Guide · 2026-05-16

OpenWebUI vs LibreChat — Best Self-Hosted ChatGPT Clone

Two open-source ChatGPT clones, two very different philosophies. We benchmark both on real hardware and pick a winner for 2026.

By Mohamed Meguedmi · 11 min read

The self-hosted ChatGPT race in 2026 is effectively a two-horse contest. Open WebUI (the project formally dropped the space in late 2025 marketing, but the docs still use both) and LibreChat together account for the overwhelming majority of GitHub stars in the category, with Open WebUI passing 95k and LibreChat past 28k as of this month. Both are MIT-adjacent licensed, both ship Docker images, and both are credible alternatives to a $20/month ChatGPT Plus seat. They are not, however, interchangeable.

The BestLLMfor editorial team deployed identical workloads on both stacks across the last six weeks: a 4-user team, a 25-user team, and a single-developer setup running Qwen3-Coder 32B Q4_K_M locally. This guide is the distilled verdict, with numbers.

The philosophy split: local-first vs router-first

Understanding the architectural divide explains 80% of the feature differences you will hit in production.

Open WebUI was born inside the Ollama ecosystem. Originally called ollama-webui, it was renamed in early 2024 and broadened to support OpenAI-compatible endpoints, but its center of gravity is still local inference. The UI assumes you have models you can pull, list, hot-swap, and fine-tune through Modelfiles. Its Pipelines framework lets you write Python middleware that intercepts requests — RAG, filters, monitoring, custom routing — all in-process.

LibreChat is fundamentally a multi-provider router with a polished frontend. Its endpoint abstraction means a single conversation can hop between GPT-5, Claude Opus 4.7, Gemini 2.5 Pro, and a local Ollama model. The agent builder is the closest open-source thing to OpenAI's GPTs, and the new Code Interpreter (sandboxed by the LibreChat-maintained librechat-code-interpreter service) actually works without you provisioning Kubernetes.

If you map this onto a Cartesian plane: Open WebUI is the vertical integrator (deep on local), LibreChat is the horizontal aggregator (broad on cloud). Pick accordingly.

Hardware footprint and real-world benchmarks

We measured both on a Linux host (Ryzen-class CPU, 32 GB RAM, RTX 4090 24 GB) with the same Ollama backend serving Qwen3-Coder 32B at Q4_K_M. Numbers below are steady-state, post-warmup, with one logged-in user and a 4k-token chat history.

Metric	Open WebUI 0.5.x	LibreChat 0.7.x
Container count	1 (+ Ollama)	4–6 (+ Ollama)
Idle RAM (all services)	~420 MB	~1.21 GB
Cold-start to login screen	4.1 s	11.7 s
First-token latency (local)	+38 ms vs raw Ollama	+71 ms vs raw Ollama
Tokens/sec passthrough (Q4_K_M)	62 tok/s	61 tok/s
Disk image size (pulled)	1.8 GB	2.4 GB + Mongo (~700 MB)
Persistent storage required	SQLite (default) or Postgres	MongoDB required

Tokens-per-second is essentially identical because both are thin shells over the upstream API — your inference backend dominates. The interesting numbers are RAM and container count: on a $5/month VPS, Open WebUI is comfortable. LibreChat needs at least the 2 GB tier and ideally 4 GB once Meilisearch indexes a few months of chats.

Authentication, RBAC, and multi-tenancy

Both projects have made enormous progress here in 2025. Both now ship OIDC out of the box. The differences are in the granularity.

Open WebUI

Open WebUI introduced group-based RBAC in v0.4 and refined it through v0.5. You can scope per-model, per-tool, and per-knowledge-base access to specific groups. The admin panel is genuinely usable — no JSON editing required. SCIM provisioning landed in late 2025, which is what tipped a few enterprise pilots in its favor.

LibreChat

LibreChat's permission model is more conversation-centric: who can use which endpoint, what spend cap they have per day/week/month, and which agents are public. The newer librechat.yaml config gives you fine-grained control over which providers each role sees. This is uniquely valuable when you do not want junior staff burning Claude Opus tokens.

Capability	Open WebUI	LibreChat
OIDC / SSO	Built-in	Built-in
SCIM provisioning	Yes (v0.5+)	Manual / via OIDC claims
Per-user spend cap	No native — via Pipeline	Yes, native (token budgets)
Per-group model access	Yes, native	Yes, via librechat.yaml
Audit log	Basic	Basic + transaction history
Shared workspaces	Knowledge bases + prompts	Agents + shared presets

RAG, knowledge, and document chat

RAG is where the two diverge most visibly for end users.

Open WebUI ships a complete pipeline: upload a PDF, it chunks via langchain, embeds with your chosen model (Ollama-served nomic-embed-text by default, swappable to OpenAI or any HF model), stores in ChromaDB or pgvector, and retrieves on each turn. You can build Knowledge collections — basically named vector stores — and attach them to specific models via the Workspace UI. Hybrid search (BM25 + dense) shipped in v0.5 and meaningfully improves recall on technical docs.

LibreChat's RAG is a separate service (rag_api) that uses a vector DB (pgvector by default). It works, but the UX is more “attach files to this conversation” than “build a persistent knowledge base”. Out of the box you cannot easily expose the same corpus to multiple users without duplication. The team is shipping shared file collections in the 0.8 milestone, but as of this writing it is not on parity with Open WebUI.

For teams whose primary use case is “chat with our internal docs”, Open WebUI is the safer bet today. Validate retrieval quality with your own corpus — our methodology page documents the eval harness we use.

Multi-provider support and the agent question

If your stack already includes API keys for 4+ frontier providers, LibreChat is genuinely a different product class. It supports OpenAI, Anthropic, Google (Gemini + Vertex), AWS Bedrock, Azure OpenAI, Mistral, Groq, OpenRouter, Ollama, and any OpenAI-compatible endpoint, with per-endpoint customization (model lists, default parameters, header injection). The endpoints block in librechat.yaml is the project's superpower.

Its Agents feature, generally available since 2025, lets non-developers compose tools (web search, code interpreter, MCP servers, OpenAPI specs) into a packaged assistant. You can pin an agent to a specific model — a real cost lever. The Agents docs are worth reading even if you end up choosing Open WebUI.

Open WebUI does multi-provider too, but the UX assumes you mainly want local plus one or two cloud fallbacks. Tools are exposed via OpenAPI servers (a clean spec, but you write the server) or the new MCP-to-OpenAPI proxy. The model is more extensible, less turnkey.

Cost of ownership over 12 months

Self-hosting is “free” only if you forget about the VPS, the maintenance hours, and the cloud API spend the UI will inevitably make easier. Below is the comparison for a 10-user team, assuming moderate usage and a mix of local + GPT-class queries. Plug your own numbers into our cost calculator.

Cost line (USD, 12 mo)	Open WebUI	LibreChat	ChatGPT Team (baseline)
Hosting (VPS or on-prem amortized)	$120	$240	$0
Inference API spend	~$600	~$840 (easier to over-spend)	Included
Local GPU electricity (4090, est.)	~$180	~$180	$0
Admin time (hrs × $75)	12 × $75 = $900	20 × $75 = $1,500	4 × $75 = $300
Per-seat license	$0	$0	10 × $30 × 12 = $3,600
Total	~$1,800	~$2,760	~$3,900

Both self-hosted options break even against ChatGPT Team well within 12 months, but LibreChat's admin time line is the silent killer. Mongo upgrades, Meilisearch reindex, and config drift across librechat.yaml, .env, and Docker Compose add up. Open WebUI's single-binary mental model genuinely saves hours.

Installation: 10-minute vs 30-minute first run

Open WebUI (single command)

docker run -d -p 3000:8080 \
  --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

If Ollama is running on the same host on its default port, the UI auto-detects it. First admin user is whoever signs up first. That is the entire happy path.

LibreChat (Docker Compose)

1. Clone the repo: git clone https://github.com/danny-avila/LibreChat.git
2. Copy .env.example to .env; set MONGO_URI, JWT_SECRET, CREDS_KEY, CREDS_IV (generate via the project's key tool).
3. Drop in librechat.yaml with the endpoints you want exposed.
4. Run docker compose up -d; wait for Mongo, Meilisearch, rag_api, and api to all become healthy.
5. Visit http://localhost:3080, register the first user, then set ALLOW_REGISTRATION=false and restart.

The LibreChat install is well-documented but unforgiving of typos. The CREDS_KEY/CREDS_IV step is the most common foot-gun — get the length wrong and the API silently fails to encrypt provider keys.

Extensibility, MCP, and the API story

Both projects have embraced the Model Context Protocol in 2025. Open WebUI proxies MCP through its OpenAPI tools framework; LibreChat has native MCP server entries in librechat.yaml. Both work.

If you are building tooling on top, both expose a chat-completions-compatible REST API you can hit from scripts or sister services. The BestLLMfor team publishes its model-evaluation results under a CC BY 4.0 public API, and the companion quelllm-mcp open-source MCP server can plug straight into either UI to give every chat live access to up-to-date model rankings.

The verdict

Use case	Recommendation	Why
Solo developer with a local GPU	Open WebUI	Single container, Ollama-native, best RAG UX out of the box.
Small team (5–25) on mixed cloud APIs	LibreChat	Per-user spend caps, agent builder, broadest provider catalog.
Enterprise pilot (SSO, RBAC, audit)	Open WebUI	SCIM, granular group permissions, simpler ops footprint.
“ChatGPT for our internal docs”	Open WebUI	Persistent knowledge collections with hybrid search.
Heavy multi-model agent workflows	LibreChat	Agents + Code Interpreter + MCP, all first-class.
Air-gapped / fully offline	Open WebUI	One container, no external services, SQLite mode works.

The honest answer for any team larger than ~30 people: run both. Open WebUI behind your office for the local-RAG workflows; LibreChat for power users who need to ping five providers and orchestrate agents. They cost about the same, and the redundancy is cheap insurance.