Unstructured
Transform complex documents and unstructured data into clean, structured formats ready for AI applications.
- Also known as
- unstructured
Available worldwide · Popular in: US, GB, EU
What is Unstructured?
Unstructured provides enterprise-grade data preprocessing and ETL capabilities that convert messy, unstructured documents (PDFs, images, tables, etc.) into machine-readable formats optimized for language models and AI workflows. It handles data ingestion, transformation, enrichment, and embedding across multiple sources and destinations with built-in security and compliance.
Unstructured pricing
Plans, per-tier features and add-ons, dated and linked to live pricing. Pricing changes often; always verify at source before you rely on it.
Free tier with 15,000 pages/month. Pay-as-you-go at $0.03/page with $3,000 monthly cap. Custom enterprise pricing available.
Free
FreeMonthly free allowance with automatic resets
- pages
- 15,000/month
- All features included
Pay-As-You-Go
Usage-based pricing after free monthly allowance
- All features included
Business
Contact salesEnterprise deployment with dedicated infrastructure and support
- Multi-user accounts
- Dedicated Instance or VPC
- Full data isolation
- 24/7 technical support
What Unstructured does
The capabilities that matter for document ai, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Prebuilt models
- ✓
- Custom model training
- ✓
- Table extraction
- ✓
- Handwriting recognition
- ✗
- Id document parsing
- ✗
- Human in the loop review
- ✗
- Confidence scoring
- ✗
- LLM vlm based extraction
- ✓
- RPA ERP integration
- ✗
- Open source
- ✓
Platform & deployment
Independently observed- CLI
- Web
- Cloud / SaaS
- On-premise
- Self-hosted
Integrations (44)
Independently observed- Airtable
- Google Cloud Storage
- Google Drive
- Jira
- PostgreSQL
- S3
- Salesforce
- SFTP
- Slack
- Snowflake
- SQLite
- Zendesk
- Amazon Bedrock
- Astra DB
- DuckDB
- Elasticsearch
- IBM Milvus
- IBM watsonx.data
- Weaviate
- OpenAI
- Anthropic
- Azure OpenAI
- Google Gemini
- AWS Bedrock
- IBM watsonx
- NVIDIA
- Together.ai
- VertexAI
- Cohere
- Amazon S3
- Azure Blob Storage
- Databricks
- OpenSearch
- Neo4j
- Kafka
- Box
- OneDrive
- Confluence
- Dropbox
- LangChain
- SAP
- Teradata
- Cursor
- Codex
Unstructured alternatives
Other document ai we track, ranked by the same independent score.
- MindeeCloud-based machine learning API that automatically extracts structured data from documentslow · 43%
- NanonetsAutomate enterprise workflows by converting business processes into autonomous AI agentsmedium · 52%
- Doclinglow · 32%
- HyperscienceEnterprise AI platform for intelligent document automationlow · 13%
- RossumAI-powered platform that automates reading, validating, and processing transactional documents end-to-endlow · 41%
- PaddleOCRlow · 16%
Compare Unstructured
Side by side against other document ai, attribute by attribute, with a source on every value.
The Vioscale score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Unstructured, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Pricing transparency | 100 | 0.08 | 8.4 | ✓ |
| Security posture | 80 | 0.07 | 5.8 | ✓ |
| Capabilities | 100 | 0.05 | 4.9 | ✓ |
| Price level | 80 | 0.05 | 4.2 | ✓ |
| Integrations | 46 | 0.04 | 1.9 | ✓ |
| Reliability | 0 | 0.07 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Open source: Yes · Soc2 type ii: Yes · Pricing model: pay_per_page_api · Prebuilt models: Yes · Table extraction: Yes · Architecture model: managed_cloud_api | mediumsource · 2026-08-21 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 40 | mediumsource · 2026-08-21 · 60% |
Market
| Attribute | Value | Evidence |
|---|---|---|
| Availability | HqCountry: US · PrimaryMarkets: … · AvailabilityScope: global · AvailableCountries: … · NotAvailableCountries: … | highsource · 2026-08-21 · 75% |