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An AI data platform is worth buying only when its architecture, APIs, governance controls, security responsibilities, operating model, and billing units fit your defined workloads. Start by documenting the data, users, source systems, model and agent actions, failure consequences, and regulatory boundaries you must support. Then require each vendor to demonstrate the same workload, permissions, policy decisions, logs, and cost assumptions. A feature list or gateway demo is not enough: you need evidence that the platform can control both the AI assets and the traffic using them, while making ownership and spend visible.
What should you define before comparing AI data platforms?
Define the workload and its boundaries before looking at products. Microsoft’s Cloud Adoption Framework treats readiness, architecture, governance and security baselines, and operational standards as connected work; separating them produces an attractive proof of concept that cannot be operated safely.
Describe the workload in operational terms
- Use case: State whether the system serves analytics, retrieval-augmented generation, model inference, agent actions, data sharing, or a combination. Identify synchronous user requests, scheduled jobs, and event-driven calls.
- Users and callers: List employees, customers, partners, service accounts, applications, and autonomous agents. Record whether each caller needs read, write, execute, approval, or administration rights.
- Information handled: Classify personal, financial, health, confidential, regulated, export-controlled, and public data. Note where prompts, retrieved context, model outputs, tool arguments, and logs may contain that information.
- Source systems: Inventory warehouses, lakehouses, operational databases, files, SaaS applications, message queues, vector stores, model endpoints, and external tools. Mark systems that must remain in a particular region or network.
- Business impact: Define what happens when an answer is wrong, a tool action is unauthorized, a model is unavailable, or a request is delayed. These consequences determine approval gates, availability targets, rollback procedures, and audit depth.
- Scale and shape: Estimate peak and average requests, payload sizes, concurrent users, batch volumes, model context sizes, retention periods, and growth. Give vendors the same assumptions so their proposals can be compared.
Set explicit data and deployment boundaries
Write down where data may be processed, cached, logged, backed up, and replicated. A platform that can connect to a source is not necessarily allowed to copy that source into a managed index or send it to a model provider. Identify network zones, private connectivity requirements, approved regions, tenant separation, and environments for development, testing, and production.
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Also decide which operations must be reversible. For example, an agent that drafts a payment change may be acceptable in a sandbox but require human approval before production execution. Your boundary document should say which tools are read-only, which can mutate records, and which actions require dual control.
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How should architecture and deployment boundaries be evaluated?
Ask for a component diagram that follows data and control flow from the caller to the source, model, tool, and audit record. The diagram should identify every service that stores content, evaluates policy, routes requests, or makes an authorization decision.
Check the control plane and data plane separately
- Control plane: Look for catalogs, model and tool registration, policy authoring, environment promotion, identity administration, key management, cost allocation, and audit configuration.
- Data plane: Trace live requests, retrieval, prompt construction, model invocation, tool execution, response filtering, retries, and telemetry. Confirm where payloads are visible and whether sensitive fields can be masked.
- Deployment model: Determine which components are vendor-managed SaaS, customer-managed in a cloud account, deployed on premises, or split across these locations. Ask what changes when a component is moved between regions or environments.
- Failure behavior: Require demonstrations of timeout, dependency outage, revoked credentials, rate-limit exhaustion, malformed input, and policy denial. A safe failure should be distinguishable from a successful but empty response.
Require architecture evidence, not a slide deck
For a representative source and identity set, have the vendor show the exact configuration used to enforce network, data-access, and model-selection boundaries. Request exportable configuration or infrastructure definitions where available, and record which controls depend on another cloud or security product. If a control is described as configurable but cannot be shown in the proposed edition, treat it as unverified.
What is the difference between an API platform, API management, a gateway, and governance?
These terms describe different scopes. WSO2’s API Platform Overview distinguishes a full platform from a gateway-only path, while Apache APISIX’s governance guidance separates governance decisions from management functions and runtime enforcement. Ask the vendor to map each required function to a named component and owner.
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|---|---|---|
| API platform | Combines design, catalog or registry, lifecycle, policy, analytics, developer or consumer management, and runtime components. | Which functions are native, which require an add-on, and which are left to another product? |
| API management | Organizes API design and publication, versions, documentation, subscriptions, consumer onboarding, policy administration, and usage reporting. | Can the team manage model, retrieval, and tool APIs through the same lifecycle, or are AI calls handled separately? |
| API gateway | Interposes on live traffic to authenticate, authorize, route, transform, throttle, and observe requests. | Which policies run on every path, and what happens when traffic bypasses the gateway? |
| Governance | Defines decision rights, standards, ownership, exceptions, lifecycle requirements, and evidence that controls operated. | Who approves exceptions, how are policies tested, and where is proof retained for an audit? |
A gateway can enforce a selected rule, but it cannot by itself decide who owns an API, whether a model is approved, how an exception expires, or whether a retired interface remains discoverable. Conversely, a governance process without runtime enforcement produces standards that traffic can ignore. Your target architecture may need all four scopes, but they do not have to come from one product.
Test API lifecycle coverage
- Submit an API or model endpoint through the proposed design and review process.
- Apply required naming, authentication, data-classification, and versioning checks before publication.
- Promote the interface from development to test and production with an identifiable approver and an immutable change record.
- Generate traffic with valid, invalid, over-quota, and unauthorized callers. Capture the response, policy decision, and audit event.
- Deprecate and revoke the interface, then verify that subscriptions, credentials, documentation, routes, and cached metadata are handled as specified.
How do you govern AI assets and the traffic that uses them?
Govern both the assets and the calls. Databricks’ AI governance guide describes asset governance through Unity Catalog and traffic governance through Unity Gateway, including access privileges, request routing, rate or budget controls, and usage tracking. Those are vendor-documented capabilities, not proof that another edition or platform provides equivalent controls; verify the exact deployment.
Govern the asset inventory
Your inventory should represent more than datasets. Include models, prompts, evaluation sets, feature or vector indexes, tools, connectors, secrets references, agent definitions, policies, and approved use cases. For each asset, record owner, steward, classification, permitted environments, dependencies, version, approval state, expiry or review date, and evidence of testing.
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Ask whether relationships are first-class. A model may depend on a prompt template, a retrieval index, a connection, and a tool with different owners. If the platform cannot show those dependencies, a change to one component can bypass review of the others.
Govern requests and responses
Traffic controls should preserve the caller’s identity and authorization context as a request moves through routing, retrieval, model invocation, and tool execution. Verify where the platform applies:
- Authentication and authorization for users, workloads, and delegated agents.
- Route selection by tenant, environment, model, region, or data classification.
- Rate limits, concurrency limits, token or payload limits, and budget ceilings.
- Prompt, response, and tool-argument filtering or redaction.
- Usage records that identify caller, asset, policy result, latency, failures, and billable dimensions.
- Approval or step-up authentication for high-impact actions.
Request a configuration-level demonstration: create a policy, send a request that should pass, send one that should be denied or throttled, and show the resulting logs and chargeback record. Ask whether an administrator can disable the policy silently, and require alerts or approvals for that change.
Who is responsible for security controls?
Do not accept “secure by design” as a responsibility model. Microsoft’s cloud guidance places security and governance baselines inside a broader operating process, and the cited platform documentation does not establish a complete cross-vendor checklist. Build a control map that names the provider, cloud operator, customer team, and connected-system owner for every control.
| Control area | Questions to assign |
|---|---|
| Identity and keys | Who provisions identities, rotates keys, manages workload identities, and revokes access? Are short-lived credentials and conditional access supported? |
| Authorization | Which system evaluates dataset, model, tool, row, column, and action permissions? Is the decision preserved through delegated calls? |
| Network and isolation | Who configures private connectivity, egress restrictions, firewall rules, tenant isolation, and environment separation? |
| Data protection | Who controls encryption keys, masking, retention, deletion, backups, and residency? Are prompts and outputs included in the same policy? |
| Monitoring and response | Who receives security and policy alerts, investigates anomalies, preserves evidence, and executes containment or rollback? |
| Supply chain | Who approves model, connector, package, prompt, and tool updates? How are provenance, vulnerability notices, and emergency disablement handled? |
| Compliance evidence | Which attestations, penetration-test summaries, subprocessor lists, and incident commitments apply to the exact service and region? |
Check identity propagation in a live scenario
Use a user who may read one data domain but not another, and an agent that can call a tool only after approval. Confirm that the source system sees the correct identity or an explicitly governed service identity, that denied data is not returned through summaries or caches, and that every authorization decision can be traced after the fact.
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Use one scripted scenario for every shortlisted platform. A fair proof tests controls and operating work, not just response quality.
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- Prepare representative inputs. Use sanitized records with the same schemas, classifications, relationships, and permission patterns as production. Include at least one sensitive field and one prohibited action.
- Declare acceptance criteria. Define latency ranges, availability assumptions, retrieval quality measures, policy outcomes, logging fields, recovery time, and maximum spend for the exercise.
- Configure identities and boundaries. Create separate development and production-like environments, least-privilege roles, network restrictions, and approved model and tool lists.
- Exercise normal and adversarial paths. Test valid requests, cross-tenant attempts, prompt injection, oversized payloads, revoked credentials, policy changes, dependency outages, and replayed requests.
- Collect evidence. Export configurations, policy decisions, request and response metadata, usage records, alerts, and administrator activity. Note any control performed manually outside the platform.
- Model operations. Have the proposed support and platform teams rotate a key, roll back a version, investigate an anomalous request, and restore service after a dependency failure.
- Document gaps and workarounds. Classify each gap as native capability, integration, custom code, manual procedure, or unresolved risk. Price and staff the workaround before scoring the product.
How do you compare shortlisted platforms?
Use a weighted scorecard tied to your documented workload. Do not award points for a feature name without configuration evidence and an owner.
| Axis | Buyer questions | Evidence to request |
|---|---|---|
| Workload fit | Are analytics, retrieval, model use, agent actions, or combinations supported with the required sources and permissions? | Workload-specific architecture and demonstration using representative data and identities. |
| API scope | Is this a full lifecycle platform, gateway, registry or catalog, or a combination? Which functions require another product? | Component diagram, API inventory, design checks, runtime policies, lifecycle process, and ownership map. |
| Governance | Are assets and requests governed? Where do permissions, limits, budgets, usage records, and exceptions apply? | Configuration demonstration, denied and throttled requests, approval records, and audit evidence. |
| Security | How do identity, access, policy enforcement, and deployment boundaries map to this workload? | Product security documentation, role mapping, architecture, contractual commitments, and control-responsibility matrix. |
| Operations | Who owns standards, exceptions, lifecycle, monitoring, evidence, and support? | RACI, runbooks, escalation path, service levels, and staffing assumptions. |
| Pricing | What unit drives charges, how is it counted, and what changes as usage or governed assets grow? | Current rate card, workload quote, usage assumptions, included environments, overage rules, and contract terms. |
Weight the axes according to failure impact. A regulated workload may give security evidence and auditability more weight than developer convenience; a high-volume public API may prioritize predictable throttling and request economics. Keep a separate record of assumptions so a score does not hide an uncertain price or an untested integration.
What pricing questions should procurement ask?
There is no reliable, apples-to-apples price list across AI data platforms in the available evidence. Obtain a current rate card and a workload-specific quote, then model costs under normal, peak, growth, and failure-retry conditions.
Identify the billable unit
Ask whether charges are based on governed assets, users, requests, tokens, data volume, storage, compute time, environments, connectors, egress, support, or a combination. Clarify the counting interval, rounding, minimum commitment, included allowance, and treatment of retries, failed calls, cached responses, replicas, and attached assets.
Use the Microsoft Purview example correctly
Microsoft Purview’s Data Governance Billing FAQ says its data-governance pricing model took effect on January 6, 2025. The FAQ describes daily counting of unique governed assets and examples in which attached assets are charged. This is a product-specific billing example, not an industry rate or a current quote for every Purview configuration; verify the edition, region, and contract.
Build a five-year cost model
- Separate one-time migration, integration, policy design, and training work from recurring service charges.
- Model development, test, disaster-recovery, and production environments individually.
- Include cloud storage, compute, network egress, model-provider fees, observability, security tooling, and support that the platform quote excludes.
- Show how costs change when assets, tenants, users, requests, context size, or retention increase.
- Record renewal caps, price-change notice, minimum commitments, overage rates, termination assistance, and data-export costs.
Require the vendor to sign the assumptions used in the quote. If the vendor cannot explain how a unit is counted, treat the estimate as a range and add a measurement period before committing to a large minimum.
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What operating model is needed after purchase?
Assign durable ownership before production. A platform team can provide paved roads, but domain teams still own data meaning, approved use, and business outcomes.
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- Platform owner: service reliability, reference architecture, shared integrations, environments, and vendor relationship.
- Security and privacy: identity standards, data classification, threat modeling, incident response, and exception approval.
- Data and model owners: quality, permitted use, evaluation, retention, versioning, and retirement.
- API or product owners: interface contracts, consumers, deprecation notices, quotas, and support expectations.
- FinOps or finance: budgets, allocation tags, unit economics, anomaly review, and forecast updates.
Keep evidence useful
Standardize the minimum audit record: caller, asset and version, policy decision, route, environment, timestamp, outcome, usage dimensions, administrator change, and correlation identifier. Set retention and access rules according to your legal and security requirements, and test that records can be exported for an investigation without exposing unnecessary payload content.
What are common buying mistakes and how do you fix them?
Choosing a gateway for a governance problem
Symptom: traffic is authenticated and throttled, but no one can show approved owners, model lineage, exception expiry, or retirement evidence. Fix: add lifecycle, catalog, ownership, and evidence requirements to the architecture and assign them to named teams.
Governing assets but not calls
Symptom: models and datasets are cataloged, while untracked scripts or agents invoke them with unrestricted budgets. Fix: route production traffic through an enforceable control point, preserve identity, and require usage records for every approved path.
Assuming the provider owns the whole security stack
Symptom: a contract cites platform security, but no one owns keys, network egress, source permissions, or incident response. Fix: complete the control-responsibility matrix and obtain written commitments for provider-managed controls.
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Comparing headline prices
Symptom: a low request price excludes environments, storage, egress, support, or attached-asset charges. Fix: compare the same workload model, counting rules, commitments, overages, and five-year operating costs.
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Scoring unverified features
Symptom: a roadmap item or higher edition is treated as available in the proposed deployment. Fix: mark it “not demonstrated,” price the required edition, and make acceptance testing a contract milestone.
What should be in the final procurement checklist?
- Workload, user, data-classification, source-system, residency, and failure-impact statements are approved.
- Control-plane and data-plane diagrams identify every policy, storage location, route, and external dependency.
- The proposal distinguishes platform, management, gateway, catalog, model, and connector functions.
- Asset governance covers models, prompts, tools, connections, indexes, policies, and versions.
- Traffic governance demonstrates identity propagation, authorization, routing, limits, budgets, filtering, and usage evidence.
- Security responsibilities are assigned across provider, cloud operator, customer, and connected systems.
- A scripted proof of concept tests normal, denied, over-limit, revoked, injected, and outage scenarios.
- Pricing documents state billable units, counting intervals, included features, commitments, environments, overages, support, and renewal terms.
- Operations has named owners, runbooks, escalation paths, review dates, and retirement procedures.
- Unresolved gaps have a risk owner, mitigation, cost, deadline, and acceptance criterion.
Frequently Asked Questions
How many workloads should a platform pilot include?
Use one high-impact workload that exercises your most important controls and one materially different workload that exposes architectural limits. A single happy-path demo can hide problems with tenancy, batch processing, tool actions, or regulated data.
Can platform governance replace our enterprise policies?
No. Product controls can enforce selected rules and produce evidence, but the enterprise still has to define acceptable use, data classification, approval authority, retention, and exception handling. Treat the platform as an enforcement and evidence layer inside that operating model.
What should we do if two vendors pass the same proof of concept?
Compare the residual work: integrations, custom policy code, staffing, migration effort, contract constraints, and cost-model uncertainty. Prefer the option with fewer unowned controls and clearer exit and data-export paths, even if feature counts are similar.
How can we prevent AI platform costs from surprising business teams?
Expose usage by team, application, environment, model, and major asset, then set budget alerts before production launch. Reconcile the platform’s usage records with cloud and model-provider invoices so differences in counting rules are found early.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
