A data platform for AI workloads is chosen from a list of workloads, not from a feature table. Write down what will actually run on it over the next twelve months: training and fine-tuning, batch inference, real-time inference, vector retrieval for RAG, feature serving. Each profile carries a requirement that a warehouse-versus-lakehouse comparison never reaches: training sets you can rebuild, index latency on the inference path, one feature definition shared between training and serving, and an audit trail that covers models and not only tables. Microsoft’s Well-Architected guidance for AI data platforms states the governing rule: before adding a component, establish that you need it, because simpler architectures ship sooner and cost less. That discipline is hard when the market is loud. Pan, Wang and Li counted more than 20 commercial vector database systems in their 2023 Survey of Vector Database Management Systems, all created within the previous five years.
What a Data Platform for AI Workloads Actually Has to Do
A data platform for AI workloads is the system that keeps data fit for training, serving and auditing models, which is a bigger job than storing and querying tables well. Search results for “AI data platform” are dominated by storage and hardware vendors answering a question about throughput, capacity and proximity to GPUs. That is an infrastructure answer to an architecture question.
This article evaluates data platforms against the AI workloads that will actually run on them: training and fine-tuning, batch and real-time inference, vector retrieval for RAG, feature serving, and model governance. It does not compare two named vendors, it does not compare two storage architectures, and it does not decide who builds the platform. If the field is already down to two products, the head-to-head verdict lives in Databricks vs Snowflake. The data lakehouse vs data warehouse question settles how you store and query tables; AI workloads add four things neither answer covers: a retrieval index, a feature layer, a model registry, and a catalog reaching across all of them. Readiness is a property of your data; this is about the system that has to serve it.
Five Workload Profiles That Set the Requirements
Requirements come from workloads, not from product categories. Five profiles cover most enterprise estates, and each one sets a requirement that survives every shortlist.
Then apply the rule no vendor comparison will hand you: ask whether you need another component at all. If the store that already holds your source data supports vector search and filtering at your volume, a separate engine adds movement, another failure mode and another bill, not capability. The Microsoft guidance and the AWS Machine Learning Lens, written independently, converge on the same concerns, which suggests the criteria are not an artifact of one ecosystem.
Most organizations still finish with more than one technology: no single engine is best at training, low-latency serving and retrieval at once. The target is not one platform. It is the smallest set of components that carries the workloads you can name.
Training and Fine-Tuning: Reproducibility Beats Raw Throughput
Training puts requirements on a platform that no benchmark reports. Data versioning comes first: without it, teams copy datasets to preserve a snapshot, and six months later nobody knows which copy produced the model in production. Then lineage from source to training set, lifecycle management for intermediate data (preprocessed copies accumulate faster than anyone deletes them), and separation of development from production with access control that holds in both.
One decision underneath that separation shapes the security design, and few teams make it deliberately. In model deployment, the model trains on production data during development and the artifact is promoted: better fit, much harder security, because development touches real data. In code deployment, training runs on non-production data and meets real data only after release: simpler security, higher compute cost. Decide which pattern applies before scoring candidates, since it sets the isolation and access requirements you are scoring against.
One warning from the same source: do not select a production platform on the strength of its notebook support. Notebooks are an exploration tool and the part of a demo that data scientists respond to most, which distorts selection. ELT stays the sensible default on ingestion, since raw data serves use cases nobody has specified yet.
Retrieval and Vectors: Treat the Index as a Production System
The search index that grounds a RAG application sits on the same request path as the model endpoint, so its reliability target has to match the endpoint’s. Teams design the endpoint for high availability and leave the index as a side store on a nightly job. A stale index does not fail loudly: the application answers confidently from the wrong context.
Three further requirements each eliminate candidates. Vector support alone is not enough: usefulness comes from hybrid retrieval, vector similarity plus full-text matching and structured filters, so a query can be restricted to one region, contract type or date range. Freshness has to be built in, since an index without incremental refresh means you maintain change detection yourself. Permissions have to survive the trip to retrieval, because access control that stops at the source system does not exist in RAG, where the index is what the model reads.
Embeddings are sensitive data, which the market rarely mentions. The risks named in the Microsoft guidance are embedding inversion (reconstructing source text from vectors), poisoning of the index, and unauthorized access to the vector store or its backups. A vector index falls under the same encryption, access and deletion rules as any other production store, erasure path included. With more than 20 commercial systems launched in five years, the useful discipline is subtraction: adopt a dedicated engine when scale, latency or hybrid search force it, otherwise use the vector search your platform already provides.
Feature Serving: Where Training and Inference Have to Agree
A feature store sits between the aggregated data store and the training environment: a catalog of curated features with metadata such as lineage, generation time and source, holding golden training data more than one model can reuse. The criterion is whether a platform provides that layer natively or your team writes it, in which case the cost belongs on the scorecard.
The requirement underneath is consistency. The same feature definition has to apply offline during training and online during serving. When the training pipeline computes a 30-day rolling average one way and the serving path computes it another, the model passes evaluation and disappoints in production, and the gap is hard to find because both numbers look plausible. Ask how a candidate guarantees that a feature is computed once and read twice, rather than defined twice.
A feature store holds sensitive data, often its most refined version, so it needs its own access control and audit trail, not whatever the lake had. A managed offering, such as the Databricks feature store or the SageMaker Feature Store, earns its overhead once features are shared across models.
Governance That Covers Models, Not Just Tables
Ask one catalog to cover data, features, models and indexes. Most estates end up with three registries stitched together by a spreadsheet, which works until somebody has to prove what happened. The requirements are an audit trail of data access, lineage from source to prediction, a metadata catalog, model versioning and a registry, and residency controls covering the index and the feature store as well as the tables. A unified governance layer for data and AI assets is one implementation; the point is coverage, not the product.
The NIST AI Risk Management Framework is a useful reference for what a platform has to answer. Its four functions, GOVERN, MAP, MEASURE and MANAGE, assume you can say what data trained a model, what the model does now, and who changed it. The framework is voluntary and non-regulatory, so treat it as a checklist for evidence, not a legal obligation. One control question separates candidates quickly: if a regulator asks which data produced this prediction, does the platform answer, or does a person spend a week reconstructing it?
A Selection Scorecard You Can Apply to Any Two Candidates
Choosing a data platform for AI workloads comes down to eight questions built from requirements rather than products, which makes the scorecard portable to candidates this article never mentions. Score each candidate against the workloads you listed at the start, and record answers, not impressions.
- Which of the five workload profiles will we actually run within twelve months?
- How many components does this platform remove, and how many does it add?
- Is training reproducible without anyone copying datasets by hand?
- Does the retrieval index meet the availability target of the inference endpoint?
- Do features have one definition offline and online?
- Does the catalog cover models, or only tables?
- Can the data leave in an open table format?
- What exactly is metered, and which component will surprise us on the invoice?
Question two compounds, and teams skip it: a platform that removes a separate index, a separate feature layer and a bespoke lineage service beats one scoring marginally higher on each in isolation. Answer question eight with a finance colleague present, since metering models differ far more than feature lists do.
A set of questions settles the choice; it does not deliver the platform. DS Stream builds and runs these platforms in production, most often on Databricks for production-scale AI, with the surrounding pipeline and platform operations work covered by our data engineering services. If your shortlist and your workload list do not line up yet, talk to our data platform team.
Frequently Asked Questions
What is a data platform for AI workloads?
A data platform for AI workloads is the system that keeps enterprise data fit for training, serving and auditing models, rather than only for storing and querying tables. It combines an aggregated data store, a processing layer, a retrieval index, a feature layer and governance covering models as well as data. Which parts you need is decided by the workloads you will run.
Do you need a separate vector database for RAG?
Not by default. If the store holding your source data supports vector search with filtering at your volume, a separate engine adds data movement and cost, not capability. A dedicated engine earns its place when scale, latency targets or hybrid search features exceed what the platform already does.
Is a lakehouse enough for AI workloads?
A lakehouse is a storage and query layer, so it answers part of the question. AI workloads add a retrieval index for grounding, a feature layer shared by training and serving, and a catalog covering models. The lakehouse and warehouse comparison decides how tables are stored, not those three additions.
Do you need a feature store?
You need one when the same features feed more than one model, or when training happens offline and serving online, where the two definitions can drift apart. For a single batch model whose features are computed in one pipeline, it is a layer without a job.
.webp)
