Looking to self-host your own AI assistant and keep your data under your control? Two of the most popular open-source ChatGPT-like platforms, OpenWebUI and LibreChat, each offer a compelling suite of features to help you spin up a secure, user-friendly chatbot on your own hardware or private cloud. In this post, we’ll compare their core functionalities, explore typical deployment scenarios, and show how you can integrate either platform for free via Requesty (which provides free credits and multi-model routing to AI powerhouses like Gemini 2.5 Pro, Claude 3-7 Sonnet, Deepseek V3, OpenAI o3 mini, and OpenAI o1).
1. Quick Overview
OpenWebUI is celebrated for its pipeline-based architecture, letting you mix, match, and extend various language models and search or retrieval plugins. It’s container-friendly, making deployment straightforward with Docker or Podman. Admins appreciate OpenWebUI’s out-of-the-box role-based access control, whitelisting, and “super admin” account for easy user management.
LibreChat, on the other hand, offers a very ChatGPT-like experience with an interface that’s almost a pixel-perfect replica of ChatGPT. It supports a wide range of authentication methods (e.g., GitHub, Azure AD, AWS Cognito, Keycloak) and includes unique features like Artifacts (inline rendering of HTML, React components, and more), a prompt library, and multi-model support via custom endpoints. While the advanced permission system is still evolving, it already excels in multi-modal capabilities and forking conversations.
2. Comparison Table: Key Features
Feature | OpenWebUI | LibreChat |
UI & Workflow | Clean, minimal, pipeline-based approach. | ChatGPT-style interface, almost identical to the real thing. |
Authentication | Basic RBAC, first user = super admin. | Robust methods: social login, LDAP, Keycloak, etc. |
Model Support | Built-in local model hosting + custom remote endpoints. | In-app model menu for OpenAI, Anthropic, Gemini, plus custom endpoints. |
Multi-Modal / Images | RAG with doc loading, video/YouTube pipelines. | Image input, data extraction, plus optional “Artifacts” for real-time rendering. |
Conversation Management | Tagging, chat cloning, memory systems, RLHF annotation. | Forking conversations, advanced editing, preset system. |
Plugins/Extensions | Highly flexible pipeline scripts. | Plugin store (some outdated), in progress updated mechanism. |
Deployment | Docker, Docker Compose, Podman, or Python venv. | Docker, npm, and multiple cloud setups; new plugin support in progress. |
User Permissions | Straightforward roles (admin vs. user) + whitelisting. | No role-based UI yet, but major multi-user authentication. |
File Q&A (RAG) | Hybrid search (BM25 + CrossEncoder) or local DB index. | Integrates with an optional RAG API (LangChain + pgvector). |
Cost / Rate Limits | No built-in cost constraints, rely on third-party routing | Same – cost mgmt handled by external router or API provider |
Community / Ecosystem | Growing but partial docs, user-driven pipeline additions. | Large active community, frequent commits, variety of docs. |
3. Highlights of Each Platform
3.1 OpenWebUI
Pipeline Flexibility Perhaps the biggest draw: you can chain multiple steps—like retrieving documents, doing a secondary classification, or applying custom transforms—before finalizing an LLM response.
Role-Based Access and Whitelisting Simple but effective admin rights for controlling who gets to see which features or models.
Easy Setup Many users praise the Docker and Podman-based install, making it a breeze to get up and running quickly.
Potential Drawbacks
The UI and advanced chat features may feel a bit more “barebones” compared to LibreChat’s ChatGPT-like interface.
Some users report occasional breakages or required tweaks for local GPU usage.
3.2 LibreChat
ChatGPT-Like UI For teams that already love ChatGPT, the transition is practically seamless.
Artifacts Feature Instantly render React components, diagrams, or even 3D animations within the chat—useful for code review or design collaboration.
Assistant Builder & Prompt Library Save custom instructions, connect external APIs (tools), and create your own “mini-agents.” Perfect if you want more advanced usage like code generation + external fetch.
Multi-Modal Supports image uploads, file-based Q&A, and optional DALL·E 3 plugin for image creation.
Potential Drawbacks
No fully fleshed-out role-based permission system (yet). Everyone sees the same UI and model list unless you tweak config files.
Plugin store is “in flux,” and some entries may be outdated.
4. Using Either Platform for Free with Requesty
One of the biggest questions for self-hosted solutions is model access. Both OpenWebUI and LibreChat support hooking up to any API that’s OpenAI-compatible (including OpenRouter or custom endpoints).
But if you’d like:
Multiple AI providers under one roof,
Free credits to start testing,
Budget & failover controls without writing custom scripts,
then Requesty is a perfect complement. Simply configure your Requesty API key in either platform’s environment variables or custom endpoints area and point it to the desired model ID—for example, “anthropic/claude-3-7-sonnet” or “openai/o3-mini.”
Steps to Integrate
Sign Up at requesty.ai to get your free credits and API key.
In OpenWebUI: Add your Requesty endpoint in the config (e.g.,
https://router.requesty.ai/v1) and set your Bearer token. Then you can choose among your configured models directly in the UI.In LibreChat: Use the “Custom Endpoint” or “OpenAI-compatible” entry, set your base URL to Requesty’s router, and paste your Requesty key.
That’s it—both platforms can now route your queries to whichever large language model suits you best, all while letting you keep track of usage, handle failovers, or set cost guardrails via Requesty.
5. Which One Should You Choose?
Opt for OpenWebUI if:
You need a streamlined pipeline approach for chaining tasks or mixing retrieval steps.
Minimal user management is fine, and you appreciate a container-based install that’s fairly quick to adopt.
You want simpler or more direct local GPU usage to run open-source LLMs on your own hardware.
Opt for LibreChat if:
You want a close ChatGPT-like experience for end-users, including advanced UI features like forking conversations, multi-modal inputs, and “Artifacts” for rendered components.
You require extensive authentication methods (e.g., Keycloak, Azure, LDAP) out of the box.
You’re excited about the possibility of building custom assistant flows or using an integrated prompt library.
Either way, you’ll end up with a capable, self-hosted ChatGPT alternative that preserves your data privacy and spares you from monthly seat licenses.
6. Next Steps
Set Up Your Platform
OpenWebUI GitHub or LibreChat GitHub.
Check their docs for the recommended install paths (Docker/Podman vs. npm, etc.).
Integrate Requesty
Sign up at requesty.ai to grab your free credits.
Configure your base URL and API key in either platform’s environment files or custom endpoint fields.
Test Your LLM(s)
Try simple tasks with OpenAI o3 mini or Claude 3-7 Sonnet, then experiment with more advanced workflows—like RAG or code generation.
Expand & Iterate
Explore advanced features: pipelines in OpenWebUI or Artifacts in LibreChat.
Tweak your usage policies or fallback rules in Requesty to optimize cost and reliability.
Conclusion
OpenWebUI and LibreChat each deliver an impressive, self-hosted AI chat experience—one that your organization fully owns. OpenWebUI might be more appealing to devops teams seeking a modular pipeline approach and simpler user controls, whereas LibreChat excels for teams who want the polish of ChatGPT’s UI, multi-modal features, and robust authentication options. Combine either solution with Requesty’s free credits and multi-model routing, and you’ll have an AI setup that’s both powerful and cost-conscious—truly the best of both worlds.