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docsgpt.cloud

Open-source private AI assistant and self-hosted RAG platform over your own documents, with cited answers. Cloud, managed, on-premises or air-gapped.

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Artificial Intelligence
Language
English
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2023
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What is DocsGPT?

DocsGPT is an open-source, self-hostable RAG (retrieval-augmented generation) platform for running a private AI assistant over an organisation's own documents. It is built by Arc53 and released under the MIT licence. You point it at your document collection, and it answers questions with citations back to the source text — the core appeal being that your documents and, if you choose, your model inference stay under your control.

What it actually does

  • Cited answers over internal documents. Every answer is traced to its source, which matters when people need to verify a claim rather than trust a chatbot.
  • Deployment choice as the central feature. The same platform runs in four modes: cloud (Arc53 multi-tenant), managed (a dedicated instance Arc53 operates), on-premises in your own data centre, and air-gapped with zero external network egress. A hybrid option pairs an on-premises platform with your own cloud LLM.
  • Model flexibility. Setup offers a built-in public API, OpenAI, Anthropic, Google Gemini, OpenRouter, Groq, or any OpenAI-compatible server such as Ollama, vLLM or LM Studio — so a fully local inference stack is possible.
  • Beyond chat. The platform covers knowledge, agents, workflows and governance, not just a single Q&A box.

Who it suits

The deployments named on the page point to a clear audience: regulated and public-sector organisations, and large enterprises with strict data-residency or egress rules. Examples include an internal knowledge assistant on government infrastructure with local inference, an assistant over internal technical documentation, and a multilingual EU-law research tool spanning millions of pages with answers in six EU languages. If your constraint is "the data cannot leave this network," that is the design brief.

How to choose a deployment mode

Mode Who operates it Best when
Cloud Arc53, multi-tenant No residency constraints; fastest start
Managed Arc53, dedicated instance You want isolation without running infrastructure
On-premises You, with Arc53 support Strict data residency; hybrid with your cloud LLM
Air-gapped You, with Arc53 support Zero-egress mandate

A practical next step

If you are evaluating it, start with the default local install: it runs on one machine first, then lets you widen access to your network or a domain with HTTPS. That gives you a working answer-with-citations loop over a small document set before you commit to an on-premises or air-gapped rollout. Decide your deployment mode by asking one question first: is external network egress permitted at all? If no, you are in air-gapped territory; if yes but residency is fixed, on-premises or hybrid is the natural fit.

How does DocsGPT keep company documents private and ensure answers are cited to their sources?

DocsGPT keeps documents private by letting you run the entire platform inside your own trust boundary, and it supports citations by linking each answer back to the source material it retrieved.

Privacy: deployment controls where data lives

  • Cloud (multi-tenant): fastest to start, but data residency is not under your control.
  • Managed dedicated instance: a private instance operated for you, still without you running the infrastructure.
  • On-premises (your data centre): you host the platform; a hybrid option lets the platform stay on-premises while the LLM runs in your cloud.
  • Air-gapped: zero external network egress, for strict mandates.
  • The licence is MIT, so self-hosting the community edition is possible; this matters for regulated environments where code review and audit are required.

Citations: answers are grounded in retrieved passages The platform is built as a RAG system over your own documents, and the page states every answer is cited to its source. In practice this means an answer should be traceable to specific documents, which is what makes it usable in compliance, legal or technical support work rather than as a general chatbot.

How to decide

Need Better fit
Quick internal pilot, no residency constraints Cloud
Private instance without operating it Managed dedicated
Strict data residency, you control infrastructure On-premises
No external network at all Air-gapped

A concrete scenario: a legal team researching EU law across millions of pages in several languages needs both multilingual retrieval and full source citations, which is exactly the combination described for LexEU. A government team with local inference requirements is the air-gapped or on-premises case.

Next step: run the one-command installer locally and point it at a small document set, then check whether the citations returned are precise enough for your review workflow before committing to a wider deployment. For comparison, other self-hosted RAG options include Danswer and Quivr.

What are the differences between Cloud, Managed, On-premises, and Air-gapped deployment options?

The main difference is where the platform runs and how much control you retain over data egress. DocsGPT offers the same platform and MIT licence across all four, so the choice is mostly about data residency, operational burden and network isolation — not features.

Deployment Where it runs Who operates it Data egress
Cloud Arc53's multi-tenant environment at app.docsgpt.cloud Arc53 Data leaves your network
Managed A dedicated instance, in your region, run by Arc53 Arc53 Data leaves your network, but isolated from other tenants
On-premises Your own data centre You, with Arc53 support Stays inside your infrastructure; hybrid (your cloud LLM) is an option
Air-gapped Your own environment You, with Arc53 support Zero external network

Cloud suits teams with no residency constraints that want to start quickly. Managed is for organisations that need a private instance but don't want to run it themselves. On-premises is for strict data residency where the platform must sit in your data centre; the hybrid variant keeps the platform local while using a cloud LLM. Air-gapped is for zero-egress mandates, where no external network connection is permitted at all.

A practical decision rule: if you have no residency requirement, choose Cloud; if you need isolation without operations work, choose Managed; if the platform must live in your own data centre, choose On-premises; if nothing may leave the network, choose Air-gapped. Note that the last two shift operational responsibility to your team, so factor in the staff and infrastructure that requires.

Next step: list your residency and egress constraints first, then match them against the table above. Public-sector and regulated examples on the page — such as the UK Department for Work and Pensions running an internal assistant on government infrastructure with local inference, and LexEU's multilingual EU-law research — illustrate how the stricter options are used in practice. For the open-source community edition and self-hosting details, see DocsGPT.

How do you install and run DocsGPT, and which AI model providers does it support?

DocsGPT installs as a self-hosted service, and the quickest route is its one-command installer. Per the project's own page, the command is curl -fsSL https://docs.ac/install | bash on macOS/Linux or irm https://docs.ac/install.ps1 | iex on Windows; Docker is required. The installer then asks three things:

  1. Who should reach it — only this computer (default), other machines on your network over plain HTTP with an access token, or a domain name with HTTPS (Caddy certificate plus access token).
  2. Which model provider — the built-in DocsGPT public API (free, no key, and the default), OpenAI, Anthropic, Google Gemini, OpenRouter, Groq, or any OpenAI-compatible server such as Ollama, vLLM or LM Studio.
  3. It then starts the server, sets up the database on first run, and prints a local address along with docsgpt status | logs | upgrade | down | uninstall for management.

Practical reading: the local-only default is the sensible first install — you get a working assistant before deciding on network exposure or a paid model key. Choosing an OpenAI-compatible server is what makes a fully local or air-gapped setup possible, since inference stays on your own hardware; picking a hosted provider instead means your prompts and document excerpts leave your network, which matters if you have residency or egress rules. The page describes deployment options of cloud, managed, on-premises and air-gapped, all on the same MIT-licensed platform.

Next step: if you are evaluating rather than committing, run the installer with option 1 and the free default provider on a machine holding a small document set, confirm the cited answers are accurate enough, then re-run and widen access or switch providers.

For background, see DocsGPT and, if you want to compare a hosted alternative to self-hosting, DocsGPT Cloud.

Which organizations and industries use DocsGPT in regulated or public-sector environments?

DocsGPT is used in regulated and public-sector settings where document confidentiality and data residency matter. The site names several government and public-sector users, plus enterprise and research organisations.

Named users and deployments

  • HM Government and the UK Department for Work and Pensions (DWP) — described as an internal knowledge assistant on government infrastructure with local inference.
  • LexEU — multilingual EU-law research over 2.5M+ pages, answering in 6 EU languages with full source citations.
  • AMD — an assistant over internal technical documentation.
  • AstraZeneca — listed among organisations whose people use it, indicating a regulated pharma context.
  • Also listed: Microsoft, Intel, Amazon, Airbnb, Siemens, Deloitte, PwC, Accenture, Payoneer, Stanford and MIT.

Industries implied Government/public sector, legal and regulatory research, healthcare and pharmaceuticals, technology and semiconductors, professional services and consulting, financial services, and academia.

Why these fit The platform is pitched as built for regulated environments, with cited answers, an MIT licence, and deployment options spanning cloud, managed, on-premises and air-gapped. That range is the practical trade-off: cloud and managed reduce operational burden but suit teams without strict residency constraints, while on-premises and air-gapped give stricter control at the cost of running the infrastructure yourself.

Next step If you work in a public body or regulated industry, check which deployment model matches your data rules — for example, a hybrid setup using your own cloud LLM, or fully air-gapped for zero external network — then review the pricing page at DocsGPT before requesting a demo.

How much does DocsGPT cost and what does the MIT-licensed community edition include?

DocsGPT's own site points to a pricing page rather than stating figures on the page summarised here, so treat any number you see as something to verify there: DocsGPT. What the page does make clear is the licensing and deployment split.

The MIT licence applies to the platform, not to every way of running it. The community edition is self-hosted under MIT, so you can run it on your own hardware at no licence cost. The hosted cloud app, the managed dedicated instance, on-premises with Arc53 support, and air-gapped deployments are commercial offerings — the same platform and licence, but you pay for hosting, isolation or support rather than for the software rights.

What the community edition covers, based on the page: private AI assistants, agents, enterprise search over your own documents, answers cited back to their sources, and a one-command Docker install that lets you pick a model provider — including a local OpenAI-compatible server such as Ollama, vLLM or LM Studio, or a hosted provider like OpenAI, Anthropic, Gemini, Groq or OpenRouter.

Choosing between the editions

Your constraint Sensible starting point
Evaluating, no residency rules Cloud or a local install
Data must stay in your own data centre On-premises self-host or managed instance
No outbound network at all Air-gapped
Want the software free, will run it yourself Community edition

The real cost driver is rarely the licence. It is who operates the stack and which model answers the queries — a self-hosted setup shifts both onto your team and your GPUs, while a hosted model provider moves cost to per-token usage.

If you are weighing this up, install the community edition on one machine, point it at a small document set and a local model, and measure answer quality before deciding whether you need the managed or air-gapped route.

Related questions

More questions →
What Are Open-Source UI Element Libraries and How Do They Differ From UI Frameworks?

An open-source UI element library is a collection of individual, ready-made interface pieces—buttons, cards, inputs, toggles, loaders—that you copy into your own project and adapt. A UI framework, by contrast, is a structured system of components, conventions, and often a theming layer that governs how your whole interface is built. The practical difference: an element library gives you a snippet; a framework gives you a way of working. If you need a polished button in ten minutes, reach for the element library. If you're building a 40-screen product with a team, you probably want the framework.

What "open-source UI element library" actually means

The term gets used loosely, so it helps to separate the parts:

  • Open-source: the code is publicly available, and the license tells you what you may do with it—copy, modify, redistribute, or use commercially.
  • UI element: a single, self-contained piece of interface, usually small enough to read in one sitting. A button with hover states, a pricing card, a search field.
  • Library: a browsable, searchable collection of those elements, typically contributed by many different people.

On a site like Uiverse, elements are shared by a community and written in plain CSS or Tailwind. You find one you like, copy the markup and styles, paste them into your project, and adjust colors, spacing, and text to fit. There's no package to install and no build step required—which is exactly the appeal, and also the source of most of the confusion.

Element library vs. UI framework: the core differences

Dimension Open-source UI element library UI framework / design system
Unit of reuse A single snippet you copy A component you import or call
Installation None; paste into your code Package install, config, sometimes a provider
Consistency Depends on you; each element may look different Enforced by shared tokens and APIs
Theming Manual edits per element Central theme/config file
Updates You own the copy; no upstream updates Version bumps bring fixes and changes
Accessibility Varies per contributor; must be checked Usually tested and documented
Best for Prototypes, landing pages, small sites, one-off needs Multi-page apps, teams, long-lived products
Learning curve Low—read the CSS Higher—learn the API and conventions

The table isn't a verdict. It's a map of trade-offs. Element libraries win on speed and freedom; frameworks win on consistency and maintenance.

Licensing and attribution: what to check before you paste

This is where people get into trouble, and it's worth slowing down for.

  1. Find the license. Every element or collection should state one. Common open-source licenses include MIT, Apache-2.0, and BSD. Some projects use copyleft licenses like GPL, which can impose obligations if you redistribute your code.
  2. Understand what the license permits. MIT and Apache-2.0 are permissive: you can typically use the code in commercial and closed-source projects. Copyleft licenses may require you to release derivative source under the same terms.
  3. Check attribution requirements. Permissive licenses usually require you to keep the copyright notice and license text somewhere in your project. That's a real obligation, not a formality.
  4. Look for per-element terms. On community sites, the site's overall terms and the individual contributor's stated wishes may differ. If a contributor asks for credit, honor it.
  5. When in doubt, ask or avoid. If a snippet has no license at all, you don't have clear permission to reuse it. Treat "no license" as "not open source," even if the code is publicly visible.

This article is general information, not legal advice. For commercial products with real exposure, have someone qualified review the licenses you're relying on.

How to use a community element in your project: a practical workflow

Here's a repeatable process that avoids most of the usual mess.

1. Start from a real need, not a browsing session

Decide what you need first—"a compact primary button with a loading state"—then search. Browsing aimlessly produces a pile of pretty snippets that don't fit together.

2. Copy the smallest version that works

Take the markup and the styles. Strip anything you don't need: demo wrappers, extra animations, decorative layers. Less code means fewer surprises.

3. Convert it to your conventions

If your project uses design tokens or CSS variables, replace hard-coded values:

/* Before: hard-coded */
.button { background: #4f46e5; border-radius: 8px; }

/* After: token-based */
.button { background: var(--color-primary); border-radius: var(--radius-md); }

This one step is what keeps a copied element from looking like a foreign object in your UI.

4. Check accessibility before you ship

Community elements vary widely here. Verify at minimum:

  • Keyboard focus is visible and the element is reachable by Tab.
  • Color contrast meets WCAG AA (4.5:1 for normal text).
  • Interactive elements use semantic HTML (<button>, not a clickable <div>).
  • Form inputs have associated labels.
  • Motion respects prefers-reduced-motion.

5. Test in context

Paste it into a real page with real content. Long labels, small screens, and dark mode break more copied elements than anything else.

6. Note where it came from

Keep a short comment or an internal credits file: source, license, date. Future you—and your legal reviewer—will be grateful.

Where element libraries genuinely shine

  • Prototypes and demos: you need something clickable today, not a design system.
  • Landing pages and marketing sites: a handful of distinctive elements, each custom.
  • Filling gaps: your framework lacks one specific component, and you don't want to build it from scratch.
  • Learning: reading well-made CSS is one of the fastest ways to improve.
  • Small projects: a personal site doesn't need a theming architecture.

Where they fall short

  • Consistency at scale: ten elements from ten contributors rarely look like one product.
  • Maintenance: you own every copy. When your design changes, you edit each one.
  • Accessibility debt: you inherit whatever the contributor did or didn't do.
  • No upstream fixes: a bug fixed in the original won't reach your copy.
  • Integration friction: different naming conventions, different units, different assumptions about resets.

When to choose which

Choose an element library when the scope is small, the timeline is short, or you need a few distinctive pieces rather than a whole system.

Choose a framework or design system when multiple people build multiple screens over months, when consistency is a product requirement, or when accessibility and theming need to be guaranteed rather than checked.

A hybrid works well for many teams: adopt a framework for the structural components—forms, navigation, layout—and borrow individual elements for the places where you want personality. Just route every borrowed element through the same token and accessibility checks, so it lands as part of your system rather than beside it.

The short version: open-source UI element libraries are a fast, flexible way to get good-looking interface pieces into a project. They are not a substitute for a design system, and the license and accessibility details are the part worth reading carefully.

Website Overview

Identifiable technologies and additional version or configuration signals make the service easier to fingerprint, which may help targeted scanners narrow their checks. Page metadata, canonical configuration and social previews work together to provide more consistent search and sharing presentation.

Domain and Registration

Transfer-protection status is present, helping reduce the risk of unauthorized domain transfers. The domain has about 3 years of registration history; its current configuration provides more context than age alone. The registrar is NAMECHEAP, a widely used domain service provider. The domain uses the common .cloud extension, which is not an independent safety signal.

DNS and Email

Nameservers are provided by Cloudflare, indicating managed DNS hosting. MX records point to the Cloudflare Email Routing email service. SPF and DMARC are configured. DKIM status is unknown. TXT records include verification markers for Google. Such markers may also remain after a service stops being used. DNSSEC signatures were not detected, so this additional DNS authenticity protection is not confirmed.

TLS and Certificates

The certificate uses an RSA 2048-bit public key, offering broad client compatibility. The server supplied a complete certificate chain. No organization name is present in the certificate; the available fields are consistent with domain validation. The certificate was issued by Let's Encrypt, commonly associated with automated certificate services. The certificate's total validity is about 89 days, consistent with a short renewal cycle.

HTTP and Browser Security

The response lacks these common security headers: CSP, X-Content-Type-Options, Referrer-Policy, Permissions-Policy, clickjacking protection. CORS permits any origin to read this response. This is common for public resources; sensitive responses need narrower handling. No X-Powered-By header was found, reducing one common source of backend fingerprinting information. No obvious internal addresses or debug information were found in the headers. The Server header contains the custom value Vercel.

Technology Stack Analysis

The public page identifies Astro 7.3.3, Vercel, with exact versions exposed for 1 technologies. These details can narrow vulnerability checks, although exposure alone is not a vulnerability.

Search and Social Sharing

The Generator tag identifies Astro v7.3.3, making the publishing system easier to fingerprint. Twitter Card metadata is configured. JSON-LD includes Organization data, helping describe the organization as an entity. The title has 52 characters, within a common display range. A meta description is present, with 149 characters.

Hosting and Email
DNSCloudflare
HostingVercel
EmailCloudflare Email Routing
Approximate IP geolocation (inferred; a CDN or proxy may differ from the origin server) United States flagUnited States Inferred 66.33.60.34

Registration details

RDAP / WHOIS

RegistrarNAMECHEAP
Registered2023-03-10
Expires2027-03-10
Domain statusclient transfer prohibited
Nameserversdolly.ns.cloudflare.com、sullivan.ns.cloudflare.com
DNSSECunsigned

DNS records

DNS

TypeNameValueTTLPriority
Acname.vercel-dns.com66.33.60.34300—
Acname.vercel-dns.com76.76.21.164300—
MXdocsgpt.cloudroute2.mx.cloudflare.net30011
MXdocsgpt.cloudroute3.mx.cloudflare.net30066
MXdocsgpt.cloudroute1.mx.cloudflare.net30069
NSdocsgpt.clouddolly.ns.cloudflare.com86400—
NSdocsgpt.cloudsullivan.ns.cloudflare.com86400—
TXTdocsgpt.cloudgoogle-site-verification=GC9HYhby93tIbh1suivBLmld2dOMbvc62SWsoabko_w300—
TXTdocsgpt.cloudv=spf1 include:_spf.mx.cloudflare.net ~all300—
CNAMEwww.docsgpt.cloudcname.vercel-dns.com300—
DMARC_dmarc.docsgpt.cloudv=DMARC1; p=reject; pct=100300—

TLS and certificates

TLS / SSL

AssessmentNormal configuration
Supported protocolsTLSv1.2、TLSv1.3
Negotiated protocolTLSv1.3
Certificate subjectwww.docsgpt.cloud
IssuerLet's Encrypt
Valid until2026-11-29T16:40 · Remaining when checked: 55 days
Verification detailsCertificate trust: Passed · Hostname match: Passed

HTTP response headers

HTTP

HeaderValue
content-typetext/html; charset=utf-8
cache-controlpublic, max-age=0, must-revalidate
serverVercel
strict-transport-securitymax-age=63072000
access-control-allow-origin*

Identified technologies
Astro 7.3.3Vercel

Pages, Search and Sharing
Meta descriptionOpen-source private AI assistant and self-hosted RAG platform over your own documents, with cited answers. Cloud, managed, on-premises or air-gapped.
Canonical URLhttps://www.docsgpt.cloud/
LanguageEnglish (default)
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