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Test a candidate provider with the same representative requests, traffic conditions, and measurement window you use for your current provider. Compare successful-request and failure rates alongside latency percentiles—not a single average—and separate network response time from model-generation time. Segment results by model, service tier, project, and region, then switch only if the candidate meets budgets you defined for your application. A status page or an aggregate benchmark can add context, but neither establishes how the service will perform for your account and workload.
What should a provider test tell you?
A useful test answers a practical question: will this provider serve your application’s requests reliably and quickly enough under the conditions you expect after migration? It does not establish that one provider is universally faster or more reliable. The result applies to the models, endpoints, account tier, region, network path, request mix, and time periods actually tested.
Keep reliability and speed separate in the analysis. Reliability concerns whether requests complete as expected, including client timeouts and interrupted streams. Latency concerns how long successful responses take to begin and finish. A provider can perform well on one measure and poorly on the other, so report both rather than blending them into a single score.
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The test also has to distinguish infrastructure from model behavior. If the providers offer different model implementations, matching prompts and settings does not make the models identical. Record that limitation: a measured difference may reflect the model, its output behavior, or the service path, rather than provider infrastructure alone.
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Which requests should you include?
Build a fixed test set from real application traffic, retaining the request characteristics that shape your users’ experience. The aim is not to create a perfect synthetic copy of production, but to make the candidate and incumbent face the same meaningful workload.
Preserve the request shape
- Prompt lengths: Include the range of input sizes your application actually sends. Long prompts can make a different request from short ones.
- Expected output lengths: Include short and longer responses as relevant. Total request time depends partly on output size.
- Model class or capability: Compare the capability your application needs, and record when model implementations are not equivalent.
- Streaming mode: Keep streaming enabled or disabled consistently. For streaming, measure when the first token arrives as well as when the response finishes.
- Endpoint features and request settings: Keep relevant settings and features aligned wherever both services support them. Note any feature that cannot be matched.
Use a stable set of requests for the direct comparison rather than changing prompts during the test. If you also want to learn how results vary across request types, label those groups so a slow long-output request does not obscure performance on a different kind of traffic.
Record what is not equivalent
Make a short list of differences that could affect results: model implementation, available endpoint features, or any setting that cannot be matched. A comparison can still inform a migration, but it should not imply that those differences were controlled away. If your application’s real decision is whether the entire candidate service works for your use case, compare the actual usable configurations and describe the result as an end-to-end comparison.
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Run both providers from the same application region and network path where possible, with the same traffic pattern and client behavior. Comparing different locations, request rates, or timeout and retry policies can make provider results difficult to interpret.
- Choose the source environment. Use the region and network path your application would use after the switch. If you cannot run both services from that same path, record the difference.
- Match request pressure. Keep concurrency and request rate comparable. A test with a different arrival pattern or number of simultaneous requests is not a like-for-like comparison.
- Keep client handling consistent. Use the same timeout, retry, and connection settings for both services. Retain the settings in the report so readers can interpret what counted as a completed request or a timeout.
- Run within the same periods, or alternate in a controlled sequence. Doing so helps reduce the influence of changing system load over time. Record when the runs occurred.
- Repeat across representative periods and regions. Production conditions vary. Include the regions and periods relevant to your application rather than treating one brief run as a forecast.
There is no universal test duration or minimum sample count established by the cited sources. Choose a duration and volume that make sense for your traffic variability and the risk of switching, and disclose the uncertainty if the sample is limited. A very small set of successful calls may show that a basic request works; it cannot, by itself, establish a dependable failure rate or latency distribution.
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Which reliability and latency measurements matter?
Keep raw counts and percentages together for failures, and report latency distributions rather than only averages. OpenAI’s troubleshooting guidance recommends reviewing P50, P75, and P95 because averages can hide real-user impact.
| Measure | What it describes | How to use it |
|---|---|---|
| Attempts and completed requests | The request volume and how many requests finished under the test’s definition of completion. | Show both so a failure percentage has visible context. |
| HTTP status codes | Responses categorized by status code. | Count and inspect them alongside client-side failures; do not treat every unsuccessful request as the same event. |
| Client timeouts | Requests that exceeded the client’s configured wait limit. | Count them as failures for the application experience, and state the timeout policy used. |
| Interrupted streams | Streaming responses that did not complete as expected. | Track them separately so partial output is not mistaken for a fully successful response. |
| Failure percentage | Failures relative to attempts. | Report it with raw counts; percentages are more informative than raw error totals when providers receive different request volumes. |
| P50, P75, and P95 latency | Latency at the median and slower portions of the observed request distribution. | Report percentiles instead of relying on the average, and specify which latency interval each percentile represents. |
| Time to first token (TTFT) | For streamed generation, the wait until the first generated token arrives. | Use it to describe how quickly generation begins; do not confuse it with a network endpoint’s time to first byte. |
| Total request duration | Elapsed time until the request completes. | Read it alongside output size and request type, since longer outputs can take longer to finish. |
| Token generation velocity | Generation speed when exposed by the client or provider. | Track it as a separate experience measure; it describes a different part of the request than time to first token or total duration. |
For a streamed request, a quick first token does not mean the full response will finish quickly. Conversely, a slow first token and a fast subsequent stream have different user-visible effects from a quick start followed by slow generation. Preserve the separate measures if the client or provider exposes them.
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How should you compare dashboard data and status pages?
Use provider dashboards and status pages to understand context, but filter and interpret them at the level relevant to your account. Aggregate availability figures are not a promise that a particular model, feature, tier, or customer will see the same result.
Filter dashboard metrics before drawing conclusions
OpenAI’s troubleshooting documentation concerns its Service Health and Usage dashboards. It advises filtering by model, service tier, and project; examining HTTP request counts and status codes; using error percentages; and reviewing P50/P75/P95, TTFT, request time, token velocity, and token usage. If you use those dashboards, compare aligned time ranges and token usage, and avoid combining tiers or projects when diagnosing a localized production issue.
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Model, service tier, and project can change what an aggregate measurement means. A result that combines different tiers or projects may hide an issue affecting the particular service configuration you intend to use. Align those dimensions as closely as possible before treating dashboard values as evidence for a switch.
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Use incident history as context, not as your result
Check the specific API component and relevant incident history for each provider. OpenAI states that its availability metrics aggregate across tiers, models, and error types, and that availability for an individual customer can differ. Anthropic’s status page lists Claude API status and incident updates, but a live incident history cannot establish how a particular account will perform under your traffic.
A green overall status page can coexist with a slower or less reliable experience for a particular model, feature, region, or tier. Conversely, a past incident does not by itself show how the service will behave during your test. Use status information to annotate the period you measured, not to replace request-level measurements.
How do service commitments affect a switching decision?
Do not infer a latency or uptime commitment from a status page or a dashboard average. The cited sources do not establish a cross-provider, contract-equivalent comparison of latency SLAs, so verify the exact terms that apply to the account and service tier you are considering.
OpenAI’s help documentation says standard-tier latency is not guaranteed and directs customers with specific latency requirements to contact support. It distinguishes tiers with defined SLAs, but the actual terms and eligibility must be checked for the customer’s account and tier. Treat this as provider-specific guidance, not a general statement about other providers’ contracts.
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Before deciding, identify the service tier you will actually use and confirm the applicable contract or SLA, its eligibility conditions, and what it covers. A commitment and an observed test result answer different questions: the test describes what happened in the measured workload and period; the contract defines applicable terms for eligible service use.
How do you decide whether the candidate is good enough?
Set the decision rule before you run the comparison. Define acceptable failure and percentile-latency budgets based on your application’s user experience and recovery needs, then evaluate the candidate against those targets over representative periods and regions.
- Write down the budgets. Set the failure limit and relevant latency-percentile limits for the experience your application needs. There is no universal threshold in the cited material; the budgets depend on your application.
- Decide what counts as failure. State how you handle HTTP errors, client timeouts, and interrupted streams, and keep those event types visible in the results.
- Check each relevant segment. Review the model, tier, project, and region needed for the migration instead of relying on an all-provider aggregate.
- Consider the period and sample. Decide whether observed variability and the amount of data are adequate for the switching risk. If they are not, describe the result as uncertain rather than claiming the candidate has established performance.
- Apply the rule consistently. Switch only if the candidate meets the predefined targets under representative conditions. If it does not, identify which metric or segment misses the budget before changing the test or the target.
Do not create a universal “winner” score unless you have a defensible way to weight reliability, first-token delay, completion time, and generation speed for your application. Keeping those measurements visible lets a team make an explicit trade-off—for example, whether a small speed advantage is acceptable when failure behavior is worse—without hiding the choice in one blended number.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should the test report contain?
A concise report should let another person see what was compared, what happened, and how much confidence to place in the result. Record the test configuration with the measurements, because a latency number without its workload and operating conditions is difficult to apply to a migration.
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- Prompt and expected-output characteristics, streaming mode, and any differences between model implementations or request settings.
- Run periods, source region and network path, concurrency, request rate, timeout, retry, and connection settings.
- Attempts, completed requests, HTTP status-code counts, client timeouts, interrupted streams, and failure percentages.
- P50, P75, and P95 for clearly named latency measurements; TTFT, total duration, and token velocity where available.
- Relevant dashboard filters, status-page incidents during the test period, and the applicable service commitment checked.
- The predefined acceptance budgets, whether each was met, and limitations such as a small sample or unmatched feature.
The LLM Latency Tracker offers an example of why scope belongs in a report: its dataset description says it uses repeated network and inference probes in four regions and reports network TTFB separately from inference TTFT. The tracker described coverage of 46 providers across four regions and stated that it had collected 4,885,121 raw probes between 2026-07-23 and 2026-10-24. Those figures describe that dataset at the time of access, not the full provider market or an application-specific forecast. Its probe locations, endpoint choices, and methodology may not match your geography, account, model, or traffic pattern.
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Independent probe data can be useful for generating a comparison or checking broad context, but it is not a substitute for your own matched workload. The tracker’s published daily aggregates include P50/P95, sample count, and success rate, while keeping network and inference timing separate; interpret those values within its stated probe setup.
Frequently Asked Questions
Can I use a public latency tracker to choose an inference provider?
Use one to identify providers or regions worth testing, not as a forecast for your own application. The LLM Latency Tracker’s measurements come from its probe locations, endpoint choices, and method, which may differ from your account, model, network path, and traffic.
What if the two providers do not offer the same model?
Compare the configurations that could realistically serve your application, then label the result an end-to-end service comparison rather than an infrastructure-only test. If model behavior changes prompt handling or output length, retain that as a limitation when interpreting latency.
Should I include retry attempts in the reliability result?
Record both the original attempts and any retry behavior so the report reflects what the application actually experienced. Keep the retry policy identical during the provider comparison; otherwise, differing client recovery behavior can obscure the service comparison.
Does a provider SLA guarantee the same latency I measured?
Not necessarily: a test records observed behavior for a particular workload and period, while an SLA is governed by its written terms and eligibility conditions. Confirm the exact account and tier coverage with the provider rather than treating an observed percentile as a contractual promise.
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