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Energy · head to head

AVEVA PI System vs Azure Machine Learning

AVEVA PI System logo

AVEVA PI System

Energy

Industrial data infrastructure for real-time insights

From
On request
Rated
-
Full reviewWebsite
Azure Machine Learning logo

Azure Machine Learning

Machine Learning

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

From
Free
Rated
-
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: AVEVA PI System pricing is not published and comes through an account representative; 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.
  • They diverge on capability: AVEVA PI System covers Real-time data collection, Azure Machine Learning covers Workspace.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which AVEVA PI System and Azure Machine Learning actually diverge.

Attributes where AVEVA PI System and Azure Machine Learning differ
AttributeAVEVA PI SystemAzure Machine Learning
Starting priceOn requestFree
Pricing modelone-timeusage-based
Free tierNoYes
PlatformsDesktop, Web, Mobile, ApiAzure Cloud
CategoryEnergyMachine Learning
Founded19671975

Identical on both: 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 AVEVA PI System

  • Real-time data collection
  • Time-series data storage
  • Asset framework
  • Visualization
  • Analytics
  • Event management
  • Notifications
  • Data integration

Only in Azure Machine Learning

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

What people use each for

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

AVEVA PI System

  • Industrial data collection and storage for oil, gas, and chemical processingnot Azure Machine Learning
  • Real-time operational data visualization and HMI dashboardsnot Azure Machine Learning
  • Supply chain and asset performance monitoring across sitesnot Azure Machine Learning
  • Compliance and audit trail management for regulated industriesnot Azure Machine Learning
  • Integration with SCADA systems and manufacturing executionnot Azure Machine Learning

Azure Machine Learning

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

Where each one falls short

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

AVEVA PI System

  • Pricing is not published and comes through an account representative
  • The vendor is moving to a subscription commercial model, so perpetual licence expectations no longer hold
  • Made up of several separately named components, including PI Server, PI Vision, Edge Data Store and the adapters and connectors needed per data source

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.

Pricing, plan by plan

AVEVA PI System

On request
  • Data Archive$undefined/license
    • Time-series data storage
    • High-speed data collection
    • Data compression
  • Operations$undefined/license
    • PI Vision dashboards
    • AF analytics
    • Event frames
  • Enterprise$undefined/license
    • PI Integrator
    • Cloud connectivity
    • Advanced analytics

Azure Machine Learning

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

Which should you pick?

Choose AVEVA PI System if

  • You need real-time data collection.
  • You work on Desktop, Web, Mobile, Api.
  • You also want time-series data storage.
AVEVA PI System review

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

Questions people ask

Is AVEVA PI System or Azure Machine Learning better?
Neither clearly leads. AVEVA PI System starts at On request and Azure Machine Learning at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AVEVA PI System or Azure Machine Learning?
Azure Machine Learning has a free tier; the other does not. Paid plans start at On request for AVEVA PI System and Free for Azure Machine Learning.
Does AVEVA PI System or Azure Machine Learning run on more platforms?
AVEVA PI System runs on Desktop, Web, Mobile, Api. Azure Machine Learning runs on Azure Cloud.
Can I use Azure Machine Learning for free?
Yes. Azure Machine Learning has a free tier, so you can try it without paying. AVEVA PI System starts at On request.
What is AVEVA PI System best used for?
AVEVA PI System is most often used for industrial data collection and storage for oil, gas, and chemical processing, real-time operational data visualization and hmi dashboards, supply chain and asset performance monitoring across sites, compliance and audit trail management for regulated industries. Of those, industrial data collection and storage for oil, gas, and chemical processing and real-time operational data visualization and hmi dashboards are not what Azure Machine Learning is typically brought in for.
What can AVEVA PI System do that Azure Machine Learning cannot?
AVEVA PI System covers Real-time data collection, Time-series data storage, Asset framework, Visualization. Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry.

Answered from the vendors’ own pages

AVEVA PI System: What is AVEVA PI System pricing?

AVEVA does not list PI System pricing on its website. AVEVA markets industrial software through enterprise sales channels with custom quotes.

Source
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.

AVEVA PI System: Does AVEVA offer a Flex Subscription Program?

AVEVA mentions Flex Subscription Program on its site for flexible product access, but pricing and terms are not published publicly.

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.

AVEVA PI System: Is AVEVA PI System available in the cloud?

AVEVA offers cloud options for its products, but per-seat or usage-based pricing for cloud deployments is not disclosed on the vendor website.

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.

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.

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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