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Choose an enterprise AI data platform by starting with the workload, data, security and operating requirements—not with a product label. First establish whether your existing warehouse, lake, database or search system can meet them. Add a separate store or index only when it solves a defined need, such as low-latency retrieval, semantic search or reducing load on a source system.
Start with the workload and architecture boundary
Before comparing platforms, map what the AI application must do and how data will move through it. A retrieval-augmented generation (RAG) application, a model-training pipeline and an analytics workload can place different demands on storage, freshness, processing and retrieval.
- Consumers: Identify the applications, users or services that need the data.
- Sources and formats: List the source systems and data types involved.
- Freshness: Specify whether data arrives in batches or must be refreshed continuously, and how quickly source changes must become available.
- Performance: Define the latency and concurrency the application needs.
- Lifecycle: Trace how data is ingested, transformed, governed, indexed or otherwise prepared, retrieved, updated and deleted.
Microsoft’s Azure architecture guidance says some designs can access source systems directly, but warns that doing so can create performance, reliability and access challenges. Treat that as a design trade-off, not a universal reason to copy data into another system. Ask whether an existing warehouse, lake, operational database or search service already meets the workload’s requirements; add a component only to address a specific gap.
Separate the platform’s functions before comparing products
“AI data platform” can describe a combination of capabilities that are native to one product, connected through integrations or assembled with custom operations. Determine which approach each candidate takes for the functions your workload needs:
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- Source ingestion and storage
- Transformation and data preparation
- Cataloging, metadata and lineage
- Feature or embedding generation
- Search indexing and retrieval
- Inference-time connections to models and applications
For each required function, ask whether it is built in, supplied by a partner integration or left to your team to operate. This exposes dependencies and implementation work that a product overview may conceal.
Specify retrieval needs for search and RAG
Retrieval is its own design concern. Microsoft’s vector-search guidance describes vector search as a way to find semantically similar data and discusses combining it with full-text search, filters and specialized data types. Those capabilities can be useful, but not every application needs all of them.
Write down the retrieval behavior the application actually requires:
- Search method: Vector or semantic similarity, keyword or full-text search, or a hybrid approach.
- Filtering: Metadata filters and, where required, document- or row-level authorization filters.
- Content preparation: Whether images, audio or video need preprocessing before indexing.
- Freshness: How incremental updates are applied, and what should happen when source data changes or is deleted.
- Reliability and speed: Required read performance and availability, plus whether index aliasing or zero-downtime refresh matters to the application.
A separate vector database is therefore a decision, not a prerequisite for RAG. Keep retrieval in an existing system if it meets the workload’s search, filtering, freshness, performance and governance needs. Consider another index or store when testing identifies a specific unmet requirement, and include the extra integration and operational work in the comparison.
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Make governance, quality and auditability platform requirements
Governance should cover both the data used by AI and the derived assets that support it. Check whether authorized teams can discover approved data, inspect metadata and lineage, understand access, review audit records and apply data-quality rules.
Databricks governance guidance describes data quality dimensions including completeness, accuracy, validity and consistency, as well as catalog, lineage, centralized access management and auditing capabilities. Use these as evaluation prompts rather than assuming that a feature claim demonstrates that a platform fits your policies or processes.
NIST’s Big Data Interoperability Framework, Volume 6: Reference Architecture states: “The System Orchestrator provides the overarching requirements that the system must fulfill, including policy, governance, architecture, resources, and business requirements, as well as monitoring or auditing activities to ensure that the system complies with those requirements.” Apply that system-level view: a platform must fit the organization’s policy, architecture, resources and oversight requirements, not just provide a place to store or search data.
Test authorization through the full retrieval path
Search relevance does not enforce access control. In RAG, a retrieved passage may be added to model context, so the application must ensure that results are permitted for the caller before they reach the model.
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Microsoft’s secure multitenant RAG guidance describes several possible implementation approaches: document tags or sensitivity levels, row-level security in the data platform, security filters in Azure AI Search, or custom controls. Which approach is appropriate depends on the architecture; verify that it enforces the organization’s actual authorization rules.
Test the complete path with representative identities and data, including users with different roles, differently classified documents, revoked permissions and multiple tenants when relevant. Confirm that both retrieved passages and downstream model context respect the caller’s permissions, and that access can be audited. Microsoft’s AI data guidance also recommends treating vector indexes as sensitive stores, with protections such as encryption, access controls, private networking and monitoring. Check how source changes and deletions propagate to derived embeddings and indexes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare interoperability, operations and dependency
Evaluate how each candidate connects to the systems your organization already uses: data sources, identity systems, query engines, orchestration tools and model services. Consider whether interfaces are open, integrations are validated for your intended use, and data and metadata can be exported or moved if a component is replaced.
Microsoft’s architecture principles emphasize open interfaces as a way to support interoperability and avoid dependence on a single vendor. Azure Databricks documentation describes validated integrations for ingestion, preparation, BI and machine learning, and says Partner Connect supports trials of selected partner solutions. Vendor-validated integration information can help establish what is supported; it is not independent quality certification.
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For designs spanning teams, clouds or organizations, NIST’s Cloud Federation Reference Architecture, published February 13, 2020, provides a framework for thinking about deployment and governance options, trust, security, and resource sharing and use. In your own evaluation, check how identities, policies, data movement and operational responsibilities work across those boundaries.
Run a representative proof of concept
Use the same realistic data, query patterns, permissions and refresh cycle for each shortlisted option. Measure the complete application path rather than one isolated feature. Set acceptance thresholds from business and risk requirements: the consulted architecture guidance does not establish universal benchmark thresholds or a neutral ranking.
- Build the test around the target workload. Include representative source data, expected user requests, realistic concurrency and the refresh behavior the application will use.
- Use real authorization rules. Test role differences, sensitive data, revocations and tenant separation where applicable; retain evidence of access decisions and audit behavior.
- Evaluate the same measures for every candidate.
- Relevance and completeness of retrieved context for target tasks
- End-to-end latency and concurrency under expected load
- Refresh and deletion behavior, including how quickly source changes appear
- Authorization correctness and audit evidence
- Availability and recovery behavior
- Integration effort and operational workload
- Cost at the intended usage pattern, including storage, compute, indexing, network transfer and separate services
- Record trade-offs and gaps. Note which requirements are met natively, through integrations or with custom work, and identify dependencies that would affect ongoing operations or replacement.
Compare results against your own thresholds and priorities. The available guidance does not provide a neutral benchmark, standard pass mark or current comparative price table.
Use a weighted shortlist, not a universal ranking
Score candidates against the requirements that matter to the workload, then weight each criterion according to sensitivity, regulatory context and existing systems. A useful comparison set is:
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- Storage and processing model
- Vector, full-text, hybrid and filtered retrieval
- Data freshness and application latency
- Governance, lineage, data quality and audit
- Security and identity integration
- Interoperability, export and exit options
- Resilience and operational requirements
- Implementation complexity and total cost at expected scale
The evidence available here does not establish current comparative pricing, contractual terms, regional availability, independent performance results or a single best platform. Treat those as items to verify directly for your shortlisted products and deployment context.
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