Open WebUI vs AnythingLLM vs PrivateGPT: 2026 Comparison

By AIFoss · 11 min read

Three tools, one overlapping use case: replace the cloud chat UI with something that runs on your hardware. Open WebUI, AnythingLLM, and PrivateGPT each solve this differently — and picking the wrong one costs you hours of reconfiguration.

Versions tested: Open WebUI v0.9.5, AnythingLLM v1.12.1, and PrivateGPT v0.6.2.

Quick Verdict

Open WebUI wins on breadth. It’s the most feature-complete local chat interface available, with active development, multi-model conversations, web search, and an enterprise-ready auth story. It’s the right pick if you want a team-ready, multi-modal workspace that goes well beyond “chat with documents.”

AnythingLLM wins on document ingestion. If “chat with my PDFs and notes” is 90% of your use case, nothing sets it up faster or keeps workspaces cleaner. The desktop app embeds Ollama — there’s nothing else to install.

PrivateGPT is the odd one out. It’s less a UI product and more a RAG API framework. Its Gradio interface works fine for testing, but if you want a daily driver, you’re using PrivateGPT as a backend, not a frontend.

What Each Tool Actually Is

Open WebUI (v0.9.5, custom BSD-based license, 138k GitHub stars) started as an Ollama frontend and has since grown into a full platform: multi-model conversations, web search across 15+ providers, voice and video support, image generation via ComfyUI and AUTOMATIC1111, enterprise authentication (LDAP, SCIM 2.0, OAuth, SSO), and a built-in RAG pipeline with nine vector database options. It connects to Ollama for local inference, or any OpenAI-compatible API for cloud and hybrid setups. As of v0.9.0 there’s also a desktop app and scheduled automations — this has become a full local AI operating environment.

AnythingLLM (v1.12.1, MIT license, 60.3k GitHub stars) is purpose-built for RAG. Workspaces act as isolated knowledge bases — documents added to Workspace A stay there, invisible to Workspace B. The desktop version embeds Ollama directly, so first-time users get local LLMs without touching a terminal. It supports 60+ LLM providers, multiple vector databases (LanceDB by default, with Pinecone, Chroma, Qdrant, Weaviate, Milvus, and PGVector as options), and a no-code agent builder with MCP compatibility. Version 1.12.0 added automatic mode for tool calling, meaning capable models now handle tool selection without the @agent prefix.

PrivateGPT (v0.6.2, Apache 2.0 license, 57k+ GitHub stars) is the most developer-oriented of the three. The core offering is a FastAPI server with a full RAG pipeline you query programmatically. A Gradio UI is included for testing, but it’s not a polished app — it’s scaffolding. PrivateGPT follows the OpenAI API spec, supports streaming responses, and works with LlamaCPP and Ollama for local inference. Its last formal release was August 2024; active commits continued through early 2026, but the release cadence has slowed as the team focuses on the commercial Zylon enterprise product.

Comparison Table

Open WebUIAnythingLLMPrivateGPT
Latest versionv0.9.5 (May 2026)v1.12.1 (Apr 2026)v0.6.2 (Aug 2024)
LicenseCustom BSD (branding restrictions >50 users)MITApache 2.0
GitHub stars138k60.3k57k+
Primary interfaceWeb app + PWA + desktopWeb app + desktop (Ollama embedded)Gradio (testing-focused)
RAG supportYes (9 vector DB options)Yes (RAG-first design)Yes (API-first)
Local LLMsOllama or OpenAI-compat APIOllama embedded + 60+ providersLlamaCPP or Ollama
Multi-userYes (LDAP/OAuth/SSO)Yes (workspace permissioning)Minimal
Workspace isolationFolders and channelsDedicated workspacesCollections
Agent/tool usePipelines + function callingNo-code agent builder + MCPLimited
Desktop appYes (v0.0.20)Yes (Ollama bundled)No
Web search in RAGYes (15+ providers)NoNo
Active developmentVery activeVery activeSlower cadence

Install and Setup

Open WebUI

The fastest start is Docker:

docker run -d -p 3000:80 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

That assumes Ollama is running on the host at port 11434. The --add-host flag lets the container reach your host network. Browse to http://localhost:3000, create your admin account, and point it at your Ollama instance or paste in an OpenAI-compatible API key. Configuration lives in the UI, and first-run setup takes under 10 minutes.

Open WebUI itself is lightweight — 300–500 MB RAM for the service. VRAM requirements are entirely model-driven.

AnythingLLM

Docker:

docker pull mintplexlabs/anythingllm

docker run -d -p 3001:3001 \
  --cap-add SYS_ADMIN \
  -v ${PWD}/anythingllm:/app/server/storage \
  -e STORAGE_DIR="/app/server/storage" \
  mintplexlabs/anythingllm

Or skip Docker entirely: download the desktop app, open it, and Ollama is bundled. That’s genuinely zero-setup for newcomers — models download straight from the AnythingLLM interface. For teams who don’t want to run a server, this is the fastest path to a working local RAG setup.

AnythingLLM itself requires about 2 GB RAM and a 2-core CPU. Inference requirements are the same model-based math as everything else.

PrivateGPT

git clone https://github.com/zylon-ai/private-gpt
cd private-gpt
pip install poetry
poetry install --extras "ui llms-ollama embeddings-ollama vector-stores-qdrant"
PGPT_PROFILES=ollama make run

Docker Compose is also available, with profiles (cpu, cuda, ollama) that ship in the repo. The friction here is choosing your components upfront — LLM backend, embedding model, vector store — before you see the UI. Developers comfortable with Python environments and service composition will handle this in 20 minutes. Everyone else should budget more time and read the docs before starting.

RAG: Where the Real Differences Show

RAG is where these three tools diverge most sharply.

AnythingLLM has the most thoughtful RAG UX. Each workspace is a separate knowledge base. Documents added to “Client Project A” workspace never bleed into “Personal Notes.” This isn’t just a folder — it’s an isolated embedding space. The document manager shows what’s indexed, lets you remove files, and displays citation sources inline with responses. As of v1.12.1, embedding progress streams in real time rather than leaving you guessing whether the upload finished.

Open WebUI’s RAG is comprehensive but higher-configuration. Documents can live in the global Knowledge base or be scoped to specific chats or folders. Nine vector database options give flexibility, but the config surface is larger. The real differentiator is web search integration — 15+ providers mean you can blend document context with live web results in a single query. AnythingLLM can’t do this natively. For teams that need internal documents and current external information in the same chat, that combination is significant.

PrivateGPT’s RAG is an API, not a product. You ingest documents via POST request, query via GET, and receive structured JSON with citations. The Gradio UI wraps this for manual testing. If you’re building a custom application — a Slack bot, a customer support tool, an internal search system — PrivateGPT’s modular LlamaIndex-based architecture is a clean starting point. If you want to chat with documents without writing code, look elsewhere.

Multi-User and Team Deployments

Open WebUI is the clear leader for teams. LDAP, SCIM 2.0, OAuth, and SSO integration lets it slot into existing enterprise auth without a separate user management system. Role-based access controls restrict model access and feature visibility per user. One licensing caveat: deployments to more than 50 end users require either a commercial license or written permission from the Open WebUI team — the custom license explicitly restricts branding changes at scale.

AnythingLLM supports multi-user mode with workspace-level permissions and an admin panel. It’s not enterprise-grade auth, but it’s adequate for small teams sharing a self-hosted instance, with no license complications for larger user counts.

PrivateGPT’s multi-user story is minimal — the API is single-tenant by default. Adding user management means building it yourself.

When to Use Each

Open WebUI fits when:

  • You want the most feature-complete local chat workspace
  • Your team needs proper authentication (LDAP/OAuth)
  • You want web search results alongside document RAG
  • Voice, image generation, or multi-modal support matters to your workflow
  • You need a polished UI that non-technical colleagues will actually use

AnythingLLM fits when:

  • Document ingestion is your primary use case
  • You want the fastest onboarding — desktop app, nothing else to install
  • Multiple isolated knowledge bases map to your projects
  • MIT licensing with no commercial restrictions matters
  • You want MCP-compatible tool use without writing pipelines

PrivateGPT fits when:

  • You’re building a custom application on a RAG backend, not using a UI daily
  • You need an OpenAI-compatible API you can query programmatically
  • Apache 2.0 licensing meets compliance requirements
  • You’re comfortable operating a Python service deployment

When NOT to Use Each

Don’t use Open WebUI if you need to white-label or rebrand it for more than 50 users without a commercial agreement — the license explicitly restricts this. Also avoid it if your team wants zero-dependency setup; Open WebUI requires a separate Ollama or inference backend (the desktop app handles this for personal use, but server deployments need Ollama running separately).

Don’t use AnythingLLM if document ingestion isn’t your primary workflow. It’s feature-rich, but the workspace model is optimized around documents — if you mainly want multi-model chat, web browsing, and voice support, you’ll be underusing it. Enterprise SSO and LDAP also aren’t there; that’s Open WebUI territory.

Don’t use PrivateGPT as your primary user-facing interface. The last formal release was August 2024. While the repository shows commit activity into early 2026, the project’s attention has visibly shifted toward the Zylon enterprise product. For a stable, actively developed UI, the gap in the release cadence is a risk factor you’re accepting. Use it as infrastructure; don’t hand it to end users.

Hardware in Practice

Open WebUI, AnythingLLM, and PrivateGPT are all lightweight applications — the hardware cost is in the inference backend. Rough planning numbers:

  • Minimum useful: 16 GB system RAM, CPU-only for 1B–3B quantized models (slow but functional)
  • Comfortable for 7B models: 8 GB GPU VRAM (RTX 3070 or 4060), 16–32 GB RAM
  • 13B and above: 16–24 GB VRAM (RTX 4090, A6000, or dual-GPU setup), 32 GB+ RAM

All three tools connect to OpenAI-compatible APIs, which means you can run the UI locally against a cloud inference endpoint if your hardware isn’t there yet. For GPU rental that works with any of these tools, RunPod exposes endpoints compatible with all three. For hardware guidance — specific card comparisons, memory bandwidth tradeoffs, used vs. new GPU decisions — runaihome.com covers the local AI hardware stack in depth.

The Bottom Line

For most people, the real choice is between Open WebUI and AnythingLLM.

Open WebUI is the more powerful, feature-rich platform. Multi-model conversations, 15+ search providers in RAG, voice support, image generation integration, and enterprise auth add up to a local AI environment that handles daily work well beyond documents. If you’re running a local AI setup for anything other than pure document Q&A, Open WebUI is where the ecosystem is. The license restriction above 50 users is worth reading — it’s not a dealbreaker for personal or small team use, but it’s there.

AnythingLLM wins on document-centric workflows and setup simplicity. Workspace isolation is more thoughtfully designed than Open WebUI’s Knowledge implementation. The desktop app with embedded Ollama is the fastest path from “nothing installed” to “chatting with my PDFs” — nothing else comes close on that metric. MIT licensing means no commercial complications at any scale.

PrivateGPT belongs in a different category. Comparing it directly to Open WebUI and AnythingLLM is slightly unfair to all three. It’s a RAG API framework for developers building applications, not a UI product. Use it when you need an OpenAI-compatible RAG backend you own; skip it when you need something your team opens in a browser every morning.

For in-depth coverage of each tool: the Open WebUI review and the AnythingLLM review cover setup and feature depth separately. If you’re evaluating inference backends rather than UIs, the Ollama vs LM Studio vs llama.cpp comparison covers that layer.

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Sources

The hardware mentioned in this guide, with current prices on Amazon (affiliate links — at no extra cost to you, purchases help support this site):

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