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Use Clobbr to check a screenshot API in two passes: first send requests sequentially to establish a latency baseline, then run requests in parallel to see how latency and failures change under concurrency. Treat the result as a focused diagnostic, not proof of production capacity: the outcome depends on the endpoint, request settings, run size and environment.
Decide what the test should tell you
Choose the question before choosing a request count. A sequential run can show response latency without overlapping requests; a parallel run can reveal changes when requests overlap. A repeatable CLI or CI run can help check whether a change shifts results over time. These are different questions, so record which one each run addresses.
Use an endpoint and screenshot parameters that reflect the traffic you care about. The API provider determines the endpoint path, HTTP method, authentication, rendering options, quotas, costs and rate limits; there is no universal screenshot workload or request mix. Clobbr’s guidance suggests mixing HTTP verbs across runs when trying to represent realistic traffic, but a screenshot API may have a specific request shape. See Clobbr’s REST API load-testing guidance.
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- Set the endpoint and method. Enter the screenshot API’s documented URL and the HTTP method it expects. Do not assume every screenshot service uses the same path or method.
- Add authentication and request data. Supply the required headers and, if the provider requires one, a payload or parameters in its documented format. Clobbr documents support for headers and payloads, while the specific authentication and rendering fields belong to the API provider. Its CLI repository documents the command-line workflow.
- Choose representative screenshot options. Use the rendering settings your application normally sends. A different page, viewport, wait condition or capture option may change the work the service must do, so keep settings fixed when comparing runs.
- Check the test boundary. Know whether you are hitting a test, staging or production endpoint, and check the provider’s usage and rate-limit terms before sending concurrent requests.
Run a sequential baseline
Start with requests sent one after another. Clobbr suggests 100 sequential requests as an initial single-endpoint baseline; this is a starting point from its product guidance, not a universal requirement or a guarantee that the sample represents your real traffic. Adjust the run to the question and the endpoint.
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Record the request count, endpoint environment, relevant request settings, and the result distribution. Capture p50, p95 and p99 latency where available; mean latency can be useful context, but it does not describe slow-tail behavior on its own. Also record successful and failed iterations. Clobbr describes sequential runs as a way to establish response latency and recommends comparing them with a parallel pass at its REST API testing page.
Run a parallel test
Next, configure a parallel run to see how the endpoint behaves when requests overlap. Clobbr suggests 500–1000 parallel requests as a starting range for examining concurrency. The figure is vendor guidance, not an industry standard; select a run size that fits your defined test and the provider’s limits.
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Keep the request and environment the same as the baseline wherever possible, changing concurrency as the intended variable. Compare p95 and other tail percentiles between the sequential and parallel runs, then inspect success percentage and failed iterations. If latency rises sharply or failures appear, investigate the endpoint, its dependencies and provider-side limits rather than inferring a universal capacity threshold: the available guidance establishes no single acceptable change or failure rate.
Compare results and make the conclusion reproducible
For each run, retain enough context to explain what the numbers mean:
- Endpoint environment and request configuration, with secrets excluded.
- Sequential or parallel pattern and iteration count.
- p50, p95 and p99 latency, plus mean if useful.
- Successful and failed request counts or success percentage.
- Any change to the API, test environment or run settings that could affect comparison.
For recurring checks, Clobbr’s CLI documentation describes JSON, YAML and CSV output and checks for quantiles and success percentage, which can support CI thresholds or retained artifacts. Keep credentials out of exported artifacts and CI logs. Clobbr says its local-first workflow keeps request contents and run history on the user’s machine; treat that as Clobbr’s own product statement, and review how your CI environment handles secrets. See the CLI documentation and Clobbr’s product site.
Report a result as a description of that run, not a capacity promise. Include the request pattern, count, environment, observed percentiles and failure rate. A short test describes only its configuration and conditions. In a company account of using Clobbr, ScreenshotOne’s author described a quick investigation prompted by a customer asking whether its API could handle 100 requests per second in parallel; the post does not establish that rate as an achieved or transferable result, and its author distinguished the quick check from a fuller CI test in an isolated production environment. Read the ScreenshotOne account.
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