Requirements & sizing¶
The app itself¶
The backend is I/O-bound: it waits on the LLM and OCR endpoints and does very little compute of its own. Nothing is persisted, so there is no storage to size.
| Minimum | Comfortable | |
|---|---|---|
| CPU | 2 cores | 4 cores |
| RAM | 2 GB | 4 GB |
| Disk | ~2 GB for the images | — |
| GPU | none | only for a co-located OCR/LLM sidecar |
RAM scales with concurrent documents, not with corpus size. The dominant consumers are the rendered page images of a scanned PDF during OCR and the detection cache (bounded at 100 entries / 15 minutes).
Supported platforms: Linux with Docker or Podman. macOS works for development.
What you actually need to size¶
The models, not the app:
- Detection LLM — an OpenAI-compatible endpoint. Throughput here sets your
throughput: a document is split into chunks, and every chunk runs
LLM_DETECTION_PASSEStimes (default 2), plus one re-check call per document.LLM_MAX_CONCURRENT_REQUESTS(default 4) caps the total in flight across all documents. - OCR — only if you process scans. A vision-LLM engine needs a GPU;
docling-serve with Tesseract runs on CPU.
VISION_OCR_MAX_CONCURRENT_PAGES(default 2) caps page requests globally.
See LLM endpoints and OCR engines.
Network¶
- Users →
frontend(port 8080 by default), behind your own auth proxy. backend→ the configured LLM/OCR endpoints. Nothing else.- No outbound internet access is required at runtime. There is no telemetry, no CDN, and no third-party font or script.
The backend container publishes no port at all: nginx in the frontend
container proxies /api/ to it over the compose network.
Browsers¶
Any current Chromium, Firefox, or Safari. The redacted-PDF preview uses the browser's built-in PDF viewer; where that is unavailable the download still works.
Before you process real data¶
Technical requirements are the easy part. Also settle:
- Who the data controller is and which legal basis applies (DPIA template).
- Where document content is allowed to flow (Data flow).
- Who reviews the output, and against what standard (Security overview).
- How well the pipeline performs on your document types (Evaluation).