Buyer’s guide · Enterprise AI platform
Enterprise AI platform: a buyer’s guide
What an enterprise AI platform actually includes, how the main vendors differ, what drives cost, and the questions to ask before you commit. Written to be useful whichever platform you choose.
Last reviewed by the Perimattic AI Suite team
In short
What is an enterprise AI platform?
An enterprise AI platform is the set of services an organisation uses to build, deploy, govern and scale AI: access to models, connections to company data, tools to build agents and applications, and the controls to run them safely. Most enterprises assemble it from a cloud provider’s AI services plus their data platform, rather than buying one product that does everything.
Rarely one vendor
Teams often use one cloud’s model service, another vendor’s data platform and open-source agent frameworks. The platform is the combination, so the gaps sit between products.
Where observability fits
Observability and governance run across every layer. If they live inside one vendor’s console, anything built elsewhere is invisible to them.
Architecture
The six layers of an enterprise AI platform
Models and gateways
Hosted model APIs, open-weight models you run yourself, and a gateway that handles routing, keys, quotas and fallbacks.
Data and retrieval
Connectors to company data, vector search and the pipelines that keep retrieved content current and permissioned.
Orchestration and agents
Frameworks and runtimes for multi-step workflows, tool use and agents that act on other systems.
Applications
Copilots, chat assistants, internal agents and AI features inside existing products.
Governance and observability
Tracing, evaluation, cost attribution, access control, policy enforcement and audit evidence across all of the above.
Infrastructure
Cloud accounts, Kubernetes, GPUs and networking, including any on-premises or sovereign-cloud requirements.
Vendors
The main enterprise AI platforms compared
These are the platforms enterprise buyers most often shortlist. Each is broad, so the table focuses on where it tends to fit rather than a feature-by-feature score.
| Platform | What it brings together | Tends to fit | Check before you commit |
|---|---|---|---|
| Amazon Bedrock (with Bedrock AgentCore) | Many foundation models behind one API, plus a managed runtime for agents built with any framework | Organisations standardised on AWS | Which models and regions you need; how agent telemetry leaves AWS |
| Microsoft Foundry (formerly Azure AI Foundry) | Model catalogue, agent service and tooling tied into Azure | Microsoft-centric estates | Regional availability for the models you plan to use |
| Google Gemini Enterprise Agent Platform | Vertex AI model services with agent building, governance and runtime, announced April 2026 | Google Cloud users and Gemini-first teams | Migration path for existing Vertex AI workloads |
| Databricks Data Intelligence Platform | AI and agents built next to lakehouse data, with Unity Catalog governance | Data-heavy organisations already on Databricks | How model calls outside Databricks are governed |
| IBM watsonx | watsonx.ai for models, watsonx.data for data and watsonx.governance for oversight | Organisations prioritising governance and hybrid deployment | Fit with the clouds and models you already use |
Product names change often in this market. Check each vendor’s current documentation.
Buyer’s checklist
Questions to ask any enterprise AI platform vendor
- Which models can we use, in which regions, and can we bring our own?
- How is company data connected, and are source permissions respected at retrieval?
- Can one user request be traced across every model, agent and tool it touches?
- Is answer quality measured on production traffic?
- Can AI spend be attributed to teams, features and users?
- What evidence can we export for auditors and regulators?
- Does telemetry work for workloads outside your platform?
- What does it cost at our expected volume, and what changes the bill?
Observability
Why the observability layer should sit across platforms
Most enterprises run AI on more than one platform within a year or two: a cloud model service here, a data platform there, an agent framework somewhere else. Monitoring that lives inside one vendor sees only its own share. An OpenTelemetry-based observability layer can follow a request across all of them and produce one set of evidence. That is the role Perimattic AI Suite plays: it doesn’t replace your platform, it observes and governs what runs on it. The AI observability guide explains the signals involved.
Cost
What an enterprise AI platform costs
There is no single price, because the platform is several products. Budget for each part separately, then use the AI ROI calculator to check whether a use case pays back.
| Cost area | What drives it |
|---|---|
| Model usage | Tokens in and out, model choice, caching and the number of agent steps per task |
| Platform and runtime fees | Agent runtimes, managed endpoints, vector search and gateways |
| Data work | Connectors, pipelines, cleaning and keeping retrieved content current |
| Engineering | Building, testing and maintaining applications and agents |
| Governance and observability | Tracing, evaluation, retention and audit evidence |
| People and change | Training, process changes and support for the teams who use it |
FAQ
Common questions
Short answers to the questions people ask most often about this topic.
What exactly is enterprise AI?
Enterprise AI is AI used inside an organisation’s own processes and products, at the scale, security and accountability a business needs: for example claims triage, customer support, document processing or forecasting. It differs from consumer AI mainly in its demands for data control, integration and auditability.
What are the main enterprise AI platforms?
The most common shortlist is Amazon Bedrock, Microsoft Foundry, Google’s Gemini Enterprise Agent Platform, Databricks and IBM watsonx. Many organisations combine one of these with open-source frameworks and separate observability and governance tools.
Is Perimattic AI Suite an enterprise AI platform?
No. Perimattic AI Suite is an AI observability platform. It runs across the enterprise AI platform you already use, tracing and evaluating every model call and agent step and turning that telemetry into audit evidence.
Should we build our own AI platform or buy one?
Most organisations do both: buy the model access and runtimes, and build the integrations and applications that are specific to them. Building the infrastructure layers yourself only pays off at large scale or under strict sovereignty requirements.
How do we know if we are ready for an enterprise AI platform?
Check whether you have a clear first use case, accessible data, someone accountable for it and a way to measure results. The AI readiness assessment scores those factors in a few minutes.
Go further
Related tools, guides and services
- ArticleEnterprise AI transformationMoving from pilots to enterprise scale.Open
- Free toolAI ROI calculatorEstimate payback and three-year return for an AI use case.Open
- Free toolAI readiness assessmentScore your data, people and process readiness for AI.Open
- ServiceAI development servicesPerimattic builds production AI on the platform you choose.Open
- ServiceAWS AI and ML servicesBedrock and SageMaker builds by Perimattic engineers.Open
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