Unstructured vs. Carbon: A Comprehensive Comparison for Document Processing

If your primary goal is to prepare unstructured data for AI applications, the Unstructured Platform is a strong choice. It offers a no-code solution for converting raw documents like PDFs, emails, and scanned files into structured, machine-readable formats, making it perfect for Retrieval-Augmented Generation (RAG) systems and enterprise data pipelines.

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What is Unstructured?

The Unstructured Platform is a specialized solution designed for transforming unstructured data—such as PDFs, emails, and scanned documents—into structured, machine-readable formats. It supports various document processing workflows, making it an ideal choice for AI applications, Retrieval-Augmented Generation (RAG) systems, and enterprise data pipelines.

Key Features of Unstructured

• No-Code Data Processing: Enables users to convert raw unstructured data into a structured format without writing custom code.

• Diverse Data Source Support: Connects to cloud storage services (AWS S3, Azure Blob, GCP), databases (Databricks, Elasticsearch, OpenSearch), and enterprise platforms (Salesforce, Google Drive, SharePoint).

• Advanced Partitioning & Chunking: Uses multiple partitioning strategies (Fast, HiRes, Auto) and intelligent chunking methods (By Title, By Page, By Similarity) to optimize content extraction.

• AI-Powered Enrichment: Generates metadata, captions, and embeddings for AI-driven document retrieval and analysis.

• Vector Database Integration: Seamlessly integrates with Pinecone, Weaviate, Chroma, Elasticsearch, OpenSearch, and other storage destinations.

• Scalability for Enterprise AI: Designed to handle high-volume ETL workloads.

Workflow Orchestration Engine

The platform’s orchestration layer manages complex scheduling, automatic retries, and parallel processing of over 53,000 documents per job while maintaining millisecond latency between processing steps. Unlike limited frameworks that focus solely on specific tasks, Unstructured provides end-to-end orchestration capabilities, including:

• Real-time document detection with automated triggering of processing pipelines.

• Intelligent incremental updates that reprocess only modified content.

• Horizontal scaling across multiple data planes in hybrid cloud environments.

• Embedded metadata governance tracking data lineage from source to vector store.

Enterprise Scalability

Performance benchmarks indicate that the hosted SaaS deployment processes over 15 million pages per hour per workflow, with proven scalability to petabytes of unstructured data. For organizations requiring full control, the in-VPC deployment model eliminates data egress costs while providing unlimited scaling based on private infrastructure capacity. This architecture supports multi-region processing with centralized governance, essential for global enterprises managing localized data residency requirements.

Enterprise Integrations

With over 71 pre-built connectors spanning storage systems, LLM providers, and vector databases, Unstructured Platform acts as the central nervous system for GenAI data pipelines. Current production integrations include direct access to OpenAI and Anthropic models for embeddings and enrichment, with expanded model support scheduled for Q2 2025. The platform’s API-first design allows custom integration with any third-party service while maintaining SOC 2 Type 2 compliance across all data flows.

Read more about how Unstructured can help you do Production-Ready data processing for GenAI here.

What is Carbon?

Carbon is a platform designed to streamline unstructured data ingestion and retrieval for generative AI applications. It focuses on preparing data for Large Language Models (LLMs) by offering tools for chunking, embedding generation, and hybrid search capabilities. Carbon’s features are particularly suited for RAG workflows and vector database integrations.

Key Features of Carbon

• Data Integration: Supports over 20 native connectors and file formats, including text, audio, and visual data.

• OCR and Table Parsing: Uses OCR to enhance table extraction from PDFs and scanned documents.

• Hybrid Search: Combines semantic and keyword search for precise content retrieval in vector databases.

• Embedding Generation: Provides flexibility in selecting embedding models and chunking strategies.

• Data Security: Offers SOC 2 Type II compliance with strict data privacy measures.

• Deployment Options: Allows users to choose between managed vector databases or self-hosted solutions.

Unstructured vs. Carbon: A Feature Comparison

FeatureUnstructured PlatformCarbonPrimary FunctionDocument processing for structured outputData ingestion and retrieval for LLMsData Sources Supported30+ connectors20+ connectorsAI CapabilitiesCanonical JSON schema, enrichmentEmbedding generation, hybrid searchIntegrationVector DBs, LLM frameworksManaged/self-hosted vector databasesBest ForPreparing unstructured data for AI workflowsEnhancing RAG workflowsPrimary FunctionDocument processing for structured outputData ingestion and retrieval for LLMs

Why Choose Unstructured?

Both the Unstructured Platform and Carbon offer powerful solutions for handling unstructured data but are tailored to slightly different needs. The Unstructured Platform excels in providing a complete pipeline for transforming raw documents into structured formats ready for enterprise AI workflows. Its no-code interface, extensive integrations, and advanced processing tiers make it an excellent choice for organizations looking to streamline large-scale document processing.

At Unstructured, we're committed to simplifying the process of preparing unstructured data for AI applications. Our platform empowers you to transform raw, complex data into structured, machine-readable formats, enabling seamless integration with your AI ecosystem. To experience the benefits of Unstructured firsthand, get started today and let us help you unleash the full potential of your unstructured data.