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The safest AI-provider choice is not the one with the biggest uptime percentage or the broadest model catalog. Compare each candidate on three service-specific questions: what you can export and reuse, what its uptime commitment actually measures, and how it handles model retirement and support. Then verify data controls, regional processing, contract rights, and migration costs for the exact model and deployment you will run.
A cloud platform may operate the endpoint while another company develops the model. In that arrangement, availability, retention, model terms, and geographic processing can be controlled by different parties. The framework below lets you compare those responsibilities without treating a general portability statement or an SLA credit as a guarantee that your application can move unchanged.
Why a single provider score is misleading
AI services are bundles of assets and obligations, not interchangeable API calls. A provider may let you download source files while keeping model checkpoints, tuning adapters, evaluation history, safety configuration, or orchestration metadata inside a managed service. Another provider may offer a high availability objective for online inference but a different target for training, batch jobs, pipelines, or the control plane. A model developer can announce a retirement date that differs from the date used by the cloud platform hosting that model.
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Make comparisons at the level you will actually buy: service, model ID and version, deployment type, region, contract, and support tier. Record evidence for each item rather than assigning a provider one blended score. The result is a decision record that can survive a vendor change, an outage claim, or a model deprecation.
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Start with an inventory of everything you may need to move
Portability begins before you read an export page. List the assets that make your system work, including items that are easy to overlook. For each asset, ask whether the provider exports it, in which format, through which method, on what schedule, with what limits and fees, and how long it remains available after termination.
| Asset | Questions to document |
|---|---|
| Source and reference data | Can you retrieve the original objects, metadata, permissions, timestamps, and version history? Is the export available through an API, standard tools, or a service-specific procedure? |
| Prompts, outputs, and conversations | Are request and response records retained? Can you export them with identifiers, timestamps, safety annotations, and tenant separation intact? |
| Logs and audit trails | Which operational, security, and billing logs are included? What retention window, pagination limit, format, and post-termination access apply? |
| Evaluation sets and results | Can you retrieve test data, scoring rubrics, labels, human feedback, and historical run results so another provider can be evaluated fairly? |
| Vector indexes and embeddings | Can you rebuild the index from source documents and embedding vectors? Are dimensions, distance metrics, chunking settings, and metadata exportable? |
| Fine-tuning data and artifacts | Can you download training files, checkpoints, adapters, tokenizer settings, hyperparameters, and evaluation artifacts, or only the original data? |
| Customer-trained or adapted models | Is a portable model file provided, or is the artifact usable only through the provider’s serving stack? Check license, format, and hardware assumptions. |
| Orchestration and configuration | Can you export prompts, tools, routing rules, agents, schemas, safety settings, secrets references, quotas, and deployment manifests as usable configuration? |
| Application code and integration | Which SDK calls, authentication flows, response schemas, streaming behavior, and error codes are provider-specific and must be rewritten? |
An exportable file is not automatically a transferable capability. A vector index may depend on a proprietary tokenizer; a fine-tuned adapter may require a particular base model; and an agent definition may omit hidden safety or routing defaults. Treat portability as the ability to recreate an equivalent workload, not merely download bytes.
Read service-level export documentation
Google Cloud’s portability documentation illustrates why a universal “export everything” promise is unreliable. It describes different methods, formats, and limitations by service and includes an exit process for named storage and data-management services. Some data is downloaded through APIs or standard tools; Filestore, for example, is copied from a mounted share rather than through one universal export action. That guidance does not establish that every AI asset or model is exportable.
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Test portability before you sign
Documentation describes a possible path; a representative export shows whether your team can use it within an acceptable time and budget. Run a controlled exercise with production-shaped data and an isolated destination.
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- Freeze an inventory. Record asset names, owners, sensitivity, size, retention requirement, dependencies, and the business process each asset supports.
- Request the formal method. Capture the API or console path, accepted formats, authentication requirements, rate limits, maximum object or archive size, region restrictions, egress charges, and support contact.
- Export a representative sample. Include large objects, multilingual data, metadata, deleted records, logs, evaluation labels, and the model configuration needed to interpret them.
- Validate integrity. Compare counts, checksums, timestamps, access controls, encoding, and relationships with the source. Confirm that pagination and retries do not silently omit records.
- Rebuild a minimal workload. Load the export into a neutral storage or serving environment and reproduce retrieval, prompting, evaluation, and audit steps. Record any manual transformations.
- Measure the exit. Time the extraction, transformation, transfer, validation, and cutover. Price storage, network transfer, engineering labor, parallel operation, and contractual assistance.
- Document deletion. Ask when the provider deletes primary and backup copies after termination, how legal holds affect deletion, and what evidence of deletion is supplied.
Repeat the exercise after major platform changes. An export that worked for one model or deployment type does not prove that another model, region, or managed feature has the same path.
How to read an uptime SLA
An SLA percentage is meaningful only with its scope and measurement method. Before comparing numbers, capture the following fields for each covered service.
- Operation: inference, training, batch prediction, pipelines, deployment, or control-plane API.
- Measurement window: calendar month, rolling period, or another interval.
- Denominator and error rule: requests, server-side error rate, five-minute intervals, or another calculation.
- Regional scope: one region, a regional average, or a multi-region service.
- Exclusions: maintenance, customer configuration, quota exhaustion, dependencies, force majeure, network conditions, or beta features.
- Evidence and claim process: logs required, deadline for filing, eligible account, and submission route.
- Remedy: service credit, refund, or another contractual remedy. A credit is not reimbursement for every business loss.
Do not call an SLA a guarantee against downtime. It applies only to the covered service under the stated conditions, and a credit may be the sole remedy.
Vertex AI example: different targets inside one platform
Google Cloud’s current Vertex AI SLA lists at least 99.9% monthly uptime objectives for specified training, deployment, and batch-prediction services. The same document lists at least 99.5% for certain custom online-prediction and pipeline services and at least 99% for the training-cluster control-plane API. These are separate categories with their own definitions and exclusions, so the highest figure cannot be used to characterize every Vertex AI operation. The SLA defines downtime using a server-side error-rate method and says a customer must notify technical support within 30 days to request a qualifying credit.
Amazon Bedrock example: interval and regional calculation
Amazon Bedrock’s SLA calculates availability in five-minute intervals from requests that do not fail with errors, then averages those results by AWS Region for the billing month. It defines an error as a 500 response and lists exclusions and service-credit terms. The page was last updated on October 4, 2023; verify that it remains the operative version for your account before relying on it. Because the calculation and scope differ from Vertex AI’s, the two percentages are not a direct reliability ranking.
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Measure the user experience separately
Keep your own telemetry for successful responses, latency, throttling, timeouts, malformed outputs, quota failures, and dependency errors. Segment results by model, region, operation, and tenant. Your measurements do not replace the contractual SLA, but they reveal impacts that an SLA denominator or exclusion may hide and provide the evidence needed for an eligible claim.
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Model continuity is model-specific. For every production deployment, maintain a lifecycle record containing:
- Exact model ID, version, snapshot, and deployment type.
- Region, cloud environment, security boundary, and launch date.
- Published deprecation and end-of-life dates.
- Minimum notice period and the channel used for notices.
- Successor model, compatibility notes, and whether old and new versions can run side by side.
- Differences in quality, latency, context limits, pricing, safety behavior, tools, and response schema.
- Lifecycle API, change feed, or alert mechanism that your team can monitor.
Microsoft Foundry lifecycle example
Microsoft’s Foundry model lifecycle policy describes a retirement date set 18 months out for generally available model versions and an existing-customer stage at 12 months. The policy also states that lifecycle treatment depends on region, cloud environment, deployment type, and security requirements. Emergency security-driven retirement can be an exception. Use the programmatic lifecycle values for the exact model and deployment rather than treating 18 months as a universal promise.
Amazon Bedrock lifecycle example
Amazon Bedrock’s lifecycle policy applies to models launched on or after September 7, 2026. It defines Active, Legacy, and End-of-Life states and describes Legacy notice periods of six months or 45 days, depending on the model. AWS notes that its Bedrock dates are specific to Bedrock and may differ from dates published by the model developer. Check both the applicable Bedrock policy and the model card.
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- Subscribe to lifecycle notices and query lifecycle status on a schedule.
- Identify a successor while the current model is still available.
- Replay a versioned evaluation set covering normal, edge, multilingual, safety-sensitive, and tool-use cases.
- Run old and new models in parallel with identical prompts, retrieval context, and decoding settings.
- Compare task quality, refusal patterns, latency, token usage, cost, structured-output validity, and downstream failures.
- Update prompts, schemas, guardrails, and capacity plans where behavior differs.
- Canary the replacement, retain rollback capability, and record the final cutover date.
A successor can change application behavior even when the API endpoint looks compatible. Treat migration testing as a release project with acceptance criteria, not as a one-line model-ID edit.
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Separate platform responsibility from model-developer responsibility
Managed model platforms can put operational, contractual, and data decisions in different hands. For each model, identify who operates the endpoint, who controls capacity and quotas, whose terms govern model use, which party receives support requests, and where prompts and outputs are processed.
Retention and operator access are different controls
OpenAI’s Bedrock guidance explains that AWS maintains deployment options and availability for models hosted through Bedrock. It also warns that an AWS Region is not, by itself, an OpenAI data-residency jurisdiction, and that retention controls are distinct from controls over operator access. A region label therefore cannot answer every residency or access question.
Amazon Bedrock’s retention documentation says models declare the retention modes they allow and that a model may be unavailable when the effective mode does not meet its requirement. OpenAI’s data-controls documentation likewise makes data use, retention, and regional controls dependent on endpoint, model, snapshot, and configuration eligibility. Obtain the applicable terms for the exact combination you plan to deploy.
Questions to put in the data-processing review
- Is customer content used for training, abuse monitoring, quality review, or service improvement?
- What retention mode applies to prompts, outputs, images, files, embeddings, and logs?
- Can the provider’s personnel or model developer access content, and under what authorization?
- Which countries process, replicate, support, or back up the data?
- Do regional controls apply to the endpoint, the model snapshot, and the selected configuration?
- Which party handles subpoenas, incident notification, deletion requests, and audit evidence?
- Do model-specific terms override the platform’s general data-processing terms?
Compare support as a contractual capability
Availability figures do not tell you whether a provider will help diagnose a failed deployment or a model-behavior regression. Compare the support plan attached to your account, not the support page visible to an unauthenticated visitor.
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|---|---|
| Eligibility | Required spend, contract type, region, product edition, and whether the model developer offers separate support. |
| Coverage hours | 24/7 or business-hours coverage, holidays, language, and regional handoff. |
| Severity definitions | What qualifies as a production outage, security incident, degraded performance, or information request. |
| Response commitments | Initial response and update targets by severity; distinguish targets from resolution guarantees. |
| Escalation | Named route for quota, billing, abuse, safety, model quality, and security issues. |
| Technical assistance | Architecture reviews, migration help, log analysis, root-cause reports, and model-specific expertise. |
| Exit assistance | Help exporting data, extending overlap, preserving logs, and completing deletion or transition. |
In a managed platform, ask whether the platform support team can escalate to the model developer and which party owns the final response. Record support commitments in the order form or service agreement where possible; an informal sales statement is difficult to enforce.
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Use a repeatable comparison worksheet
Build one row per provider, model, deployment type, and region. A useful worksheet contains these columns:
- Data and assets: exportable items, formats, API or process, limits, timeline, egress fees, deletion and retention terms.
- Service availability: operation, measurement interval, error definition, target, exclusions, evidence, claim deadline, and remedy.
- Model lifecycle: version, launch and retirement dates, notice, successor, overlap, lifecycle API, and exceptions.
- Data controls: training use, retention mode, operator access, processing geography, eligibility, and third-party terms.
- Support: tier, eligibility, hours, severity, response commitments, escalation, and responsibility split.
Score only after the evidence fields are complete. Weight the criteria according to your risk: a regulated workload may give residency and deletion priority, while a fast-changing application may emphasize model overlap and exportable evaluation assets. Keep a separate “unknown” status; do not turn missing documentation into a favorable score.
Contract and procurement questions
Ask these questions in writing and attach the answers to the service order or data-processing agreement:
- Which named assets can be exported at termination, in what formats, through which API or service, and within what time?
- Are there size, rate, region, residency, or egress-cost limits on the export?
- How long are primary, replica, backup, prompt, output, and audit records retained after termination?
- What exact service and operation does the SLA cover, and how is downtime calculated?
- What exclusions, evidence requirements, claim deadline, and service-credit schedule apply?
- For each production model, what are the deprecation and end-of-life dates, notice periods, successor options, and overlap rules?
- Can the current and replacement models be invoked simultaneously for testing, and are both available in the required region?
- Which party controls quotas, endpoint availability, model terms, retention, and data processing?
- What support tier is included, what are the severity response targets, and how does escalation reach the model developer?
- What happens if a security issue requires emergency retirement or a region loses eligibility?
Common comparison mistakes
- Comparing headline percentages. Different operations, denominators, regions, and exclusions make the figures non-equivalent.
- Assuming stored-data export means model portability. Check checkpoints, adapters, indexes, prompts, evaluations, and configuration separately.
- Ignoring application behavior. A compatible endpoint can still alter refusals, tool calls, structured output, latency, or cost.
- Treating a cloud region as residency proof. Processing, support, backup, and model-developer controls may cross boundaries.
- Waiting for a retirement email. Lifecycle dates can leave too little time for evaluation, procurement, and parallel operation.
- Counting service credits as business protection. Credits may not cover lost transactions, staff time, or regulatory impact.
- Assuming support is included. Eligibility, hours, severity definitions, and model-vendor escalation vary by plan.
- Leaving unknowns unrecorded. A missing answer is a migration risk that needs an owner and a deadline.
Keep the comparison current after selection
Portability, uptime terms, model dates, retention rules, and regional availability change. Assign owners for a quarterly or release-triggered review. Maintain lifecycle alerts, test exports after major schema changes, monitor your own service telemetry, and rerun replacement evaluations when a model version changes. Store the evidence with the architecture record so a new engineering or procurement team can understand what was promised and what was actually tested.
The defensible choice is the provider whose exact service, model, region, controls, support plan, and exit path fit your risk. A high SLA number can be useful evidence, but it cannot substitute for an export test, a lifecycle plan, or clear responsibility across a managed model stack.
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