Question 1

olmOCR or Paperless-ngx: which should a small business choose?

Accepted Answer

Choose olmOCR if you want: fewer license conditions (Permissive vs Copyleft (GPL)) and simpler deploy (one container vs a multi-container stack). Choose Paperless-ngx if you want: no GPU server needed, an actively maintained project (olmOCR is stale) and larger community (47.6k vs 19.8k GitHub stars). Paperless-ngx publishes no official RAM figure, so we cannot compare hosting cost directly.

Question 2

Which is cheaper to self-host, olmOCR or Paperless-ngx?

Accepted Answer

olmOCR: GPU server (not estimated). Paperless-ngx: not estimated (no official RAM figure). Our estimate is the cheapest Hetzner Cloud plan that meets each project's official RAM and vCPU figure; it covers the server only.

Question 3

How much hardware do olmOCR and Paperless-ngx need?

Accepted Answer

olmOCR: not published. Paperless-ngx: not published. olmOCR needs a GPU for practical use.

Question 4

What is the license difference between olmOCR and Paperless-ngx?

Accepted Answer

olmOCR: Apache-2.0 (Permissive). Permissive. Internal and commercial use, modification and redistribution allowed; keep the license and notices. Paperless-ngx: GPL-3.0 (Copyleft (GPL)). Free for internal and commercial use. If you distribute a modified version you must release it under GPL-3.0; running it for your own team is not distribution. This is a summary, not legal advice.

Question 5

Is olmOCR or Paperless-ngx more actively maintained?

Accepted Answer

olmOCR: Stale (no commit in 180+ days), last commit Mar 25, 2026. Paperless-ngx: Active, last commit Nov 3, 2026. GitHub data refreshed Nov 3, 2026.

Question 6

Which is easier to deploy, olmOCR or Paperless-ngx?

Accepted Answer

olmOCR installs via Docker (GPU image with model included) (single container); Paperless-ngx via Docker Compose (official install script) (multi-container stack). olmOCR lists 3 known gotchas and Paperless-ngx lists 4; see each app page.

	olmOCR	Paperless-ngx
Category	Document processing	Document processing
What it is	Ai2's vision-language OCR that turns PDFs and scans into clean Markdown, including tables, equations and handwriting.	Document management system that OCRs scans into a searchable archive, auto-tags them and adds optional LLM suggestions and chat.
License	Apache-2.0	GPL-3.0
License type	Permissive	Copyleft (GPL)
License conditions	Permissive. Internal and commercial use, modification and redistribution allowed; keep the license and notices.	Free for internal and commercial use. If you distribute a modified version you must release it under GPL-3.0; running it for your own team is not distribution.
Status	Stale (no commit in 180+ days)	Active
GitHub stars	19.8k	47.6k
Last commit	Mar 25, 2026	Nov 3, 2026
Latest release	v0.4.27	v3.4.1
Minimum hardware	30 GB disk	Not published
Recommended hardware	Not published	Not published
RAM measured by us	Not tested yet	Not tested yet
GPU	Required for local inference: a recent NVIDIA GPU with at least 12 GB of GPU RAM (tested on RTX 4090, L40S, A100, H100). Without a GPU you can point it at a remote vLLM or OpenAI-compatible server.	No. OCR and the built-in classifier run on CPU; LLM features call the provider you configure.
Est. server cost	GPU server (not estimated)	Not estimated
Deploy method	Docker (GPU image with model included)	Docker Compose (official install script)
Known gotchas	3 listed	4 listed
Telemetry	Not documented	Not documented
Calls home by default	Not documented	Not documented
SSO (OIDC / SAML / LDAP)	Not documented	Free (OIDC free)
Audit log	Not documented	Yes, free
RBAC	Not documented	Not documented
Details	Full olmOCR guide →	Full Paperless-ngx guide →

olmOCR vs Paperless-ngx

Short answer