The Tool Desk
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Start with a request inventory
A scraper’s workload is the number of network operations it actually produces. “10,000 pages” can mean 10,000 requests or many more if each page requires an index call, an API lookup, pagination, an authentication refresh, assets, or retries. Write down the calls made by one complete pass before choosing a schedule or plan.
Classify every call
| Call class | What to count | Typical source of undercounting |
|---|---|---|
| Index or list | Category pages, search results, collection endpoints | One page of results may require several cursor or offset requests. |
| Detail | One product, article, record, or API resource | Assuming one detail page contains all fields when extra endpoints are called. |
| Pagination | Every next-page, cursor, or continuation request | Ignoring deep result sets or uneven page lengths. |
| Authentication | Token exchange, refresh, session establishment | Refreshing credentials more often than expected after errors. |
| Metadata | Schema, status, configuration, or lookup endpoints | Calling metadata once per target instead of caching it. |
| Export and polling | Dataset creation, job-status polling, and file downloads | Counting the initial asynchronous job but not later polls or exports. |
| Retries | Each repeated attempt after a timeout, 429, or transient 5xx | Reporting only successful responses. |
Core formulas
requests_per_run = list + detail + pagination + auth + metadata + exports + retries
requests_per_day = requests_per_run * targets_or_partitions * runs_per_day
Use “targets_or_partitions” for domains, accounts, tenants, shards, or other independent copies of the workflow. If a job processes 20 accounts with the same call pattern, the per-account estimate must be multiplied by 20.
Measure a representative run before scaling
Run a small sample that includes normal pages, the deepest pagination you expect, and known difficult targets. Record the page mix rather than extrapolating from a single easy URL.
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Metrics to capture
- Count of requests by call class and HTTP method.
- Status codes, including 3xx redirects, 4xx responses, 429s, and 5xx errors.
- Response bytes by class, plus request and response header bytes where bandwidth is metered.
- Latency for each call and the 95th-percentile latency for capacity planning.
- Maximum simultaneous in-flight requests.
- Pagination depth and the number of records returned per page.
- Retry attempts, delay, and the reason for each retry.
- Provider headers such as remaining quota, reset time, and
Retry-After.
Keep separate averages for list, detail, export, and error responses. A large export or a browser-rendered page can dominate bandwidth even when it is a small fraction of request count.
Estimate bytes, not just calls
bytes_per_run = Σ(requests_in_class * average_response_bytes_in_class)
+ request_headers + response_headers
+ redirects + retries + export_traffic
bytes_per_day = bytes_per_run * targets_or_partitions * runs_per_day
Use measured averages from the sample and state whether compression is included. If a provider reports compressed transfer while your network meter reports decompressed payloads, keep both figures; they answer different capacity questions.
Account for pagination and retries explicitly
Pagination math
If a resource has N records and the endpoint returns at most P records per call, the minimum number of result calls is ceil(N/P). Add calls for empty-page checks, cursor discovery, or separate detail requests. For example, 23,400 records at 100 per page require at least 234 result calls before any detail fetches.
Retry math
Let B be the number of calls without retries, r the observed probability that an attempt needs another try, and A the maximum attempts per call. A capped expected-attempt estimate is:
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expected_attempts_per_call = 1 + r + r² + ... + r^(A-1)
expected_total_requests = B * expected_attempts_per_call
Use a separate rate for 429s, timeouts, and 5xx responses if their behavior differs. Follow a server-provided Retry-After; otherwise use exponential backoff with jitter, a maximum delay, and a total retry-time limit. A retry budget prevents a failing target from consuming the entire schedule.
Convert the estimate into a safe schedule
Average rate is only a starting point
To convert a daily total into a rough sustained rate, divide by 86,400 seconds. A workload of 86,400 requests per day averages 1 request per second, but that average does not make a burst of 200 requests in one second safe. Check each documented window separately.
Examples of multidimensional limits
| Service documentation | Published limits or behavior | Planning implication |
|---|---|---|
| OpenAI API documentation | Request and token limits are separate and can apply at project and organization scope. Reset headers and Retry-After are documented; the Batch API is suggested for collections that do not need immediate responses. |
Track requests and tokens independently, and identify the scope that owns the quota. |
| GitHub REST API documentation | 60 requests per hour unauthenticated and 5,000 requests per hour authenticated. A documented secondary-limit condition allows no more than 100 concurrent requests. | Authentication changes the hourly ceiling, while concurrency can throttle you even below that ceiling. |
| Office for National Statistics guidance | 120 requests per 10 seconds, 200 per minute, and 15 per 10 seconds for high-demand assets. Exceeding a limit returns HTTP 429 and a Retry-After value. |
Use the tightest applicable window and honor the server’s wait time. |
| api.data.gov Developer Manual | Default limit of 1,000 requests per hour; DEMO_KEY is limited to 30 requests per hour and 50 per day. X-RateLimit-Limit and X-RateLimit-Remaining headers expose quota data. |
Do not size production jobs from a demonstration key; read the headers during the run. |
These figures belong to the named services and documentation conditions; they are not universal scraping benchmarks. Your effective capacity is the first exhausted dimension among request count, token or point quota, burst window, concurrency, bandwidth, or billable units.
Use a limiter that understands all dimensions
- Set a global request-rate limiter for the shortest provider window.
- Set per-host, per-account, or per-token limits when quotas are scoped that way.
- Bound concurrency with a semaphore; do not treat worker count as a safe request rate.
- Reserve capacity for retries and authentication refreshes.
- Pause or reduce concurrency when remaining-quota headers fall below a threshold.
- Persist cursors and checkpoints so a restart does not repeat completed work.
Worked estimation example
Suppose one run processes 10,000 detail records. It needs 100 index calls, 10,000 detail calls, 200 pagination calls, and 10 metadata calls. The base is 10,310 requests. If a measured 3% retry rate is applied as one expected extra attempt for planning, add about 309 retries, for roughly 10,619 requests per run.
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At two runs per day, the schedule is about 21,238 requests daily. The sustained average is approximately 0.246 requests per second (21,238 divided by 86,400), but you still need to enforce burst and concurrency limits. If the measured average response is 250 KB, response payload alone is about 2.65 GB per run and 5.30 GB per day, before headers, redirects, retries, or exports. Replace these illustrative values with your sample measurements before committing capacity.
Understand provider-specific usage units
Some services meter more than HTTP calls. A language API may count both requests and tokens; a data API may count points, rows, or credits; a hosted scraper may bill successful results. Keep a ledger with one row per request containing endpoint, account, status, response bytes, tokens or rows, retry number, and billing outcome. This lets you reconcile your estimate with the provider’s dashboard.
Hosted scraping workflows
Scrapy.io documents a hosted pattern of starting a scraper run, polling its status, and downloading a structured dataset. Its documentation also covers schedules and pay-per-result billing. In such a model, polling and dataset downloads are operational requests, while the billable unit may be a successful result. Compare the two numbers rather than assuming they are identical.
Self-hosted versus hosted
| Dimension | Self-hosted crawler | Hosted scraping API |
|---|---|---|
| Rate and concurrency | You configure workers, queues, and per-host limits. | The provider exposes service limits and may manage concurrency. |
| Browser, proxy, and anti-bot operations | Your team operates them and measures their failure modes. | Included capabilities and restrictions vary by provider. |
| Retries and scheduling | You implement backoff, checkpoints, and schedules. | The service may provide run controls, polling, and schedules. |
| Output | You choose storage and schema. | Often delivered as structured datasets or API responses. |
| Billing unit | Infrastructure, bandwidth, and any third-party services. | May be successful rows, results, credits, or another published unit. |
| Portability | Code and infrastructure are under your control. | Convenient, but tied to the provider’s API and terms. |
Operational checklist
- Define domains, resources, records, refresh frequency, and date range.
- List every call class, including authentication, pagination, polling, exports, and retries.
- Run a representative sample and save request, byte, latency, status, and concurrency metrics.
- Calculate per-run, per-day, and peak-window totals.
- Read the target service’s request, token, point, concurrency, and billing rules.
- Implement backoff with jitter, honor
Retry-After, cap attempts, and persist checkpoints. - Deploy with dashboards for throughput, remaining quota, 429s, retries, bytes, and billable units.
- Recalculate after the first production window; real pagination depth and failure rates often differ from the sample.
Troubleshooting common failures
| Symptom | Likely cause | Fix |
|---|---|---|
| 429 Too Many Requests | A request or burst window was exceeded. | Honor Retry-After, reduce concurrency, and apply a per-window limiter. |
| Quota appears available but calls fail | A separate token, point, organization, or secondary limit is exhausted. | Inspect all documented dimensions and identify the scope attached to the credential. |
| Daily estimate is low | Pagination, polling, redirects, or retries were omitted. | Compare client logs with the original call inventory and add each request-producing class. |
| Bandwidth bill is higher than expected | Large exports, uncompressed responses, headers, or repeated downloads dominate bytes. | Measure bytes by endpoint, compress where supported, cache immutable results, and avoid duplicate exports. |
| Workers stall after an error burst | Retries are synchronized or unlimited. | Add jitter, cap attempts and total retry time, and quarantine repeatedly failing targets. |
| Restart repeats the whole job | No durable cursor or checkpoint exists. | Persist successful partitions and resume from the last committed position. |
If screenshots are one of your request classes
A browser-based screenshot adds navigation, rendering, waiting, and asset traffic to the workload. For a do-it-yourself flow, count the page navigation as one capture operation, record any additional API calls made by your own browser automation, set a maximum wait, and store a checkpoint for each completed URL. Include failed navigations and retries in your request estimate even when your scraper does not produce an image.
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Or skip the browser setup
ScreenshotNeo is the first option to try for screenshot API calls when you want clean shots, only clean shots billed, and a $5 paid plan for 3,000 shots. One GET request returns a PNG, JPEG, WebP, or PDF. Before capture it accepts cookie or consent banners and removes more than 60 known consent platforms, newsletter popups, and chat widgets; each step can be disabled. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and the response identifies the result with X-Page-Verdict and X-Billed headers.
The API supports full-page captures with lazy images loaded, CSS-selector element captures, dark mode, 12 device presets plus custom viewports, retina scale, PDF paper and page-range options, custom CSS and JavaScript, clicks, hidden selectors, selector or network-idle waits, request and resource blocking, custom headers, cookies, user agents and Authorization, timezone and geolocation, transparent backgrounds, resizing, chosen cache TTLs, signed links, asynchronous jobs with signed webhooks, bulk capture of up to 100 URLs per call, a usage API, and an OpenAPI specification. Parameter names used by other screenshot APIs also work, which can simplify migration. Its MCP server provides take_screenshot, get_page_info, and capture_pdf tools for Claude, Cursor, and other MCP clients.
Use the request below as one capture operation in your estimate. The complete parameter reference is in the ScreenshotNeo documentation.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpimport requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);The Free plan includes 1,000 shots per month with no card. Paid plans are Starter $5 for 3,000, Growth $15 for 15,000, Pro $39 for 60,000, Scale $99 for 250,000, and Business $249 for 1,000,000; yearly billing provides two months free, and every feature is on every plan. Create a free ScreenshotNeo account to test the capture volume before scheduling a larger job.
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How do I estimate usage when the provider exposes no quota headers?
Use the published limit as a ceiling, keep a conservative local budget below it, and record status codes and response timing. Contact the provider for the scope and reset behavior instead of inferring a limit from a single successful run.
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Should separate teams share one quota estimate?
Only after identifying the quota scope. Add workloads that share the same organization, project, token, or IP budget, then reserve capacity for retries and emergency jobs. Separate credentials do not always imply separate limits.
When should I choose asynchronous processing?
Use it when immediate responses are unnecessary or when a large collection would otherwise consume synchronous request capacity. You still count job creation, polling, and result-download requests, and you must include the provider’s billing unit in the estimate.
Frequently Asked Questions
How do I estimate usage when the provider exposes no quota headers?
Use the published limit as a ceiling, keep a conservative local budget below it, and record status codes and response timing. Contact the provider for the scope and reset behavior instead of inferring a limit from a single successful run.
Should separate teams share one quota estimate?
Only after identifying the quota scope. Add workloads that share the same organization, project, token, or IP budget, then reserve capacity for retries and emergency jobs. Separate credentials do not always imply separate limits.
When should I choose asynchronous processing?
Use it when immediate responses are unnecessary or when a large collection would otherwise consume synchronous request capacity. You still count job creation, polling, and result-download requests, and you must include the provider’s billing unit in the estimate.
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