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What does 98% uptime allow?
For an assumed 30-day month of 43,200 minutes, 98% availability allows 864 minutes of unavailability: 14 hours and 24 minutes. This is a calculation from the stated 30-day assumption, not an AWS-published statistic. A 28-, 29-, or 31-day calendar month has a different time-based allowance.
You can also define availability by requests rather than elapsed time. In that case, 98% means at least 98% of valid requests meet your success criteria during the measurement window; at most 2% may fail those criteria. Do not convert a request-based target into hours of downtime.
AWS defines availability as the percentage of time a workload is available for use, but the owner must define what “available” means for the workload. A server returning a response is not enough if a customer cannot complete the essential task, or if the response arrives too late to be useful. AWS’s availability guidance discusses measuring workload availability around its functions and customer experience.
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Separate the SLI, SLO, and AWS SLA
| Term | What it means | Example |
|---|---|---|
| SLI | The service level indicator: the measured signal. | Successful valid customer requests divided by all valid customer requests. |
| SLO | The service level objective: the target and measurement window you choose. | At least 98% of valid requests succeed over a calendar month. |
| SLA | A contractual service-level agreement from a provider, with defined coverage and remedies. | An AWS service commitment with its own availability calculation, exclusions, and possible service credits. |
Your application’s SLO is an engineering target you select and measure. An AWS service SLA applies only to the named service and the conditions in its current terms. An AWS component meeting its SLA does not prove that your application met its SLO: your application may also depend on other AWS services, third parties, code, configuration, and client network paths.
Choose an SLI that matches the customer experience and use one coherent definition throughout dashboards and reports. Amazon CloudWatch SLOs support period-based objectives, such as good periods divided by total periods, and request-based objectives, such as good requests divided by total requests. The documentation also describes error-budget reporting and composite SLOs that combine two to 20 operations. See Amazon CloudWatch SLO documentation for the current feature details.
Define what counts as available for your workload
Start with the critical customer journey, not the infrastructure diagram. For each operation that matters, specify valid traffic, the expected result, and an acceptable response-time threshold. A request that succeeds only after the client has timed out may be a failure from the customer’s perspective, even if a server eventually returns success.
- Identify the key operation or journey, such as signing in, placing an order, or retrieving a record.
- Define which requests are valid for measurement and which outcomes count as good, failed, or too slow.
- Decide how to treat scheduled maintenance, periods with no traffic, client errors, and partially working features. Document the choice rather than silently inheriting an AWS service’s SLA rules.
- Measure from both the service side and a client perspective where appropriate. Client-side canaries can expose failures that internal health checks miss.
For a period-based SLI, define the period length and the condition for a good period—for example, whether a one-minute period is good only when the critical operation works within the latency limit. For a request-based SLI, define the request population and success condition. These approaches answer different questions; avoid combining them as if their denominators were interchangeable.
Set the 98% SLO and track its error budget
Write the target together with its SLI and window, such as “98% of valid checkout requests succeed during each calendar month.” A bare “98% uptime” target is ambiguous if it does not say what is measured, over what period, and what counts as unavailable.
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The error budget is the amount of non-compliance the target permits in its measurement window. For a 98% request-based objective, up to 2% of valid requests may fail the defined success criteria. For a 30-day time-based objective, the corresponding allowance is 14 hours and 24 minutes. Track budget consumed as well as whether the target was met, so an incident early in the period is visible before the window closes.
AWS describes an error budget as the amount of requests that can be non-compliant while the application still meets its SLO. That request-based wording applies to that definition; for a period-based SLO, track the budget using the period-based SLI’s own units. CloudWatch’s SLO documentation explains both objective types and error-budget reports.
Map dependencies before adding redundancy
List everything needed to complete the measured customer operation: application components, data stores, identity, DNS, network paths, third-party APIs, and operational processes. Mark single points of failure and note which components are hard dependencies. If a critical dependency is unavailable, determine whether the customer operation can still succeed in a degraded mode or fails outright.
For hard dependencies, AWS illustrates that the invoking workload’s availability is the product of component availabilities. This model shows why several individually reliable components do not automatically make a highly available end-to-end service. The multiplication is a theoretical model, not a substitute for measuring the workload, and correlated failures can invalidate assumptions of independence.
Redundant components can improve theoretical availability when they fail independently and failover works. A second instance, Availability Zone, or region will not help as intended if both paths share a failure mode, if capacity is insufficient, or if health detection and recovery are not reliable.
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Improve detection, recovery, and resilience
Detect user-impacting failures
Alert on the SLI that represents customer impact, not only on infrastructure signals such as instance state. Combine service-side telemetry with health checks or client-side canaries where they add coverage. Watch latency as well as outright errors: a slow response can be functionally unavailable to a client with a fixed timeout. Look for partial failures, such as a working home page but a failing checkout operation.
Make recovery repeatable
Use tested runbooks, safe automated recovery, and incident practice to reduce time to detect and restore service. A health check should reflect whether the workload can perform its critical operation, not merely whether a process is running. Test the recovery path regularly; an untested failover mechanism is an assumption, not evidence of resilience.
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Add redundancy for a defined failure domain
Choose redundancy based on the failures you need to tolerate. Multi-AZ deployment addresses a different failure domain from an instance-level replacement or a multi-region design. Plan for capacity, failover behavior, data consistency, health detection, and the operating work required to keep the design safe. Set and test recovery objectives, including recovery time and data loss tolerance where relevant.
AWS cautions that higher availability typically increases cost and calls for stronger testing, validation, and operational practices. Its Reliability Pillar availability guidance uses 99.999% as an explanatory “five nines” example, not as a universal AWS workload promise. A more demanding target is useful only when it matches the business need and the system can operate it reliably.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Understand what AWS service SLAs cover
Service SLAs are useful for understanding AWS’s contractual commitments for particular dependencies, but their scope and measurement are service-specific. The figures below are examples from AWS’s official SLA pages reviewed on October 4, 2026; check the current terms before relying on them because service terms can change.
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Amazon EC2
The Amazon Compute Service Level Agreement states a 99.99% regional commitment when all running instances are deployed concurrently across two or more Availability Zones in a region, with a stated alternative for a region that has only one Availability Zone. It also states a 99.5% commitment for a single EC2 instance. The page specifies service-credit tiers and exclusions. These figures apply to the SLA’s defined EC2 scope and conditions, not to an arbitrary application built from multiple services.
Amazon S3
The Amazon S3 Service Level Agreement uses per-request-type error rates over five-minute intervals to calculate uptime and lists different credit tiers by storage class. For specified S3 Standard, S3 Express One Zone, Glacier Flexible Retrieval, Glacier Deep Archive, and other requests, the listed tiers begin below 99.9%, then below 99%, then below 95%. For Intelligent-Tiering, Standard-IA, One Zone-IA, and Glacier Instant Retrieval, listed tiers begin below 99%, then below 98%, then below 95%. The SLA also sets exclusions and a claim deadline.
These service-level thresholds are not your application’s SLO. A workload result of 98% does not by itself establish eligibility for an AWS service credit; the applicable service, calculation, exclusions, and claim requirements control. Credits are subject to the exact SLA and are not necessarily cash refunds or compensation for business impact.
Review results and adjust the design
Review SLO attainment and error-budget use alongside incidents, recovery times, user-facing latency, and the cost and operating complexity of resilience measures. If the target is missed, identify whether the cause was a dependency, a detection gap, a slow recovery, or a failure mode the design does not isolate. If the target is comfortably exceeded, consider whether the reliability margin is worth its cost or whether the objective should better reflect customer needs.
The measure of success is the workload’s customer-visible result over the SLO window. Architecture diagrams, component SLAs, and redundancy are inputs to that outcome—not substitutes for observing it.
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