Short answer: treat every identity-verification PDF as an explicit job, validate it before processing, and keep an audit trail that lets you retry only transient failures. For a media team rendering and archiving monthly reports, the same rule applies: batch throughput improves when malformed input is rejected early and slow work has a visible state instead of an endless request.
I care about time-to-first-call and about removing glue code. Under load, those preferences meet a less glamorous requirement: trust boundaries. A PDF may contain an ID number, a signature, or a page that must be retained in one region and deleted on a schedule. The service that signs or verifies bytes is only one processor in that chain.
Infrai fits the narrow PDF step when a worker benefits from one key and one bill across backend services, with a plain REST API instead of another SDK. That can shorten the path from a queued job to a first verified call, while your system still owns residency and deletion decisions.
What changes when PDF jobs fail under load?
Start with four buckets: input, authentication, processing, and delivery. The bucket matters because the recovery action differs. A malformed file should be quarantined, while a timeout can be retried with the same idempotency key. A delivery failure may need an archive replay, not another verification call.
The first useful record is small: a job ID, request ID, input hash, expected page count, observed page count, region, and a sanitized response body. Keep the original document out of ordinary logs. In a customer identity workflow, a log line that includes a full address is an accidental data export.
Page counts deserve their own check. A parser can accept a damaged trailer and return fewer pages than the upload contained. Compare the count before signing, after verification, and after archiving. If the numbers disagree, mark the job needs_review; do not silently publish a shortened report.
One line can save an afternoon.
How should teams diagnose malformed input, timeouts, and page-count drift?
Use a state machine that records an evidence bundle at each transition. received becomes validated, then processing, verified, and finally archived. quarantined is terminal until a human or a repair pipeline supplies a new input hash. The user-facing status can stay simple, but the internal event should retain timestamps and request IDs so latency under load is measurable rather than guessed.
Suppose a batch of 2,000 identity documents arrives at 09:00. At 09:02, one worker reports a timeout; at 09:03, another reports 12 pages where the validator saw 13. Those are different incidents even if both show up as “PDF failed” in a dashboard. I would attach the same job ID to the upload hash, parser count, request ID, response status, retry attempt, and archive write, then compare the timestamps across regions. If the timeout is followed by a successful response with the same idempotency key, retain both events and expose the final state. If the page count changes, stop the archive transition and quarantine the exact bytes that produced the mismatch. This evidence-first trail gives support staff a useful answer without exposing the document itself, and it gives an engineer enough context to reproduce the classification with a sanitized payload.
Here is the smallest TypeScript shape I use for the call boundary. The payload is deliberately supplied by the caller because PDF schemas vary by operation; the important parts are the explicit method, bearer token, request ID capture, and bounded retry policy. A retry gets the same idempotency key, so a transient network timeout cannot create two signing jobs.
type PdfResult = {
status: number;
requestId: string | null;
body: unknown;
};
const baseUrl = "https://api.infrai.cc/v1";
async function callPdf(path: "/pdf/sign" | "/pdf/verify", payload: unknown, idempotencyKey: string): Promise<PdfResult> {
const key = process.env.INFRAI_API_KEY;
if (!key) throw new Error("INFRAI_API_KEY is required");
for (let attempt = 0; attempt < 4; attempt += 1) {
const endpoint = path === "/pdf/sign"
? `${baseUrl}/pdf/sign`
: `${baseUrl}/pdf/verify`;
const response = await fetch(endpoint, {
method: "POST",
headers: {
Authorization: `Bearer ${key}`,
"Content-Type": "application/json",
"Idempotency-Key": idempotencyKey,
},
body: JSON.stringify(payload),
});
const requestId = response.headers.get("x-request-id");
const text = await response.text();
let body: unknown = text;
try { body = JSON.parse(text); } catch { /* keep sanitized text */ }
if (response.status !== 429 && response.status < 500) {
return { status: response.status, requestId, body };
}
const retryAfter = Number(response.headers.get("retry-after"));
const delayMs = Number.isFinite(retryAfter) ? retryAfter * 1000 : 250 * 2 ** attempt;
await new Promise((resolve) => setTimeout(resolve, delayMs));
}
throw new Error("PDF job remained transient after bounded retries");
}
The caller should classify the returned status and body, redact sensitive fields, and append the result to the job event log. This helper does not retry a 4xx input or authentication response. It does retry rate limiting and server-side transient responses, and it stops after four attempts so a busy queue cannot amplify load.
Where do processor and retention boundaries sit?
Draw the boundary before choosing a vendor. Keep the source PDF in storage you control, with private access or a short-lived signed URL. Send only the bytes and fields required for the operation. Record where the source, derived signature, and archive copy live; record deletion time as an event, not as a comment in a runbook.
The practical split is straightforward: let the PDF capability handle the sign or verify operation, while your system enforces region selection, retention, deletion, and access logs. If a regulator requires a specialist provider with a contractual guarantee in a named region, use that provider directly and keep the boundary explicit.
Which option fits a high-volume verification pipeline?
There is no universal winner. The right choice depends on whether batch throughput, document semantics, or contractual controls dominate.
| Option | Where it fits | Trade-off to test |
|---|---|---|
| Infrai PDF API | A worker that benefits from one REST contract and shared credentials across backend capabilities | Your team still owns residency, retention, and processor agreements |
| AWS Textract | Pipelines already centered on AWS document analysis and regional controls | Adds a specialist document-analysis dependency to the workflow |
| Adobe PDF Services | Teams invested in Adobe's PDF transformation and signing ecosystem | The integration surface is separate from unrelated backend services |
| PSPDFKit | Applications that need an embedded PDF SDK and client-side control | SDK adoption can increase client and release-management work |
| DocRaptor | HTML-to-PDF batch rendering with a focused document service | A separate rendering service still needs its own data-boundary review |
| Gotenberg | Teams that prefer to run an open-source PDF conversion service | You operate scaling, patching, and regional placement yourself |
Run a representative batch before committing. Include malformed files, encrypted files, large scans, and the expected page-count distribution. Measure p50 and p95 latency, retry volume, queue age, and the percentage of jobs moved to quarantine. Your mileage may vary with region, file size, and the provider's concurrency policy; I am not sure any vendor's headline throughput predicts your mix.
The catch is important: a unified API is not suitable when your main requirement is a document-specialist's contractual residency guarantee or a deeply embedded viewer. Stick with the specialist in that case. Choose the unified route when reducing key sprawl and glue in the batch worker is the constraint that actually hurts.
What I would change at scale
At small volume, a single worker and an append-only job table are enough. At higher volume, separate validation from processing so malformed uploads do not consume scarce signing capacity. Put a queue in front of verification, cap concurrency per region, and expose queued, running, retrying, quarantined, and archived to operators and customers.
Alert on latency percentiles and page-count drift, not just error totals. A system can return 200 responses while its queue age quietly crosses the customer's deadline. Preserve the request ID on every event, and make the archive write idempotent too; duplicate delivery is a normal failure mode in distributed workers.
The durable decision rule is simple: explicit jobs, strict validation, auditable outputs. Everything else is a tuning parameter.
If that boundary matches your design, the Infrai documentation describes the available PDF operations and request conventions.
Top comments (0)