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Choose a cloud data warehouse, an AI data platform, or a hybrid by scoring the workloads you run, then comparing governance, interfaces, operating effort, and cost on the same data. A warehouse is the usual fit when governed SQL, business intelligence, relational modeling, scheduled transformations, and broad analyst access dominate. An AI data platform is the stronger fit when training, inference, feature management, streaming, unstructured or vector data, and production model operations are central. Keep both when those groups need different performance, tools, or owners, and only if lineage, policy, and cost allocation stay explicit across the split.
The labels overlap, so judge each candidate’s capabilities and the services your team must run. No single published price or performance figure decides this for every organization. The comparison below is the decision method, and the table under it is the first filter.
At a glance
| Name | Best for | Key spec | Checked |
|---|---|---|---|
| Cloud data warehouse | Governed SQL analytics, BI, relational modeling, and a broad analyst population | ELT, scheduled transformations, governed sharing, and a smaller platform surface | Nov 2026 |
| AI data platform | Production training and inference, features, vectors, streaming, and unstructured data | Feature management, model development, orchestration, and model serving on governed data | Nov 2026 |
| Hybrid | BI and machine learning that need different performance, tooling, or owners | Explicit lineage, synchronization, policy boundaries, data ownership, and cost allocation | Nov 2026 |
Read the rows as patterns to test in that order. Keep the row whose “Best for” cell matches the work that dominates the next 12 to 36 months. A hybrid is a third design with its own operating cost, which earns a place only after the first two fail to cover both jobs cleanly.
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A cloud data warehouse is built to run governed SQL for many people: dashboards, ad-hoc analysis, relational models, and curated tables with stable access rules. An AI data platform keeps that governed-data job and extends it into machine-learning and AI workflows such as feature management, model development, orchestration, streaming, unstructured-data processing, and model serving.
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The boundary is blurry in product marketing. A warehouse may offer notebooks and model hooks. An AI platform may offer a SQL endpoint that analysts like. Compare the surface you will actually use: the SQL endpoint, the pipeline runner, the notebook or job, the feature store, the vector index, and the serving endpoint. A feature that exists only in a slide, or only in a separate companion product you have not priced, does not count as native.
Scope is the practical difference. A warehouse-first estate tries to stay narrow: storage, a SQL engine, ingestion, transformation, access policy, and business-intelligence (BI) tools. An AI-oriented estate accepts more services because the path from raw data to a served model is part of the platform. Unification can remove handoffs between an analytics team and a separate machine-learning stack. It also adds runtimes, policies, and on-call coverage. Write down every service someone must patch, secure, monitor, and support before you treat a single vendor as a simpler estate.
| Question | Cloud data warehouse | AI data platform |
|---|---|---|
| Primary job | Governed SQL analytics and BI for a wide analyst group | Governed data plus production machine-learning and AI workflows |
| Data that must be first-class | Relational and semi-structured tables used in metrics and reports | Those tables, plus events, documents, images, audio, features, vectors, and model artifacts |
| Common processing habit | Transform into modeled tables, often extract-load-transform (ELT) on a lake-style store | ELT for tables, plus extract-and-load of raw media for later extraction, and pipelines that build features and models |
| Latency that usually decides the deal | Dashboard freshness and interactive query time under analyst concurrency | Those, plus feature lookup, retrieval, and online or batch inference |
| Who feels the operating load | Analytics engineering, BI, and data platform staff | Those roles plus machine-learning engineering, platform engineering, and production on-call for serving |
Databricks’ own comparison guidance tells buyers to judge governance depth, AI readiness, support for more than one kind of user, and cloud or hybrid compatibility before committing to a vendor. That is a capability test, which applies to Databricks and to every warehouse or platform on the shortlist. Microsoft’s architecture guidance makes the same demand in different words: compare options and design an end-to-end solution, which you can read in Microsoft’s guide to AI and machine learning products.
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Open a vendor evaluation after you can name the work, the controls that can knock a product out, and the people who will operate the winner. A meeting without those three items becomes a feature tour.
- A workload inventory that separates BI and SQL, ELT, streaming, batch training, feature generation, vector retrieval, online inference, and batch inference, each with freshness, latency, concurrency, and failure targets.
- Decision weights and non-negotiable controls, written down before the first demonstration and left unchanged when a demo is impressive.
- Rights to a de-identified slice of production-shaped data, including realistic skew, file sizes, schemas, and unstructured content where that content is in scope.
- Named owners for security, finance, residency, and the pager that will fire when a pipeline or endpoint fails.
- A one-page sketch of sources, transforms, BI tools, and any training or serving path you already run.
- Low, expected, and peak utilization pictures covering the next 12 to 36 months, even if the peak figure is a reasoned ceiling.
Microsoft’s analytics comparison guidance tells architects to weigh price beside source and format support, engines and APIs, network isolation, regional availability, governance boundaries, operational control, and skills the organization already has. Put those axes on the pre-read. Familiarity with a SQL dialect is relevant, and it does not override a failed residency or key-management gate.
Treat non-negotiables as pass or fail. Common gates are data residency, private networking, customer-managed encryption keys, row and column controls, masking, audit retention, and a tested export of core tables. A candidate that misses a gate leaves the shortlist while the demo is still on the screen.
Which workloads belong on the comparison matrix?
Split the estate into separate rows, then fill every row with volume, freshness, the median latency (p50), the 95th-percentile latency (p95), concurrency, and retry or failure behavior. A single “analytics and AI” score hides the row that will hurt in production.
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Interactive SQL and online serving are different contracts. A platform can lead the dashboard row and still need another system for a low-latency feature read or a model response. An AI platform reduces integration work when those functions are native and they meet the target you wrote down. A separate service that meets the target beats a native service that misses it. Score the outcome, then note whether you had to add a component to get there.
| Workload row | What to record | What the result tells you |
|---|---|---|
| Dashboards | Refresh allowance, freshness, concurrency at peak business hours | This is the warehouse’s home row. If the AI platform misses it, plan to keep a warehouse or reject that platform for BI. |
| Ad-hoc SQL | p50 and p95, concurrency, SQL features analysts already use | Dialect gaps become rewritten queries, extra BI connectors, and training time. Those hours belong in the cost model. |
| ELT | Batch volume, schema-change rate, completion time, retry behavior | Extract, load, then transform fits lakes and lakehouses. Confirm schema evolution, retention, and time travel on the loaded raw data. |
| Streaming ingestion | Event rate, acceptable lag, replay, late-arriving data | Micro-batch SQL can look like streaming and still miss the freshness target. Streaming also adds its own compute line. |
| Batch training | Data volume, time allowed, accelerator need, rerun from a known snapshot | Acceleration, raw-data retention, and reproducibility decide this row. A sample of warehouse aggregates is a weak stand-in for the real training set. |
| Feature generation | Feature count, freshness, point-in-time rule, backfill window | A feature must be computed from data that was knowable at that time. A join that accidentally sees future rows will look accurate and still be invalid. |
| Vector search | Corpus size, embedding setup you standardized, quality target, query latency, rebuild time | Index build, storage, and refresh are cost lines. The index has to enforce the same row filters as the source table. |
| Retrieval-augmented generation | Document and media types, who owns chunking, retrieval latency, lineage from answer to source file | This follows the extract-and-load pattern: store the file, extract later. Governance includes the path back to the file, which is a control. |
| Online inference | p50 and p95, availability, feature-lookup time inside that budget | Score serving on its own. A win on dashboards says nothing about this row. |
| Batch inference | Throughput, completion window, cost per run, model version, input snapshot | Batch scoring often fits warehouse-style compute. Still bind each run to a model artifact and a data snapshot. |
Keep feature generation and online inference on separate rows. Feature generation computes a value as of a moment in time. Online inference returns a decision inside a latency budget, often by reading a feature that was computed earlier. A warehouse query that joins raw tables on each request can satisfy a prototype and miss p95 once real concurrency shows up. Record the compute path and the read path.
Write the failure rule in operational language before you test. A dashboard may slip one refresh interval and then must alert. A stream may replay from a checkpoint. A training job may restart from a checkpoint. An online request may fail alone, leaving the rest of the traffic up. The platform that shows those behaviors under your real identity policy is the one you can run. If a row is outside the 12- to 36-month horizon, mark it excluded and say so in the decision record. An excluded row is an explicit scope choice. A blank cell is a hole.
How do ETL, ELT, and EL change the platform decision?
Microsoft’s architecture guidance distinguishes three processing patterns. The pattern your dominant data follows is a filter on platform shape, which is separate from the vendor’s category name.
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Extract, transform, load (ETL) changes data before it lands in the analytical store. Traditional warehouses grew up on this pattern: a pipeline applies business rules, then modeled tables arrive ready for SQL. It fits stable metrics, well-known schemas, and teams that want analysts to see curated tables. It is a weak default when models must be rebuilt from an earlier, unmodified source, because the raw input may never have been kept.
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Extract, load, transform (ELT) lands raw data first, then transforms it inside the lake, lakehouse, or warehouse. A lakehouse, in this comparison, means warehouse-style tables and governance on files that stay in open storage. ELT is the common pattern for lakes and lakehouses. It favors engines that can query raw and curated data, evolve schemas, and keep time travel or retention long enough to rerun a transform. Confirm that downstream tools can read the stored files through an open format, which is a lock-in test as much as a performance test.
Extract and load (EL) stores documents or media first and extracts structure later. Microsoft describes this pattern as common in generative AI and retrieval work. The platform has to accept large files, retain them under the same residency and access rules as tables, and support the later extraction, chunking, embedding, and index build. A warehouse that can store a binary object and cannot govern or process it at the volume you expect will push you into a second system. An AI platform that can store the file and cannot show lineage from a retrieved passage back to that file will fail a different gate.
Preserve raw data when that preservation makes model preparation and results reproducible. Ask where the original files live, how long they live, which identities can read them, and whether a training or inference run can be tied to that snapshot. Time travel and retention settings are part of the answer. A cheaper transform-only copy that discards the source can be the wrong economy if you may have to explain or rebuild a model.
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Which security and governance controls have to cover AI assets?
Every control has to cover the assets you will trust in production: tables, training sets, features, vector indexes, and model artifacts. A policy that stops at curated SQL tables leaves the AI path ungoverned.
Compare these controls on each shortlisted product, and test them with the identities your staff will actually use:
- Single sign-on and integration with your identity and access management (IAM) system.
- Fine-grained access, including row policies and column policies.
- Masking of sensitive values, including when the same person reaches the data from SQL and from a job.
- Encryption in transit and at rest, plus who holds the keys.
- Private networking and network isolation, so the test matches the path production will use.
- Audit logs that show who changed a grant, a pipeline, a feature definition, and a served model.
- Lineage and catalog integration across tables and AI assets.
- Residency: the region where data, indexes, logs, and model artifacts sit.
- Separation of duties among people who administer data, promote models, and operate serving.
Run the same restricted principal through every surface: notebooks, SQL endpoints, pipelines, feature stores, vector indexes, and model-serving endpoints. A row filter that works in a BI tool and disappears in a notebook is a failed gate. A vector index built from a table the user cannot read is a failed gate. A serving endpoint that returns a prediction from a row the caller cannot select is a failed gate. Write the identity, the surface, and the expected deny or mask into the test log.
Databricks’ platform comparison guidance says: “Organizations should evaluate platforms using structured criteria including governance depth, AI readiness, multi-persona support, and cloud or hybrid compatibility before committing to a vendor.” The same guidance highlights unified governance and semantics, automation, open-format support, scalable compute, and end-to-end lineage. Use that list as a checklist for every finalist. Multi-persona support means an analyst, an analytics engineer, a data scientist, a machine-learning engineer, and a security reviewer can each work under their own rights. One shared administrative login during a proof of concept hides the result you need.
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Catalogs, lineage tools, and cloud-cost controls often sit beside the platform. Score them as part of the control plane when they enforce grants or explain spend. A catalog that only describes data, while the engine enforces a different policy, is documentation. Adjacent tools do not repair a platform that fails a gate on its own surfaces.
How do open table formats and interfaces change the cost of leaving?
Lock-in is a set of exit tasks: move tables, metadata, policies, features, and model artifacts, then point BI tools and pipelines at the new home. Open file and table formats lower the table-moving part of that bill. They do nothing, by themselves, for policies, feature definitions, and model registries.
Check relational tables, nested and semi-structured data, events, documents, images, audio, schema evolution, retention, time travel, and bulk import and export. Then check the formats and interfaces around them: Delta Lake, Apache Iceberg, Parquet, the SQL dialect, APIs, connectors, catalog integrations, model registries, vector interfaces, export speed, deletion, and egress. Databricks documentation describes Delta Lake and Apache Iceberg as open formats intended to reduce lock-in. Verify the claim on your data. A downstream engine should query the files through that open format, with no proprietary rewrite step in the middle.
SQL dialect differences show up late, during the BI test. Connect the same dashboard tool you use in production, run the same representative queries, and list every function you had to rewrite. Rewrites are engineering hours, and they are also a preview of migration cost. Orchestration is the same kind of test: a pipeline that only runs inside one vendor’s scheduler needs a rewrite if you leave. A pipeline that submits ordinary jobs through a documented API is easier to move, and you still have to time the export.
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Require a written exit path before you assign a cost score. The path should say how core tables, metadata, policies, feature definitions, vector indexes, and model artifacts leave, how long a full export took on the proof-of-concept slice, and how deletion is confirmed. A platform with a low run rate and a blocked exit carries the switching cost inside the decision model. If the vendor will not allow an egress test, record exit as unproven and keep that candidate behind any finalist that completed the test.
Hosted platforms also have a scope boundary worth reading before you assume a format or a governance feature is included. Microsoft publishes that kind of boundary for one hosted platform in its page on the scope of the Databricks platform on Azure. Ask every vendor for the equivalent document: what they run, what you run, and which open format you can read if the managed service is turned off.
How do I compare performance without a single published benchmark?
There is no authoritative, controlled cross-vendor performance or price figure for this comparison. Vendor and cloud architecture material, as reviewed for this decision guide and current as of November 2026, explains criteria and architecture. It does not crown a universal winner. Use your data, and keep the method attached to every number you brief.
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Attach four facts to every figure: data shape, software version, region, and method. Data shape means row counts, skew, file sizes, and the share of unstructured content. Version means the engine build, the open-table format version, and the model or embedding revision if the row is a machine-learning test. Region matters because capacity, accelerators, and network paths differ. Method means concurrency, cache state, and whether the run was cold or warm. A number without those four facts is a demo anecdote.
Compare candidates on the same slice, the same questions, and the same success rule. Change one variable at a time when a result looks anomalous. If you cannot rerun a figure, leave it out of the score. Publish exclusions beside the chart so a missing streaming test is visible as scope, which keeps it from being read as a win.
What costs go into a 12- to 36-month model?
Price the work, then the contract. A list price is a poor benchmark across vendors because packaging, regions, and commitment terms change, and because two platforms meter different things. As of November 2026, ask each vendor for a current quote for your region, support tier, and term, and build the model from that quote plus the consumption you observed.
Include every line that will appear once the workload is real:
- Storage of raw files, curated tables, features, vector indexes, and model artifacts, including retention and time travel.
- Compute for queries, transforms, and tasks, including how concurrency pushes the warehouse or cluster size.
- Streaming ingestion and any always-on capacity it requires.
- Orchestration and job scheduling.
- Acceleration for training or large retrieval builds.
- Data movement and egress, both inside the proof of concept and in the exit plan.
- Observability: logs, metrics, and tracing for pipelines and endpoints.
- Support tier.
- Reserved capacity or other commitments, including what you still pay in the low-utilization case.
- Vector-index build and storage, and model-serving capacity.
- Engineering labor: hours to build the test, rewrite SQL, stand up policy, and cover on-call for the extra services.
Run the model at low, expected, and peak utilization over 12, 24, and 36 months. Peak shows whether concurrency or accelerators dominate. Low shows whether a commitment becomes the main cost when the project slips. Expected is the case you will steer toward, and it is the wrong case to use alone.
Microsoft’s analytics guidance pairs this price work with source and format support, engines and APIs, network isolation, regional availability, governance boundaries, operational control, and existing skills. A cheap engine in a region you cannot use, or a cheap engine your team cannot secure, is a failed option with a flattering quote. Put the switching cost from the exit test into the same spreadsheet so a low meter rate cannot hide a trapped estate.
When is a cloud data warehouse the better primary system?
Start with a warehouse when governed SQL and BI are the work you must do well, relational data is the core, many analysts need self-service, and you want a mature operating model with a limited platform surface. That pattern covers dashboards, ad-hoc SQL, ELT into modeled tables, scheduled transformations, and governed sharing.
The warehouse remains the primary system when machine learning is occasional exploration on curated tables, batch scoring can run as SQL or a scheduled job, and you do not yet have a production requirement for feature lookup, vector search, or online inference. In that estate, adding a full AI platform widens the surface before the workload needs it. Keep the raw data you may need later, so a future training set can be rebuilt, and keep export drills current so the later move is possible.
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A warehouse-first choice still has to pass the rows you marked in scope. If analysts need semi-structured data, the warehouse has to handle nested types and schema change. If a partner receives governed shares, the sharing control has to match your row and column policy. If the honest roadmap puts online inference inside the decision window, score that row now. Discovering the gap after the purchase is how teams end up with an unplanned hybrid.
When is an AI data platform the better primary system?
Prefer an AI data platform when training and inference are production workloads, feature or vector services matter, unstructured or multimodal data is a real share of the estate, and one orchestration path from data to model removes duplicated tools. The gain is fewer handoffs. The cost is a broader set of services and skills.
This pattern fits when several matrix rows are live together: streaming or frequent ELT, feature generation with point-in-time rules, a vector index or document store, and an inference path with its own latency budget. Native support helps when the same identity policy, lineage, and catalog cover those rows. If you would otherwise run a warehouse, a separate feature store, a separate vector database, a separate experiment tracker, and a separate serving cluster, count those contracts, identity integrations, and failure points on the warehouse-plus-addons side before you call the warehouse simpler.
The AI platform still has to win the original analytics job or sit beside something that does. Analyst concurrency, SQL dialect, and BI connectors are part of its score. A platform chosen for notebooks and weak on governed self-service will push analysts back to exports, which recreates the split you hoped to close. Read AI readiness as the ability to run your rows under your controls, which matches the evaluation criteria Databricks publishes, and it is a standard you should apply to every vendor.
When is a hybrid design justified?
A hybrid is justified when governed BI and machine learning differ in performance, tooling, or ownership, and a single platform would force one of those groups into a poor fit. Finance-grade reporting may belong on a warehouse with a strict residency and a large analyst population, while a product team needs streaming features and online inference on a different cadence. Those are different operating problems.
Write five rules before you split the estate. Lineage has to cross the boundary, so a metric and a model can be traced to the same source snapshot. Synchronization has to name the dataset, the freshness, and which system is the source of truth. Policy boundaries have to say which engine enforces grants, and they have to match. Data ownership has to name the group that approves access and retention. Cost allocation has to say which budget pays for storage, movement, and the duplicate copy.
A hybrid that copies data and also copies a different, weaker set of controls has added risk. If you cannot staff both sides, the split is premature. If both workloads pass on one platform, the second platform is optional complexity. Revisit the split when the 12- to 36-month model shows the integration labor exceeding the benefit that caused the split.
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How do I run a proof of concept that every vendor has to pass?
You need the frozen gates, the de-identified production-shaped slice, the workload matrix, and a quote request that uses the same region and support assumptions for every vendor. Then every candidate runs the same script. A proof of concept that each vendor designs for itself cannot be scored together.
- Set decision weights and non-negotiable controls before any demonstration. Store the document. If a stakeholder wants a new weight after seeing a demo, record the change as a new decision with a date, and rerun the affected tests.
- Load one representative, de-identified slice with the skew, file sizes, schemas, and unstructured content you listed. Keep a manifest of object counts and sensitive-field handling so a later audit can see what entered the tenant.
- Replay SQL, ELT, streaming, training, feature, vector or retrieval, and inference workloads at the concurrency you set. Use your BI tool for the SQL rows. Use your target freshness for streaming. Use a point-in-time check for features. Use your p50 and p95 budgets for online inference.
- Apply real IAM, masking, lineage, retention, residency, and audit requirements on each surface. Include a user who must be denied, a user who must see masked values, and an administrator whose actions must appear in the audit log.
- Record latency, throughput, freshness, reliability, recovery, engineering hours, and consumption cost. Recovery means the time to restore the pipeline or endpoint after a fault you actually trigger, such as a killed job or a revoked credential.
- Repeat at low, expected, and peak utilization. Write the software version, region, assumptions, and exclusions on the same page as the numbers. Peak should include the noisy-neighbor pair: heavy batch beside interactive users.
- Score with the weighted matrix and publish an exit plan for data, metadata, policies, features, and models. A candidate with missing exit evidence stays incomplete.
Use the same clock and the same success rule. Warm-up runs are allowed when you label them as warm-up and exclude them from the score. Cold runs matter for dashboards that fire after an idle period and for endpoints that scale from zero. If a vendor engineer tunes the estate during the test, write the tuning into the method. A result that requires an engineer you will not have in production belongs in the labor line.
Trigger at least one failure on purpose. Stop a load, expire a credential, and confirm the retry behavior you specified at the start. Error rate and recovery time are scored fields. A platform that only shows the happy path has not finished the test.
What has to be in the exit plan?
The exit plan is a deliverable of the comparison. It names the objects that leave, the format they leave in, the time a full export took on the test slice, the policy objects you must recreate, and the deletion proof you will accept.
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Time the export and the delete. Export speed on a toy table will mislead you when the real estate is wide files plus a vector index. Deletion has to include derived copies: indexes, checkpoints, logs, and backups the vendor retains. Put the hours and any egress fee into the 12- to 36-month model. A cheap platform with a slow or partial exit is more expensive on the day you need to leave.
How do I match the platform to the team that will run it?
Map the winner to the team that will hold the pager, then check that this team can cover every service you just scored. A warehouse-first design usually fits an analytics-engineering group with mature BI practice: modeled tables, reviews of SQL changes, and a known incident path for late dashboards. A broader AI platform usually needs machine-learning engineering, platform engineering, MLOps practice, and on-call coverage for training jobs and serving endpoints.
Count the surface in units a manager can staff. List services, policy engines, runtimes, and interfaces. For each one, name who patches it, who reviews access, who watches freshness and error rate, and who answers when inference latency slips. If a name is missing, the operating model is incomplete, and the cost model is missing labor. Existing skills are a Microsoft comparison axis because a platform the team cannot secure will fail in ordinary weeks.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Multi-persona support is the same question from the user’s side. Analysts need governed SQL and a catalog they can search. Analytics engineers need transforms, tests, and deployments. Data scientists need reproducible runs against permitted data. Machine-learning engineers need registries, features, and endpoints. Security needs audit and separation of duties. A product that serves one of these groups and forces the others through shared admin roles will look fast in a pilot and fragile in production.
How do I turn proof-of-concept notes into a score leadership can audit?
Split the score into gates and weights. Gates are pass or fail, and a fail removes the candidate. Weights apply only to candidates that passed every gate. Set the weights before the demonstrations, make them sum to 100, and store them with the date.
| Criterion | How it scores | Pass evidence to file |
|---|---|---|
| Identity, row and column controls, masking | Pass or fail | Denied user and masked user tested on SQL, jobs, features, vectors, and serving |
| Encryption, keys, private networking, residency | Pass or fail | Region, key owner, and network path match the production constraint |
| Audit, lineage, catalog, separation of duties | Pass or fail | One traced change each for a grant, a pipeline, and a model-related asset |
| Query, pipeline, and concurrency results | Weighted | p50, p95, throughput, freshness, version, region, method |
| Training, feature, retrieval, and inference results | Weighted | Duration or latency against the budget, with snapshot identifiers |
| Reliability | Weighted | Error rate and a triggered recovery, plus noisy-neighbor notes |
| Economics | Weighted | Low, expected, and peak models, including labor, commitments, and egress |
| Interoperability and exit | Weighted | Dialect gaps, connector results, timed export, deletion proof |
| Skills and operating coverage | Weighted | Named owner for every service that must be patched, secured, and monitored |
Publish the assumptions and exclusions on the score page. Leadership should be able to see which rows were out of scope, which figures came from a warm cache, and which quote expires. A weighted total without that page cannot be audited. When a stakeholder challenges a weight, change it in the document, date the change, and recompute. Quiet edits after a favorite vendor trails are how comparisons lose credibility.
What should the finished comparison decide?
The finished comparison names one primary pattern: cloud data warehouse, AI data platform, or hybrid. It shows that every non-negotiable control passed on the surfaces you will use. It shows a cost range for low, expected, and peak utilization across 12 to 36 months, including labor, commitments, serving, vector indexes, and the exit. It lists failed gates and excluded workloads in the same packet as the winner.
Keep version, region, data shape, and method beside every figure. If those items are missing, the packet is still a vendor demonstration. If the winning pattern is a hybrid, the packet also contains the lineage, synchronization, policy, ownership, and cost-allocation rules. Re-quote prices when a contract starts, because packaging and regional rates move, and rerun any row whose software version will differ from the one you scored.
Frequently Asked Questions
What if two platforms tie on the scorecard?
Treat the tie as unfinished when any gate is still marked unknown, and finish that test before you compare totals. When both candidates passed the same gates and the weighted totals match, break the tie with items you left out of the weights: how clearly the contract assists exit, whether support terms match the way your on-call team works, and whether one option matches skills you already run in production. Time-box a follow-up on the single workload that carried the most weight, and leave eliminated vendors off that follow-up.
Should an external data catalog be the system of record?
Put the system of record where grants are actually enforced. An external catalog can govern a hybrid when every engine applies that catalog’s permissions to SQL, jobs, and serving endpoints. When a platform only imports metadata and then enforces a separate native policy, the catalog is a map, and the native controls still need their own tests. Write down which system an auditor would open first during an access review, and use that system in the proof of concept.
How do I compare an on-demand quote with a committed discount?
Turn both quotes into the cost of one successful unit you already defined, such as a finished dashboard refresh, a training run, or a batch scoring window, under the low, expected, and peak pictures. Give unused committed capacity its own line so a lower rate cannot hide spend that continues when volume drops. Ask for a restated quote if one proposal omitted a region, a support tier, or acceleration the other proposal included.
Who should sign off on the comparison besides the data team?
Security signs the gate evidence, finance signs the utilization model, and each sensitive-data owner signs residency, retention, and masking for their domain. The person who will carry the pager for pipeline and serving failures signs the recovery notes. A sponsor signature that never reviewed the exclusions does not approve the figures in the packet.
How do we tear down the proof-of-concept tenant after the decision?
Remove derived assets before raw files: serving endpoints and model artifacts, vector indexes, feature tables, and experiment logs, then source objects, keys, and identity apps that existed only for the test. Get a written vendor confirmation that support tickets, backups, and staff copies used in troubleshooting are deleted or covered by your retention schedule. Keep the scorecard, versions, and region notes with the decision record, and drop the dataset once that record stands on its own.
