.06 per 1M input tokens and $0.12 per 1M output tokens; Laguna S 2.1 is $0.10 per 1M input tokens and $0.20 per 1M output tokens.

Source
Azure Machine Learning: Does it work with MLflow?

Yes. The tracking interface is MLflow-compatible, so existing logging code generally works unchanged, and that compatibility is the least locked-in part of the platform.

Poolside: Where can I access Poolside's models?

Laguna models are available through third-party gateways OpenRouter and Vercel AI Gateway, as well as via Poolside's own desktop and CLI tools.

Source
Azure Machine Learning: What is the difference between SDK v1 and v2?

A different object model and a different way of expressing jobs, components and endpoints. v2 is the current one. v1 code does not translate mechanically and a lot of material found online still assumes v1, which is a common source of wasted time.

Poolside: Does Poolside offer enterprise or government pricing?

Yes, Poolside offers custom, sales-quoted contracts for large enterprises and government/defense customers, including on-premise or dedicated deployment options.

Source
Azure Machine Learning: Do endpoints scale to zero?

Managed online endpoints do not; they hold their virtual machines. Batch endpoints only consume compute while a job runs, so intermittent workloads are much cheaper served as batch where the use case allows it.

Poolside: How do I get started building with Poolside's models?

Poolside's documentation provides a quickstart guide for installing the `pool` CLI and running an agent task, alongside API documentation for custom integrations.

Source
Azure Machine Learning: Do I need an ML engineer to run it?

For the data science work, not necessarily. For the workspace itself, yes, somebody has to understand Azure identity, networking, quota and cost management, and on teams without that person the platform becomes the bottleneck rather than the model.

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