SecureAI Tools

Private AI on your own server, honestly reviewed. What you actually get when you deploy it in 2026.

TL;DR

• What it is: A self-hosted web app that wraps local or remote AI models with a chat interface, document Q&A (RAG), multi-user authentication, and integrations with Paperless-ngx and Google Drive [README].
• Who it’s for: Technically-curious individuals or small teams who want a private ChatGPT-style interface without sending data to OpenAI, and who need basic document chat on their own hardware [README].
• Cost savings: Replacing a $20/mo ChatGPT Plus subscription with a $5–6/mo VPS is possible — but only if you’re comfortable running Docker and managing your own stack. API costs still apply if you use OpenAI models.
• Key strength: Unusually clean setup story for a self-hosted AI tool — Docker Compose, one script, under five minutes according to the README [README].
• Key weakness: The project has 1,734 stars and 100 total commits as of this review. It is functionally superseded by more active projects like AnythingLLM [1] and Open WebUI that have continued to ship features this project listed on a wishlist and never finished [README].

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What is SecureAI Tools

SecureAI Tools is a self-hosted web application that puts a user-facing chat interface in front of AI language models — either local ones running via Ollama or remote ones accessed through the OpenAI API. The pitch is in the name: private, secure AI without your conversations transiting a third-party service [README].

The core feature set has two parts. First, a standard AI chat interface similar to ChatGPT — you type a message, the model responds. Second, a document chat feature using retrieval-augmented generation (RAG) — you upload PDFs, the system indexes them, and you can ask questions against their content. Both features work with local models through Ollama or with cloud APIs [README].

What distinguishes it from a bare Ollama install is the access control layer: SecureAI Tools ships with built-in email/password authentication and user management, meaning you can share a single instance with family members or coworkers without exposing the raw model API to everyone on your network [README].

The project is licensed under AGPL-3.0, which matters more than most people realize. AGPL means anyone running a modified version as a service must release their modifications as open source — which closes off the path of taking this code and building a commercial SaaS on top without open-sourcing your changes [README]. For a personal or team deployment, this is fine. For anyone wanting to embed it in a product, it’s a genuine constraint.

GitHub numbers as of this review: 1,734 stars, 87 forks, 100 commits. The 100-commit count is the number that tells you the most about where this project sits in its lifecycle.

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Why people choose it over alternatives

Based on the README and what it actually delivers, the people who land on SecureAI Tools fall into one of a few buckets. They found it through a GitHub search for “self-hosted ChatGPT” before AnythingLLM became the dominant result. Or they wanted something lightweight — a tool with fewer moving parts than the more feature-heavy options.

The privacy angle is the strongest genuine reason to look at any tool in this category. When you connect your email, your internal documents, or your work files to a hosted AI service, that data crosses someone else’s servers. SecureAI Tools, like AnythingLLM [1] and similar self-hosted tools, runs entirely inside your own network when paired with Ollama — the inference included. A question you ask about a confidential document never leaves the machine [README].

The Paperless-ngx integration is a specific and genuinely useful differentiator the README highlights. If you’re already running Paperless-ngx for document management, SecureAI Tools can directly query that document library. The demo video in the README shows this working with a locally-running Llama2 model — your document store, your model, no third-party involved [README].

However, third-party reviews of SecureAI Tools specifically are not available, which is itself diagnostic. A tool with 1,734 stars and no visible coverage in the tech press or review aggregators is a tool that most of the self-hosting community has passed over in favor of more active alternatives. The comparison has to be made against what this space looked like when the project was active versus what’s available now.

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Features

From the README, what SecureAI Tools actually ships:

Core functionality:

• Chat interface against AI models (ChatGPT-style) [README]
• Document chat (RAG) — PDFs only as of the current codebase [README]
• 100+ AI models supported through Ollama, plus OpenAI API and OpenAI-compatible APIs [README]
• Reusable document collections — index a set of documents once, query them repeatedly [README]
• Offline document processing [README]

Access and user management:

• Built-in email/password authentication [README]
• Multi-user support — multiple accounts on a single instance [README]
• No OAuth, no SSO, no LDAP — authentication is local only [README]

Integrations:

• Paperless-ngx — query your self-hosted document archive [README]
• Google Drive [README]

Infrastructure:

• Docker Compose deployment [README]
• PostgreSQL for data storage [README]
• GPU support for Nvidia hardware on Linux (optional; CPU-only also works, just slower) [README]

What’s on the wishlist but not shipped: The README’s features wishlist section is worth reading carefully, because it shows where the project stopped:

• Support for more file types (Google Docs, Docx, Markdown) — not done [README]
• Markdown rendering in responses — not done [README]
• Chat sharing — not done [README]
• Mobile-friendly UI — not done [README]
• Per-chat model selection — not done [README]
• Prompt templates library — not done [README]

These are all features that were aspirational as of the last commit. They’re also features that competitors shipped. That list is the clearest signal of where development stopped.

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Pricing: SaaS vs self-hosted math

SecureAI Tools has no SaaS tier. There’s no company behind it offering a hosted version. It’s purely self-hosted, which means the pricing math is: your infrastructure cost versus whatever you’re currently paying for AI access.

What self-hosting SecureAI Tools actually costs:

• VPS to run it: $5–10/month (Hetzner, Contabo, or DigitalOcean — 2GB RAM minimum for local models, though more is better)
• SecureAI Tools itself: $0 (AGPL-3.0)
• If using local models (via Ollama): $0 in API costs, inference runs on your hardware
• If using OpenAI API through SecureAI Tools: your actual OpenAI API spend (gpt-4o at $2.50/1M input tokens — data not available on typical usage patterns)

What you might replace:

• ChatGPT Plus: $20/month (gpt-4o, browsing, plugins)
• Claude Pro: $20/month
• A combination of both, which some founders run: $40+/month

The honest math: If you’re paying for ChatGPT Plus to chat with a model and occasionally ask questions about uploaded documents, and you’re willing to run a $6 VPS and accept that local models (Llama 3.1, Mistral, Qwen) are not gpt-4o, you can eliminate that $20/month subscription. Over a year that’s roughly $170 saved after VPS costs.

If you need GPT-4 class model quality, you’ll still pay OpenAI API costs — SecureAI Tools routes your requests to the API, it doesn’t change the pricing model of the underlying service.

The more important savings is data privacy, not dollars. If your use case involves confidential documents and you’re currently uploading them to ChatGPT, the calculus is less about money and more about what leaves your network.

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Deployment reality check

The setup story is the strongest part of this project. The README gives a four-step Docker Compose install that genuinely does get you to a running instance quickly [README]:

1. Create a directory
2. Run the setup script (downloads docker-compose.yml, generates .env)
3. Optionally edit .env for OpenAI API keys or GPU settings
4. docker compose up -d

First login is at http://localhost:28669/log-in with the default credentials [email protected] / SecureAIToolsFTW! — change those immediately [README].

What you actually need:

• Docker and docker-compose on a Linux server (or Mac for local use)
• 2GB+ RAM if running purely as a web frontend to the OpenAI API
• 8GB+ RAM for running local models through Ollama (which you install separately)
• A reverse proxy (Caddy or nginx) for HTTPS if exposing outside localhost
• A domain name if sharing with others

What can go sideways:

• Ollama isn’t bundled. If you want local inference, you install and configure Ollama separately, then point SecureAI Tools at it. The README mentions this dependency but doesn’t walk you through the Ollama setup [README].
• GPU acceleration on Linux requires the Nvidia container toolkit and manual editing of the docker-compose.yml file — not automatic [README].
• CPU-only inference is noted as slow on Linux and Windows. M1/M2/M3 Mac performance is described as “really good” [README] — but that’s relative to other CPU-only paths, not GPU inference.
• The project has 100 total commits. If something breaks with a Docker or PostgreSQL version update, there may not be an active maintainer to ship a fix. This is the risk of building on a low-activity project.

Realistic setup time for a technical user: 20–40 minutes including Ollama configuration. For someone who hasn’t used Docker before: several hours, and it’s worth considering whether a more active project with a larger community would give better support when things break.

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Pros and Cons

Pros

• Fast initial setup. The Docker Compose path is well-documented and the setup script handles env file generation. Fastest path to a working AI chat instance among similarly-featured tools [README].
• Built-in user management. Most bare Ollama-based tools have no access control. SecureAI Tools gives you email/password auth and multi-user support out of the box [README].
• Paperless-ngx integration. If you’re already running Paperless-ngx, being able to ask questions against your document archive through the same stack is genuinely useful [README].
• Works with both local and cloud models. Flexible architecture — you can run fully local (Ollama + Llama), fully cloud (OpenAI API), or both [README].
• GPU support. Nvidia GPU acceleration available on Linux for meaningfully faster local inference [README].

Cons

• AGPL-3.0 license. Stricter than MIT. You cannot incorporate this into proprietary software or run a hosted service on modified code without open-sourcing your changes [README]. Not an issue for private use; a real constraint for anything commercial.
• Low activity. 1,734 stars, 87 forks, 100 commits. The features wishlist has six unchecked items including mobile UI, markdown rendering, and multi-file format support. These are gaps that competitors have closed [README].
• PDF only for document chat. The README explicitly notes PDFs are the only supported document format. Google Docs, Docx, and Markdown are listed as future work that hasn’t shipped [README].
• No markdown rendering. Responses render as plain text according to the wishlist status — which means code blocks, lists, and formatted AI responses look worse than they should [README].
• No mobile-friendly UI. Listed on the wishlist, not done [README]. Using it on a phone is functional but not designed for it.
• No per-chat model selection. You configure one model in settings, and all chats use it. Switching models requires going to the admin settings panel [README].
• No SSO, no LDAP. Authentication is email/password local accounts only. For a small team this is manageable; for an organization with existing identity infrastructure, it’s a manual burden [README].
• No active community signal. No visible Hacker News threads, no tech press coverage, no review aggregator presence. If you hit a problem, StackOverflow won’t have the answer — you’re reading the README and the source code.

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Who should use this / who shouldn’t

Use SecureAI Tools if:

• You want the fastest possible path to a private AI chat instance with document Q&A and don’t need anything beyond that.
• You’re already running Paperless-ngx and want to query your document archive with AI — the integration is pre-built [README].
• You’re deploying for personal use or a very small group (2–5 people) who just need private AI chat, and you don’t anticipate needing features beyond what’s currently shipped.
• You want to experiment with self-hosted AI without committing to a more complex stack.

Skip it — try AnythingLLM [1] instead — if:

• You need more file format support beyond PDFs.
• You want markdown rendering in responses.
• Mobile access matters to your use case.
• You want a tool with an active development community that will ship fixes and features.
• You’re building anything commercial — AGPL is the wrong license for that.

Skip it — use Open WebUI instead — if:

• Your primary need is a good Ollama frontend with multi-model support, model management, and an active codebase.
• You want the ability to select different models per conversation.
• You care about a polished, actively maintained interface.

Skip it — stay with ChatGPT Plus — if:

• You need GPT-4 class output quality and aren’t willing to accept the gap between frontier models and local alternatives.
• You don’t have a technical person available to manage the infrastructure.
• You upload fewer documents than the ChatGPT free tier allows.

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Alternatives worth considering

The self-hosted private AI space has moved fast since SecureAI Tools was built. These are the tools doing what SecureAI Tools does, more actively:

• AnythingLLM [1] — The closest direct comparison. Multi-user, document chat (RAG), local and cloud model support, active development, supports more than PDFs. Better choice for nearly every use case SecureAI Tools targets.
• Open WebUI — The standard Ollama frontend. Not focused on document chat, but excellent for multi-model AI chat with an active community and regular releases.
• LibreChat — Broader feature set: multi-model chat, document handling, plugin support, OpenAI-compatible API. More complex to deploy.
• PrivateGPT — Purpose-built for private document Q&A with local models. More focused on the RAG use case specifically.
• Khoj — Personal AI assistant with document indexing and web search integration [2].
• ChatGPT Plus / Claude Pro — If setup friction is the real constraint, $20/month buys frontier-model quality and zero infrastructure responsibility. Only makes sense if privacy is not a requirement.

For a non-technical founder whose main concern is keeping documents off third-party servers: AnythingLLM is the realistic starting point today, not SecureAI Tools.

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Bottom line

SecureAI Tools was a solid early entry in the self-hosted AI chat space. The setup story is genuinely good — Docker Compose, one script, working instance in minutes — and the core idea (private AI chat with document Q&A and built-in user management) is the right idea. The problem is that the project shipped its first features and then largely stopped, while the tools it competes with continued to ship. Six wishlist items remain unchecked, third-party reviews are nonexistent, and 100 commits over the project’s lifetime means you’re betting on a codebase that may not respond when something breaks. In 2026, there are more active tools doing the same thing better. If private AI on your own server is the goal, the destination is right — but AnythingLLM or Open WebUI will get you there with fewer rough edges and a community to fall back on when things go sideways.

If you do decide to deploy it and want someone to handle the infrastructure, that’s exactly what upready.dev does — one-time setup, you own the stack.

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Sources

1. AnythingLLM — Product Hunt Product Page. https://www.producthunt.com/products/anythingllm — producthunt.com
2. Rui Carmo — Large Language Models (Tao of Mac). https://taoofmac.com/space/ai/llm — taoofmac.com

Primary sources:

• GitHub repository and README — SecureAI Tools. https://github.com/SecureAI-Tools/SecureAI-Tools (1,734 stars, AGPL-3.0, 100 commits)