sizeless vs Unstructured: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of sizeless and Unstructured — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
sizeless
sizeless
Turns a smartphone video of an open trench into a centimetre-accurate 3D point cloud, CAD as-built plan and GIS-ready digital twin of buried utilities.
Key features
- Smartphone capture: Field crews record an open trench with a standard iPhone Pro — no specialist scanning hardware and no separate surveying appointment
- Centimetre-accurate point clouds: Reconstruction algorithms developed at ETH Zurich build a high-resolution 3D point cloud of the excavation from the video alone
- Standards-compliant CAD output: Generates as-built plans in DWG and DXF, with couplings and pipe runs identified and measurements simplified
- 3D digital twin and GIS export: Produces a model of the pipe route including building entries that drops into existing GIS systems
- Works without GPS: Captures basement sections and building entry points where GNSS-based surveying fails
- Immediate backfilling: Because capture takes minutes, trenches close right after filming instead of waiting on a survey crew
- Documentation in about 72 hours: Complete records arrive weeks earlier than conventional surveying, enabling prompt connection billing
- Third-party utility capture: Records crossing utilities and as-laid geometry as unbroken 3D evidence, replacing hand sketches
Best for
- A utility network operator documenting residential service connections without booking a surveyor for every site
- A contractor closing a trench the same day instead of leaving it open pending a survey appointment
- Capturing a building entry point in a basement where GPS-based surveying cannot get a fix
- A district heating project producing as-built DWG plans for regulatory sign-off
- Spotting a laying error in the 3D point cloud before backfilling, while the fix is still cheap
- Feeding as-built pipe geometry into a GIS system for long-term network maintenance planning
Unstructured
Unstructured
Open-source ETL platform that converts complex documents into structured data for LLMs and GenAI workflows.
Key features
- Multi-format Ingestion: Supports a broad set of input types (PDF, HTML, DOCX, PPTX, XLSX, EPUB, images, emails, CSV/TSV, compressed archives) to ingest documents from varied sources and normalize them for downstream processing.
- Modular Bricks and SDKs: Provides reusable, open-source building blocks (bricks) and language SDKs to assemble custom preprocessing pipelines for parsing, cleaning, and transforming document content.
- Pipeline Orchestration & Enrichments: Routes data through dynamic transformation pipelines that perform partitioning, enrichment, metadata extraction, and content normalization to produce structured outputs tailored for LLMs.
- Layout Parsing & Chipper Model: Includes layout and document structure analysis (layout parsing) to extract tables, figures, headings, and positional context from complex page layouts for accurate content segmentation.
- Chunking & Embedding Preparation: Implements intelligent chunking and embedding generation workflows to create LLM-friendly segments and vectors, improving retrieval, RAG, and semantic search performance.
- Hosted API & Local Libraries: Offers a hosted Unstructured API (API keys required) for cloud-based processing alongside open-source local libraries for on-prem or custom deployments, enabling flexible integration models.
- Enterprise Platform Capabilities: Provides production-grade Platform features—continuous ingestion, monitoring, partitioning strategies, and scalability—targeted at enterprise workflows and compliance needs.
- File-type Analytics & Metrics: Collects analytics on processed document types and transformation success to help operators measure ingestion quality and pipeline performance.
- Convert documents to structured data (supports PDFs, HTML, Word, images, tables, graphs)
- Modular components ("bricks") for building custom preprocessing pipelines
- Dynamic transformation and enrichment pipelines for routing and improving data quality
- Partitioning and chunking to prepare content for LLM consumption
- Embedding support and integration points for vectorization
- Layout parsing and inference models (separate inference repository)
- Python SDK and libraries (unstructured, unstructured-api, unstructured-inference)
- Containerized deployment options (Dockerfile present in repo) and Makefile-driven install
- Apache-2.0 open-source licensing for core libraries
- Enterprise Platform for production-grade workflows, continuous automated processing and scaling
Best for
- Preparing LLM Training & RAG Corpora: Clean, partition, and chunk large collections of PDFs, manuals, and reports into semantically coherent passages and embeddings for retrieval-augmented generation and model fine-tuning.
- Automated Document Ingestion for Knowledge Bases: Continuously ingest and transform new documents (contracts, policies, manuals) into structured records for searchable knowledge bases and Q&A assistants.
- Table and Figure Extraction for Data Pipelines: Parse complex tables, figures, and embedded images from financial reports or scientific papers to convert them into structured datasets for analytics or downstream models.
- Compliance and Contract Analysis: Extract clauses, metadata, and named entities from legal and regulatory documents to populate contract management systems and support compliance workflows.
- Invoice/Receipt Processing: Normalize and extract line-items, totals, dates, and vendor information from invoices and receipts to automate AP workflows and accounting ingestion.
- Migration of Legacy Documents: Convert large legacy document collections (scanned PDFs, archived emails, disparate formats) into structured, searchable formats to modernize enterprise data stores.
- Prototype to Production Pipelines: Use open-source bricks to prototype document parsing locally, then scale to the Unstructured Platform for continuous, monitored production processing with enterprise controls.
- Preprocessing document corpora to create high-quality input for retrieval-augmented generation (RAG) pipelines
- Extracting tables, figures, and structured fields from PDFs and scanned documents
- Continuous ingestion and enrichment of enterprise documents for knowledge bases
- Generating embeddings and chunked passages for semantic search over documents
- Receipt, invoice, and financial filings parsing (example pipelines and archived repos exist)
- Building document Q&A or chatbot applications using cleaned, structured document content