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Machine Learning · head to head

Azure Machine Learning vs Knack

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
Visit Azure Machine LearningFull review
Knack logo

Knack

Databases

Hosted no-code database and web app builder priced by stored records and storage, with unlimited user accounts on every plan.

From
$59/month
Free plan
No
Visit KnackFull review

The short version

  • Azure Machine Learning has a free plan, then $0.05/mo; Knack starts at $59/mo.
  • Only Azure Machine Learning lists compute clusters; only Knack lists visual database builder.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Azure Machine Learning and Knack differ
AttributeAzure Machine LearningKnack
Starting priceFree$59/month
Pricing modelUsage-basedSubscription
Free tierYesNo
CategoryMachine LearningDatabases

Identical on both: platforms (Web).

Not yet recorded: founded for Knack.

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 Knack

  • Visual database builder
  • User logins and roles
  • Page builder
  • Unlimited users
  • E-commerce
  • Automation flows
  • Scheduled tasks
  • REST API and webhooks

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 Knack
  • Training that needs to burst onto a GPU cluster occasionally without buying hardware, with the cluster scaling back to zero afterwardsnot Knack
  • Regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based accessnot Knack
  • Teams already using MLflow who want the tracking interface they know backed by a managed service and enterprise identitynot Knack

Knack

  • A member or client portal where thousands of external people need their own login and see only their own recordsnot Azure Machine Learning
  • Replacing a shared spreadsheet used by a team that has outgrown it but cannot justify a developer to build a bespoke internal toolnot Azure Machine Learning
  • Inventory, asset or case tracking for a small operations team that needs roles, forms and a record of who changed whatnot Azure Machine Learning
  • A registration or booking application for an association, school or non-profit where the user count is large and the record count stays modestnot 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

Knack

  • Plans are capped by stored records, so an application that logs events or keeps history rather than current state climbs tiers on data volume alone, and archiving old records becomes a permanent maintenance chore rather than a one-off
  • API access is capped per day, at 1,000 calls on the entry plan and 10,000 at the Corporate tier, so a scheduled sync or a busy automation can exhaust the day's quota before the working day ends and integrations then fail silently
  • The application itself cannot be exported or self-hosted; records can be downloaded but the pages, roles, flows and business logic exist only inside Knack, so outgrowing the platform means rebuilding the app rather than moving it
  • Customisation beyond the builder is limited to injected CSS and JavaScript that Knack's support does not cover, so any non-standard interface behaviour becomes yours to maintain against a DOM the vendor can change without warning
  • Compliance capabilities that regulated buyers assume are baseline, HIPAA and GovCloud options, advanced SSO, IP restrictions and private environments, are gated to the Corporate and Enterprise tiers, so a small regulated pilot cannot legitimately start on an entry plan

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

Knack

$59/month
  • Starter$59/mo
    • 20,000 database records
    • Unlimited users, tables, fields
    • 25 AI credits
  • Pro$130/mo
    • 50,000 database records
    • Remove Knack branding
    • E-commerce payments
  • Corporate$300/mo
    • 125,000-2.5M database records
    • App restore functionality
    • Advanced SSO
  • EnterpriseOn request
    • Dedicated account manager
    • HIPAA/GovCloud-based options
    • Custom pricing

Which should you pick?

Choose Azure Machine Learning if

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

Choose Knack if

  • You need visual database builder.
  • You also want user logins and roles.
Knack review

Questions people ask

Is Azure Machine Learning or Knack better?
Neither clearly leads. Azure Machine Learning starts at Free and Knack at $59/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Azure Machine Learning or Knack?
Azure Machine Learning has a free tier; the other does not. Paid plans start at Free for Azure Machine Learning and $59/month for Knack.
Does Azure Machine Learning or Knack run on more platforms?
Both run on Web, so platform support will not decide this one for you.
Can I use Azure Machine Learning for free?
Yes. Azure Machine Learning has a free tier, so you can try it without paying. Knack starts at $59/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 Knack is typically brought in for.
What can Azure Machine Learning do that Knack cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. Knack covers Visual database builder, User logins and roles, Page builder, Unlimited users.

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.

Knack: Is Knack charged per user?

No. User accounts are unlimited on all plans. Plans are bounded by stored record count, file storage, API calls per day and automation transactions.

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.

Knack: What happens when I exceed the record limit?

You move to a higher tier or reduce stored records. Because the limit is on data rather than people, apps that accumulate history hit it while apps that track a fixed set of entities generally do not.

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.

Knack: Can I export my application or self-host it?

You can export record data. The application itself, its pages, roles, permissions and flows, is not exportable and cannot be self-hosted, so leaving means rebuilding elsewhere.

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.

Knack: Is it suitable for public-facing portals?

That is its strongest case, because logins are unlimited and role-based permissions are built in. The constraint is record volume and API throughput, not audience size.

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.

Knack: Can I use it for regulated data?

HIPAA-aligned and government cloud options exist but only on the Enterprise tier, along with private server environments. Lower tiers should not be used for regulated workloads.

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