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The safest way to compare AI providers is to examine evidence for the exact services and models you will run—not to rank vendors by one portability slogan or SLA percentage. Check five things before signing: which assets you can export, what an uptime promise actually measures, how model retirement is handled, which party controls data and processing, and what your support plan really guarantees.
What a meaningful AI-provider comparison covers
An AI service is rarely one indivisible product. Your application may depend on a cloud control plane, an inference endpoint, a training service, a vector index, a third-party model and provider-specific safety or orchestration settings. Each component can have different export rights, availability targets, lifecycle dates and support responsibilities.
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Compare providers at the service-and-model level. Record the exact model ID or version, deployment type, region, account tier and contract terms. A provider that lets you download source files may still leave you unable to move a fine-tuned adapter, evaluation history, prompt configuration or application behavior without rebuilding it.
Use the following five axes:
- Data and asset portability: what can be exported, in which format, through which process, within what limits and at what cost.
- Service availability: the covered operation, measurement method, exclusions, claim deadline and remedy.
- Model lifecycle: notice of deprecation, end-of-life dates, replacement options and testing overlap.
- Data controls: retention mode, training use, operator access, processing geography and third-party terms.
- Support: eligibility, hours, severity definitions, response commitments and escalation ownership.
These axes produce a decision you can defend in procurement and revisit when a model, region or contract changes.
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Start with an inventory of everything you might need to move
Before asking a provider whether your data is portable, list the assets your application actually uses. Mark each item as business-critical, replaceable or disposable. Require a written answer for every critical item.
| Asset | Questions to ask | Migration test |
|---|---|---|
| Source and customer data | Can you export all records, metadata, permissions and timestamps? Which API, file format, size limit, region restriction and egress charge apply? | Export a representative tenant, verify checksums and restore it in an isolated account. |
| Prompts, outputs and conversations | Are templates, system messages, tool definitions, generated outputs and moderation decisions retained and exportable? | Recreate a test workflow from the export without opening the original provider console. |
| Logs and audit records | What event types are included, how long are they retained, and can you obtain them after termination? | Request a date-bounded export and confirm it contains request IDs, timestamps and error details required for incident analysis. |
| Evaluation sets | Can you export prompts, expected answers, labels, scores and evaluator configuration in a machine-readable format? | Run the same evaluation harness against a second endpoint. |
| Vector indexes and embeddings | Can vectors, source-document IDs, chunking settings, metadata and embedding-model details be exported? Are dimensions or formats proprietary? | Rebuild an index elsewhere and compare retrieval on a fixed query set. |
| Fine-tuning data and artifacts | Can you download training files, checkpoints, adapters, tokenizer details, hyperparameters and safety settings? | Load the artifacts in a clean environment and document any missing dependency. |
| Customer-trained or adapter models | Is the resulting model exportable, or only the training data? Are weights tied to a provider’s runtime? | Deploy the exported artifact on an independent compatible runtime, if contractually permitted. |
| Orchestration and configuration | Can you export agents, tools, routing rules, schemas, secrets references, quotas and policy settings as code? | Recreate a non-production environment from version-controlled files. |
| Application code | Which SDK calls, proprietary APIs, safety filters and identity integrations must be replaced? | Maintain an adapter layer and run integration tests against a second provider. |
Exportability is not the same as interoperability. A JSON download may preserve records while losing indexes, permissions or model behavior. Treat every “export” claim as incomplete until you know the format, process, retention window and ability to use the result outside the provider.
How do I switch AI providers without losing my data?
Plan the switch as a controlled migration rather than a final-day download. The sequence below exposes hidden dependencies before termination rights or retention periods expire.
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- Read service-specific terms. A general cloud exit page may describe storage exports but not model weights, tuning artifacts or AI logs. Confirm the terms for the actual AI service and model.
- Export a representative sample. Include large objects, multilingual text, deleted records, permissions, metadata and failure cases. Record duration, API calls, throttling and any fees.
- Verify integrity. Compare counts, checksums, timestamps and relationships. Have the receiving system parse the files without manual editing.
- Rebuild dependent services. Restore indexes, reapply chunking and embedding settings, recreate policies and replace provider-specific SDK calls.
- Run quality and behavior evaluations. Compare accuracy, safety outcomes, latency, cost and tool-calling behavior on a fixed test set. A technically complete export can still produce a materially different application.
- Operate in parallel. Route a controlled percentage of traffic to the candidate provider while retaining rollback capability. Keep old and new configurations versioned.
- Terminate deliberately. Obtain a final export, confirm deletion and post-termination retention obligations in writing, and preserve evidence of the completed handoff.
Ask for the provider’s documented exit process, but do not assume one universal download action exists. Service-specific methods are common: some assets use APIs or standard tools, while file services may require copying from a mounted share. The export method, format and limitations must be confirmed for each service.
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Read the uptime SLA, not just the percentage
An SLA is a conditional service-credit formula, not a promise that your application will never be unavailable. A percentage is comparable only after you normalize what is measured.
| Field | What to capture | Why it changes the decision |
|---|---|---|
| Covered service and operation | Inference, training, batch prediction, pipelines, deployment, control plane or another named function. | An inference target does not protect a training job or management API. |
| Measurement window | Calendar month, billing month or another interval; interval length such as five minutes. | Short failures can be rounded or missed differently by each method. |
| Error denominator | Requests, server-side errors, successful intervals or another calculation. | Client timeouts, throttling and network failures may be excluded. |
| Regional scope | Region, multi-region service or account-wide measurement. | A global percentage may hide a region you cannot use. |
| Exclusions | Maintenance, force majeure, customer configuration, dependencies, quota breaches and unsupported versions. | Excluded downtime cannot earn a credit. |
| Claim process | Evidence required, support channel and deadline. | Missing a deadline can forfeit an otherwise eligible claim. |
| Remedy | Service credit, refund or another contractual remedy. | A credit rarely equals the business cost of an outage. |
What the published examples actually show
Google Cloud’s Vertex AI SLA lists separate monthly objectives. It states at least 99.9% for specified training, deployment and batch-prediction services; at least 99.5% for certain custom online-prediction and pipeline services; and at least 99% for the training-cluster control-plane API. The document defines downtime using server-side error rate and lists exclusions. It says a customer must notify technical support within 30 days to request a qualifying credit. These objectives apply only to the covered categories and conditions in the current SLA.
Amazon Bedrock’s SLA calculates availability in five-minute intervals from requests that do not fail with errors, then averages the result 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 October 4, 2023; verify that it remains the operative version for your account and service before relying on it.
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Those figures cannot be turned into a provider-wide reliability ranking. Different services, denominators, regions and exclusions produce different exposure. Measure your own client-side success rate, latency and dependency failures in addition to checking SLA eligibility.
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What happens when an AI model is deprecated?
Model continuity is model-specific. For every production model, record the exact identifier and version, launch date, announced deprecation date, end-of-life date, minimum notice, proposed successor, deployment type and region. Ask whether the old and replacement versions can run side by side long enough to evaluate real traffic.
Microsoft Foundry lifecycle timing
Microsoft’s Foundry lifecycle information describes an 18-month-out retirement date for generally available model versions and an existing-customer stage at 12 months. The policy also says application depends on region, cloud environment, deployment type and security requirements. Emergency, security-driven retirement can be an exception. Treat the 18-month statement as a policy framework, not a universal guarantee: check the programmatic lifecycle values for the exact model and deployment.
Amazon Bedrock lifecycle states
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 Bedrock dates are specific to Bedrock and may differ from dates published by the model developer. Check the model card and the applicable policy instead of assuming a developer’s public date governs your endpoint.
Build a replacement test before notice arrives
- Subscribe to lifecycle notifications or schedule periodic API checks where the provider offers them.
- Pin the production model ID and log every change, including automatic aliases and regional substitutions.
- Obtain a candidate replacement early and run your evaluation set, safety tests and latency benchmarks.
- Compare cost, context limits, tool schemas, output structure and refusal behavior; migration can alter all of them.
- Keep rollback and dual-run infrastructure until the replacement meets your acceptance criteria.
A successor name alone is not a compatibility statement. Require an overlap period and a documented rollback path in the contract when model behavior is business-critical.
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Managed model platforms: separate the operator from the model developer
When a cloud platform hosts a model created by another company, several parties may control different risks. The platform may operate the endpoint, quotas and regional capacity, while the model developer sets model terms, retention requirements or lifecycle dates. Your contract should identify which party is responsible for each item.
- Endpoint operations: who maintains availability, scaling, quota increases and incident response?
- Model terms: whose license, acceptable-use rules and retirement policy apply?
- Data handling: which party determines training use, retention mode and operator access?
- Geography: where is data stored, processed and routed for safety or abuse monitoring?
- Evidence: which logs and support records can you obtain from each party?
OpenAI’s Bedrock guidance explains that AWS maintains deployment options and availability for models hosted in Bedrock and cautions that an AWS Region is not itself an OpenAI data-residency jurisdiction. It also distinguishes retention controls from controls over operator access. Amazon Bedrock’s retention documentation states that models declare allowed retention modes and may be unavailable if the effective mode does not meet a model’s requirement. OpenAI’s data-control documentation likewise makes data use, retention and regional controls dependent on endpoint, model, snapshot and configuration eligibility.
Do not infer residency from a hostname or region label. Obtain the applicable service terms, model terms and configuration eligibility for the endpoint you will use.
Compare support as a contract, not as a reputation
Availability figures say little about whether a support team will help you migrate a model or diagnose a data-export failure. Compare the support plan attached to your account.
| Support dimension | Evidence to request |
|---|---|
| Eligibility | Required plan, spend threshold, region and named-account conditions. |
| Hours and channels | 24/7 or business-hours coverage, phone or ticket access, incident portal and language support. |
| Severity definitions | What qualifies as critical, high or normal; whether a degraded model response counts. |
| Response commitments | Initial-response and update targets, not merely “best effort.” |
| Escalation | Route from service support to platform engineering and, where relevant, the model developer. |
| Migration assistance | Whether export, replacement-model testing or architecture guidance is included or separately charged. |
| Post-termination help | Access to logs, exports and support during the contract’s retention and deletion window. |
Ask who owns an incident when a managed model fails: the endpoint operator, the model developer or both. Require a single escalation path and a named handoff process, even if multiple parties investigate internally.
A practical comparison worksheet
Score providers only after collecting evidence for the same workload. “Not stated” is a valid result and should trigger a clarification request rather than an optimistic assumption.
| Criterion | Record for each provider | Decision question |
|---|---|---|
| Data and assets | Exportable items, formats, API or process, limits, timeline, egress fees, deletion and retention language. | Can another team restore the critical assets without proprietary console access? |
| Availability | Exact operation, region, interval, error definition, target, exclusions and credit process. | Does the SLA measure the failure that would hurt this application? |
| Lifecycle | Model/version, retirement and notice dates, successor, overlap, deployment and region exceptions, alert mechanism. | Can you test and cut over before the old model ends? |
| Data controls | Training use, retention mode, operator access, processing geography, eligibility and third-party terms. | Can the configuration meet your regulatory and contractual requirements? |
| Support | Tier, eligibility, hours, severity, response, escalation and responsibility split. | Will an accountable engineer be available when migration or production fails? |
Common comparison mistakes
- Using a general portability statement as proof of AI portability. A storage-export page may not cover model weights, adapters, prompts, logs or application configuration.
- Comparing 99.9% with 99.5% without reading the denominator. Different operations and error definitions make the percentages non-equivalent.
- Calling an SLA a guarantee. Exclusions, claim deadlines and credit-only remedies limit what the promise provides.
- Assuming the model developer’s lifecycle applies to a hosted endpoint. Managed platforms can publish separate dates and notice rules.
- Inferring residency from a cloud region. Processing, safety review and operator access may involve separate locations and terms.
- Ignoring non-model dependencies. Control planes, indexes, identity systems, quotas and tool integrations can cause an outage or block migration even when inference is available.
- Waiting for a deprecation notice to begin testing. Quality, latency, cost and output structure can all change during replacement.
- Accepting “support included” without a severity table. Confirm eligibility, hours, response targets and escalation ownership in writing.
Questions to settle before signing
- Which exact assets can we export, in which documented formats, and through which API or procedure?
- What size, rate, residency or egress restrictions apply to each export?
- How long will exports, logs and deleted data remain available after termination?
- Which SLA covers our inference, training, batch and control-plane operations in our chosen region?
- How are errors and downtime calculated, what is excluded, and how do we claim a remedy?
- What are the retirement and notice dates for each model ID and deployment type?
- Can old and replacement models run concurrently, and can we obtain lifecycle values programmatically?
- Which party controls retention, training use, operator access and processing geography for a managed model?
- Which support tier applies to our account, and what are the response and escalation commitments?
- What evidence will the provider supply during an outage, export dispute or model migration?
Recheck these answers at procurement and whenever you change region, model, deployment type or contract. Provider documentation, lifecycle dates, retention rules and customer entitlements can change.
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