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Choose an AI provider by verifying the uptime commitment for your exact service, model and deployment, then checking how it is measured, what it excludes and what remedy it offers. Separately, map how you would export or rebuild the parts that depend on that provider. An SLA is useful contract evidence, not proof of which provider will have the best real-world uptime for your workload.
Start with the workload you need to keep available
Before comparing providers, write down what must work in production. A generative AI application can depend on more than a model endpoint: prompts, orchestration, retrieval, identity, monitoring and data pipelines may all affect whether users can complete a task. A provider’s commitment may cover only a named service or configuration, not the whole application.
- Identify the workload: list the model or models, API, deployment type, region, and production configuration you intend to use.
- Set a service requirement: specify the user-facing functions that must remain available and what an interruption means for your business.
- Map dependencies: record the provider-specific services and integrations the workload relies on, including any accepted dependencies.
- Define evidence you need: distinguish contract terms from operational evidence such as incident information and your own application-level telemetry.
This gives you a concrete scope to compare. If a provider cannot confirm that the intended model and deployment are covered by the cited commitment, do not treat that commitment as protection for the workload.
Read the SLA as a conditional contract, not an uptime ranking
An SLA’s percentage is meaningful only alongside its service definition, measurement rules, exclusions and remedy. Compare the following terms for the exact API or deployment you plan to use:
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- Coverage: named service, model or configuration, deployment type, region, and any production-use conditions.
- Measurement: request and error criteria, measurement window, aggregation period, and how idle periods or partial failures are treated.
- Exclusions: events or causes the provider does not count toward downtime.
- Claims and remedy: what evidence is required, who must submit a claim, the deadline, applicable thresholds or caps, and whether the remedy addresses your business impact.
For example, Google Cloud’s Vertex AI SLA lists service-specific commitments rather than one promise for every Vertex AI model or endpoint. In the SLA accessed on October 7, 2026, it lists 99.9% for training, deployment and batch prediction; 99.9% for certain AutoML online prediction workloads; 99.5% for certain custom-model online prediction configurations and Vertex Pipelines; and 99% for the training cluster control-plane API. For covered services in that document, “Downtime” means a server-side error rate greater than 5%, and an eligible credit request must be made within 30 days. Check the relevant row and terms against your actual configuration.
The Amazon Bedrock SLA describes a monthly uptime calculation for each AWS Region, defines an error as a request returning HTTP 500, lists exclusions and a service-credit claim process, and describes credits as the remedy absent another agreement. Its page says it was last updated October 4, 2023; recheck the live SLA and your governing agreement before relying on it. Its credit schedule starts below 99.9% monthly regional uptime, subject to the stated definitions, exclusions and claim requirements.
These figures are contractual terms with different scopes and measurement rules. They do not establish a like-for-like history of outages, nor do they predict availability for your application, model, region or account. Do not rank providers by comparing the percentages alone.
Check operational availability for your exact deployment
A contract tells you how a defined commitment is assessed; it does not show how often your users will experience an interruption. Review incident and status information, confirm regional and endpoint availability, and understand any relevant quotas. Then measure availability at the application level: an endpoint responding successfully does not by itself establish that your users’ complete workflow is functioning.
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Ask the provider and your internal service owner to identify what operational evidence is available for the specific service and region. The sources cited here do not establish a comparable historical uptime ranking across providers, so a universal “most reliable” choice cannot be inferred from these SLA pages.
Verify model lifecycle and data terms before committing
Plan for model changes and retirement
Model availability and lifecycle rules can vary by region, cloud environment and security requirements. Microsoft Learn’s Foundry Models lifecycle and support policy advises customers to evaluate newer models rather than wait for an official replacement. Include lifecycle notices, migration lead time, replacement options and a repeatable evaluation process in your provider review. A replacement model can require behavior checks even when the surrounding application remains unchanged.
Keep prompts and other modifiable application assets versioned. When changing a model or provider, assess response behavior and integrations with an evaluation or regression run; a successful data export alone does not show that the application will behave equivalently.
Confirm retention settings, eligibility and geography
Retention can affect whether a model is usable. Amazon Bedrock’s data-retention documentation says each model declares allowed retention modes; if the effective mode is below a model’s requirement, the model may appear unavailable or invocations may fail. Check the effective setting, the model’s eligibility and the terms that govern your deployment before assuming your preferred mode works for every model.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsFor OpenAI models used through Bedrock, the OpenAI on Amazon Bedrock integration guide says AWS maintains Bedrock deployment options and availability and directs customers to check model and endpoint availability, cross-Region inference and quotas. It also cautions that an AWS Region is not, by itself, an OpenAI data-residency jurisdiction. This qualification concerns that specific integration; verify the service, model, deployment and contract that apply to your own choice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build an exit path that can actually be used
Portability is not just the ability to download data. It is a documented, owned plan for maintaining continuity, moving or rebuilding dependencies, and meeting return and deletion obligations. The Australian Government Architecture’s Guide to managing lock-in, portability and exit planning recommends considering data and metadata export, return obligations, migration costs, continuity, ownership and testing.
- Inventory dependencies. List model endpoints, prompts, orchestration, retrieval stores, identity controls, monitoring and data pipelines. Mark which are provider-specific and why each dependency is accepted.
- Document export mechanics. For every item you may need to move, record the export method, documented format, responsible party, expected duration, backup location, limits or charges, and how long data remains available after contract end. AWS’s six lock-in considerations specifically call attention to process, technical requirements, timeframes, charges, formats, backup locations and post-contract availability.
- List what must return or remain available. Include data, metadata, logs, configuration, evaluation assets and application code where relevant. Identify contractual return and deletion obligations and what evidence you need to confirm they were met.
- Separate direct moves from rebuilds. Record which components can be exported and reused, which require replacement, and which need revalidation. Include prompt versions and the evaluation process for a replacement model.
- Assign ownership and continuity. Name the service owner and exit-plan owner, estimate transition time and cost, and specify how the workload will operate during a migration or interruption.
- Set review and exercise triggers. Decide when to revisit the plan—for example, when the model, contract, deployment, business criticality or data sensitivity changes—and whether and how to test the exit path.
UK Government guidance on managing technical lock-in in the cloud advises balancing exit planning against the value of staying with a provider. The Australian Government guide makes the same trade-off explicit: “Maximum portability is not always the best outcome.” More abstraction, redundancy or portability can add engineering work, cost and operational complexity. Choose a level proportionate to the workload’s criticality, sensitivity and continuity needs, and record the dependencies you accept.
Use a decision record to compare providers
For each candidate, keep one concise record that connects contract evidence to the real workload. It should make gaps visible rather than hide them behind a single score.
| Decision area | What to record |
|---|---|
| Covered workload | Model, API, deployment, region and production configuration; the exact SLA coverage or an explicit coverage gap. |
| Availability terms | Measurement interval, error definition, aggregation, exclusions, claim procedure, deadline and remedy. |
| Operations | Available incident and status information, quotas, regional or endpoint constraints, and application-level monitoring owner. |
| Data controls | Retention mode, model eligibility, residency scope, account or project controls and governing deployment terms. |
| Change management | Lifecycle notices, replacement choices, migration lead time and model evaluation process. |
| Exit readiness | Export and deletion path, formats, costs, transition duration, continuity arrangements, responsible owners and test or review triggers. |
| Accepted trade-offs | Provider dependencies retained, portability or redundancy measures declined, and why the cost and complexity are proportionate. |
Choose the provider whose documented terms and operational fit meet your requirements, and whose remaining dependencies you can accept and manage. If a material point—such as coverage for the intended model, export timing or data-return obligations—is not established in the live documentation or contract, resolve it with the provider before procurement rather than treating it as guaranteed.
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