Self-Hosted Document AI: The Complete Buyer's Guide (2026)

If you search for "self-hosted document AI" today, you will find Reddit threads debating open-source stacks, GitHub repos with half-finished Docker configurations, and tutorials that assume you have a spare A100 in your closet. What you will not find is a clear buyer's guide for the IT director or DevOps lead who needs a production-ready platform, not a weekend project.

This guide is for that buyer. It covers what self-hosted document AI actually involves, what hardware you need, when it makes sense over cloud alternatives, what to look for in a commercial platform, and how compliance requirements (HIPAA, ITAR, SOC 2) map to deployment decisions. If you are evaluating self-hosted document AI for your organization, this is the checklist.

What Is Self-Hosted Document AI (and Who Needs It)?

Self-hosted document AI is a software platform that ingests documents (PDFs, images, Word files, scanned papers), extracts structured data from them, and lets users search and query the content using natural language. The difference from cloud document AI: every component runs on infrastructure you control. Your servers, your network, your storage. No API calls to external services, no data leaving your perimeter.

The typical self-hosted document AI stack includes five components:

1. Document parser: converts PDFs, DOCX, images, and scanned documents into machine-readable text
2. OCR engine: extracts text from scanned pages and images where text is embedded in pixels rather than encoded as characters
3. Embedding model: converts text chunks into vector representations for semantic search
4. Vector database: stores and retrieves embeddings for fast similarity search across your document corpus
5. LLM inference engine: runs a large language model locally for chat, summarization, and question-answering over your documents

When all five components run on your hardware, you have true self-hosted document AI. This is different from "private AI" or "secure cloud AI," which often means a cloud-hosted platform with access controls and encryption. Private cloud is valuable, but it is not self-hosted. The distinction matters for organizations where data sovereignty is a legal requirement, not a preference.

Who Searches for Self-Hosted Document AI?

The typical buyer profile falls into one of these categories:

• Defense contractors and intelligence agencies: ITAR restrictions and SCIF requirements mean documents cannot touch commercial cloud infrastructure. Air-gapped deployment is the only option.
• Healthcare systems: HIPAA does not require on-premises deployment, but large health systems often prefer it for maximum control over Protected Health Information. Self-hosting can eliminate the need for a Business Associate Agreement with the software vendor entirely.
• Law firms and legal departments: Attorney-client privilege creates a strong incentive to keep privileged documents off third-party servers. Even with a BAA or NDA, some firms consider any external processing an unnecessary risk.
• Financial services: SOX compliance, SEC examination readiness, and client confidentiality requirements drive on-premises preferences, especially for firms handling M&A documents or trading strategies.
• Government agencies: FedRAMP authorization for cloud services is expensive and slow. Self-hosted deployment on GovCloud or on-premises hardware is often faster to approve.

The common thread: these are organizations where a data breach or unauthorized disclosure is not just embarrassing but legally or operationally catastrophic. They are willing to accept higher infrastructure costs and operational complexity in exchange for complete data control.

The Self-Hosted Document AI Stack: What You Are Actually Running

Before evaluating vendors, it helps to understand what a self-hosted document AI deployment actually looks like at the infrastructure level. This is not a single binary you install. It is a multi-component system with real hardware requirements.

Component Breakdown

• Document parsing (CPU-bound): Apache Tika handles PDF, DOCX, PPTX, and other formats. Docling (by IBM) is a newer alternative with better table extraction. Neither requires a GPU.
• OCR (CPU-bound): Tesseract is the standard open-source engine. PaddleOCR offers better accuracy on complex layouts. Both run on CPU, though PaddleOCR can optionally use GPU acceleration.
• Embedding model (GPU-optional): Models like Voyage AI, E5, or sentence-transformers convert text chunks into vectors. These can run on CPU (slower) or GPU (faster). For batch ingestion of large document sets, GPU acceleration significantly reduces processing time.
• Vector database (CPU + RAM): Qdrant, Milvus, or Weaviate store document embeddings and serve similarity queries. These are memory-intensive but do not require GPUs. A corpus of 100,000 document chunks typically requires 8-16GB of RAM for the vector index.
• LLM inference (GPU-required): This is the expensive component. Running Llama 3 8B requires 24GB of VRAM minimum. Llama 3 70B requires 80GB. There is no practical way to run production LLM inference on CPU alone; response times would be measured in minutes, not seconds.

Open-Source DIY vs Commercial Platforms

You can build a self-hosted document AI stack entirely from open-source components. The pieces exist: Tika for parsing, Tesseract for OCR, Milvus for vectors, vLLM or Ollama for inference, and a custom application layer to tie them together.

The challenge is not assembling the components. It is making them work reliably in production:

• Edge cases in document formats: scanned PDFs with mixed orientations, password-protected files, embedded images within spreadsheets, corrupted headers. Each format quirk requires custom handling.
• Chunking strategy: how you split documents into chunks for embedding dramatically affects retrieval quality. Too large and you lose specificity; too small and you lose context. Getting this right requires experimentation per document type.
• Update and patch management: when a new Llama model releases, or Tesseract pushes a security patch, who is responsible for testing compatibility and rolling out updates across your stack?
• Observability: when a user reports that search results are wrong, how do you diagnose whether the issue is in parsing, OCR, chunking, embedding, retrieval, or the LLM's response generation?

Commercial platforms package these components into a tested, versioned product with a support contract. You trade some flexibility for reliability and reduced operational burden. The right choice depends on your team's capacity and appetite for infrastructure maintenance.

Hardware Requirements

Here is what production deployments typically require:

• Standard (8B model): NVIDIA A10G (24GB VRAM), 64GB system RAM, 1TB NVMe storage. Handles document ingestion, search, and chat for teams of 10-50 users.
• Analyst (70B model): NVIDIA A100 (80GB VRAM) or 2x A6000 (48GB each), 128GB system RAM, 2TB NVMe storage. Better reasoning quality for complex queries; supports larger concurrent user counts.

Both configurations assume Ubuntu 22.04 LTS or RHEL 8/9. Windows Server is technically possible but rarely used for LLM inference workloads. If you already have on-premises GPU infrastructure (common in organizations doing ML/data science), the incremental cost of adding document AI is primarily the software license.

Self-Hosted vs Cloud Document AI: When Each Makes Sense

Self-hosted is not always the right answer. Cloud document AI platforms offer real advantages: faster deployment, lower upfront cost, automatic model updates, and no hardware to maintain. The decision depends on your specific constraints.

Choose Self-Hosted When

• Regulatory requirements mandate it: ITAR, certain CMMC levels, air-gapped SCIF environments, or organizational policies that prohibit sensitive data on third-party infrastructure
• You process at high volume: cloud document AI pricing is typically per-page or per-API-call. At 100,000+ pages per month, on-premises hardware costs amortize favorably against API fees
• You need complete audit control: self-hosted means you control every log, every access record, and every network packet. No reliance on a vendor's audit log export
• You already have GPU infrastructure: if your organization runs ML workloads on-premises, adding document AI is incremental, not a new capital expenditure

Choose Cloud When

• Speed to deploy matters most: cloud platforms are operational in hours; self-hosted takes days to weeks depending on your infrastructure readiness
• Your team lacks GPU ops experience: managing CUDA drivers, model serving, and GPU memory allocation is specialized work. If your IT team has not done it before, the learning curve is steep.
• You need the latest models immediately: cloud platforms update to new model versions faster. Self-hosted deployments lag by weeks or months, depending on the vendor's release cycle.
• Volume is low or unpredictable: for 1,000 pages per month, the economics of on-premises hardware do not make sense

The Hybrid Middle Ground

Some organizations use a hybrid approach: document parsing, OCR, and embedding happen on-premises (where the sensitive data is), while LLM inference routes to a cloud API for higher-quality responses. This keeps raw document content on your hardware while sending only sanitized queries and retrieved text chunks to the cloud model.

The trade-off is that the retrieved text chunks still transit to the cloud, which may contain sensitive excerpts. Whether this is acceptable depends on your data classification and the cloud provider's data handling agreement. For fully classified environments, hybrid is not an option. For organizations that are primarily concerned about bulk data exposure rather than individual query snippets, it can be a pragmatic compromise.

For organizations that need complete air-gapped isolation, see our complete guide to air-gapped AI deployment, which covers ITAR, CMMC, and disconnected-network architecture in detail.

What to Look for in a Self-Hosted Document AI Platform

If you have decided that self-hosted is the right deployment model, here is the evaluation checklist. Not all "self-hosted" products are created equal.

Deployment Method

• Docker containers + Helm charts: the standard for modern self-hosted software. Look for official Helm charts if you run Kubernetes, and standalone Docker Compose configurations if you do not.
• Bare metal installer: some vendors offer scripts that install directly on the OS. Simpler for single-server deployments, but harder to manage at scale.
• Air-gapped delivery: can the vendor deliver the full software package (including model weights, which can be 4-140GB) via secure download or physical media? This is non-negotiable for disconnected environments.

Supported LLMs

• Which models ship with the product? Llama 3, Mistral, Qwen, and other open-weight models are standard.
• Can you swap models as new ones release, or are you locked to the vendor's bundled model?
• Does the vendor provide quantized model variants for organizations with smaller GPUs?

OCR and Document Parsing Quality

• Test with your actual documents, not the vendor's demo set. Scanned PDFs with handwriting, multi-column layouts, tables spanning pages, and low-resolution faxes are where parsers fail.
• Ask about supported file formats: PDF, DOCX, XLSX, PPTX, images (PNG/JPEG/TIFF), and email formats (EML/MSG) are table stakes.

Vector Database Options

• Does the product bundle a vector database, or do you bring your own?
• If bundled, which one? Qdrant and Milvus are the most common for self-hosted deployments.
• Can you point the product at an existing vector database if you already run one?

Update and Licensing Model

• How do updates work? For air-gapped environments, updates must be deliverable offline. Ask whether the vendor provides versioned release bundles or requires internet access for updates.
• Licensing: is it per-seat, per-server, per-GPU, or flat-rate? Per-seat licensing for on-premises software often creates friction when you want to expand access. Flat-rate or per-server licensing is simpler to budget.
• License validation: does the software phone home for license checks? For air-gapped deployments, this is a deal-breaker. Offline license keys or hardware-locked licenses are required.

Red Flags

Compliance and Security Considerations

Self-hosted deployment does not automatically make you compliant with any framework. It changes your risk surface and your obligations, but compliance still requires documented controls, policies, and (often) third-party validation.

HIPAA

Self-hosted document AI can simplify HIPAA compliance in one important way: if the software vendor has zero access to your environment, they may not meet the definition of a Business Associate under 45 CFR 160.103. No remote telemetry, no support tunnels, no cloud license checks. In that scenario, a BAA with the software vendor may not be required (consult your healthcare attorney).

However, you still bear full responsibility for the HIPAA Security Rule: access controls, encryption, audit logging, workforce training, and incident response. Self-hosting shifts the compliance burden from "vendor management" to "infrastructure management." For a detailed breakdown of HIPAA requirements for AI document tools, see our HIPAA-Compliant AI Document Processing: BAA Buyer's Guide.

ITAR and CMMC

For defense contractors handling Controlled Unclassified Information (CUI) or technical data subject to ITAR, self-hosted deployment on compliant infrastructure is typically required. Cloud-hosted solutions must meet FedRAMP Moderate or High baselines, which limits your options and extends procurement timelines. Self-hosted on your existing CMMC-assessed infrastructure avoids this dependency.

Our air-gapped AI deployment guide covers ITAR and CMMC compliance mapping in detail, including architecture requirements for disconnected networks.

SOC 2 and Data Residency

• SOC 2: your self-hosted environment must meet SOC 2 Trust Service Criteria if your organization has SOC 2 obligations. The document AI software itself does not carry a SOC 2 report (that is a cloud-service concept), but the vendor's development practices and update delivery process should be auditable.
• Data residency: self-hosted eliminates data residency concerns by definition. Data stays on your hardware in your jurisdiction. No cross-border data transfer issues.
• Audit logging: ensure the platform generates comprehensive audit logs (document access, user queries, administrative actions) that you can export to your SIEM or compliance reporting tools.

How Zedly AI Handles Self-Hosted Deployment

Zedly offers two self-hosted deployment models, both designed for organizations that require complete data sovereignty.

On-Premise (Docker)

We provide Docker containers and Helm charts that you run on your bare metal servers or existing Kubernetes cluster. The stack includes:

• Local LLM inference: Llama 3 (8B or 70B) or Mistral, running entirely on your GPUs via vLLM
• Document processing: Apache Tika for document parsing, Tesseract OCR for scanned pages
• Vector storage: self-hosted Qdrant or Milvus for semantic search across your document corpus
• Embedding generation: local Voyage AI or E5 models for document indexing
• Application layer: the Zedly web application, chat interface, vault storage, and admin controls

You control compute, storage, and network configuration. Updates are delivered as versioned container images.

Air-Gapped (Offline)

For SCIFs, defense contractors, and environments with no internet connectivity, we deliver the full software package via secure download or physical media. This includes all container images, model weights, and configuration files needed to deploy without any network access. The software makes zero outbound connections. No telemetry, no license phone-home, no cloud dependencies.

Hardware Requirements

• Standard (Llama 3 8B): NVIDIA A10G (24GB VRAM), 64GB RAM, 1TB NVMe. Supports teams of 10-50 users.
• Analyst (Llama 3 70B): NVIDIA A100 (80GB VRAM) or 2x A6000, 128GB RAM, 2TB NVMe. Better reasoning quality for complex document queries.

Compatible with Ubuntu 22.04 LTS and Red Hat Enterprise Linux 8/9. For full deployment details, hardware specifications, and pricing, see the Zedly Self-Hosted AI product page or request a technical briefing.

Frequently Asked Questions

Can I run document AI without internet access?

Yes. A fully air-gapped document AI deployment runs every component locally: document parsing (Apache Tika, Tesseract OCR), embedding generation (Voyage AI or E5 models), vector storage (Qdrant or Milvus), and LLM inference (Llama 3, Mistral). Software and model weights are delivered via physical media or secure download, and no internet connection is required after initial installation. This is the standard deployment model for SCIFs, defense contractors, and organizations subject to ITAR restrictions.

What GPU do I need for self-hosted document AI?

For production workloads with an 8B-parameter model (such as Llama 3 8B), you need at minimum an NVIDIA A10G with 24GB VRAM, 64GB system RAM, and 1TB NVMe storage. For 70B-parameter models (Llama 3 70B), you need an NVIDIA A100 with 80GB VRAM or two A6000 GPUs, 128GB system RAM, and 2TB NVMe. Document parsing, OCR, and embedding generation are CPU-bound and do not require a GPU, but LLM inference for chat and summarization does.

Is self-hosted document AI HIPAA compliant?

Self-hosted deployment can simplify HIPAA compliance because PHI never leaves your network. If the software vendor has zero access to your environment (no remote telemetry, no support tunnels, no cloud callbacks), they may not qualify as a Business Associate, which means a BAA may not be required for the on-premises deployment itself. However, if the vendor provides remote support or the software phones home for licensing, a BAA is still needed. Consult your healthcare attorney for your specific deployment model. For a detailed breakdown, see our HIPAA-Compliant AI Document Processing: BAA Buyer's Guide.

How does self-hosted document AI compare to Google Document AI?

Google Document AI is a cloud API: you send documents to Google's infrastructure for processing. It excels at structured form extraction and OCR, but your data transits Google's servers. Self-hosted document AI keeps all processing on your hardware. The trade-off is that cloud APIs often have higher accuracy on specialized extraction tasks (due to larger training datasets), while self-hosted solutions give you complete data control and eliminate per-page API costs at scale. For organizations processing sensitive or regulated documents, the data sovereignty advantage of self-hosted typically outweighs the accuracy gap.

Can I use open-source models for document processing?

Yes. The core stack for self-hosted document AI can be built entirely from open-source components: Apache Tika or Docling for document parsing, Tesseract or PaddleOCR for optical character recognition, sentence-transformers or Voyage AI for embeddings, Milvus or Qdrant for vector storage, and Llama 3 or Mistral for LLM inference. The challenge is integration: making these components work together reliably, handling edge cases in document formats, and maintaining the system over time. Commercial platforms package these components into a tested, supported product with a single update and support path.