Docling vs Unstructured
Both are alternatives to AWS Textract. Here's how they stack up — verified facts, no spin.
Also searched as Unstructured vs Docling — same comparison, one verdict.
Docling and Unstructured are closely matched on ownership (94 vs 90) — this one comes down to pricing and to which trade-offs below you can live with.
Docling
TOP PICKIBM's document converter. Layout-aware PDF to clean Markdown, MIT.
Docling parses PDFs, Office documents, images and HTML into a structured representation that preserves reading order, tables, figures and headings, then exports to Markdown or JSON. It uses purpose-trained layout and table models rather than heuristics, which is why it holds up on multi-column academic papers and financial statements where simpler extractors interleave columns into nonsense. It integrates directly with LlamaIndex and Haystack, is MIT licensed, and runs entirely locally including on CPU.
Unstructured
One interface for every document format you will actually be handed.
Unstructured normalises an unusually wide range of inputs — PDF, Word, PowerPoint, Excel, HTML, email, EPUB, images — into a consistent element structure of titles, narrative text, tables and lists, ready for chunking. Breadth is the point: real corpora are never one format, and writing a parser per type is where ingestion projects stall. The open library is Apache-2.0 and runs locally; the company also sells a hosted API with additional models.
Side by side
10 points of comparison, every one read from a verified field. Green marks the side that wins a row outright. A dash means we do not hold that fact — never that it is zero.
| Docling | Unstructured | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 94 | 90 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first data | Yes | Yes |
| License | MIT | Apache-2.0 |
| Pricing | Free, MIT. Runs on your own hardware, CPU or GPU. | Open library free under Apache-2.0; a paid hosted API is offered separately. |
| RAM to run it wellThe figure that actually matters, not the vendor's minimum. | 8 GB — the layout models are the memory cost | 8 GB with the high-resolution strategy |
| Realistic running costWhat the box costs each month if you run it yourself. | $0 on your own hardware. A 100,000-page corpus is a weekend of compute against a four-figure Textract invoice. | $0 for the open library; their hosted API is priced per page |
| Setup timeHonest first-install estimate, not the marketing quickstart. | 1 hour | 2 hours, mostly fighting system dependencies |
| Ongoing maintenanceThe part nobody budgets for. | Low. | Moderate. The dependency surface is the maintenance. |
Docling is Macrostack's recommended AWS Textract alternative, so it's our pick here.
Weighing both against staying on AWS Textract? Is AWS Textract free? What it actually costs →
Docling
Strengths
- +Layout-aware — preserves reading order, tables and structure
- +Outputs clean Markdown/JSON that drops into a RAG pipeline
- +Direct integrations with LlamaIndex and Haystack
- +MIT, fully local, no per-page cost
Trade-offs
- −Slower per page than cloud OCR on very large batches
- −Handwriting support is weak compared with Textract
- −No specialised invoice or receipt models
Unstructured
Strengths
- +Widest input-format coverage of anything here
- +Consistent element output regardless of source format
- +Chunking strategies built in for RAG pipelines
- +Apache-2.0 open library
Trade-offs
- −Best-quality models sit in the paid hosted tier
- −Local install pulls in heavy system dependencies
- −Depth on complex PDFs is below Docling's
Which one fits you
The trade-offs above, turned into a decision. Find the line that describes your team.
Choose Docling
if a lower exit cost matters more to you than any single feature, and layout-aware — preserves reading order, tables and structure.
Choose Unstructured
if widest input-format coverage of anything here.
Neither, yet
if both carry a real cost you should weigh first — slower per page than cloud OCR on very large batches, and best-quality models sit in the paid hosted tier. If either of those is a dealbreaker for your team, the shortlist is wrong rather than the choice.
What it takes to run these yourself
Real requirements and honest running costs, not the vendor quickstart.
Self-hosting Docling
8 GB — the layout models are the memory cost RAM · 1 hour
$0 on your own hardware. A 100,000-page corpus is a weekend of compute against a four-figure Textract invoice.
Self-hosting Unstructured
8 GB with the high-resolution strategy RAM · 2 hours, mostly fighting system dependencies
$0 for the open library; their hosted API is priced per page
Docling vs Unstructured — common questions
Is Docling a better fit than Unstructured for document ai & ocr?
It depends on what you are optimising for, and the honest split is this: Docling scores 94 to Unstructured's 90 on data ownership and exit cost, so it is the safer choice if you care about being able to leave. Unstructured earns its place on a different axis — widest input-format coverage of anything here. Neither is a wrong answer for every team; the table above is the actual comparison.
What happens if we want to switch later?
Docling keeps its data local or in open formats, so leaving is an export rather than a negotiation. Unstructured is still self-hostable, so the files stay on your server either way — but it is not local-first by design, so check what its export produces before you rely on it.
Can I self-host Docling or Unstructured?
Both can be self-hosted. The difference is what it costs you in time rather than whether it is possible — see the setup and maintenance rows above.
Are Docling and Unstructured both alternatives to AWS Textract?
Yes — both appear in our AWS Textract comparison, which is why they are worth putting side by side. People usually arrive here already having decided to move off AWS Textract and now choosing between the two replacements, which is a narrower and much easier question.
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.