Best Self Hosted Alternatives to LobeChat Cloud

Ollama is a lightweight runtime for running large language models on your machine and exposing them through a simple local service. It provides a CLI for model lifecycle operations and a REST API for integrating chat, text generation, and embeddings into applications.

Key Features

• Pull and run many popular open and open-weight models with a single command
• Local REST API for text generation and chat-style conversations
• Embeddings generation for semantic search and RAG workflows
• Model customization via Modelfiles (system prompts, parameters, and composition)
• Import and package models from GGUF and other supported formats
• Supports multimodal models (vision-language) when using compatible model families

Use Cases

• Local developer-friendly LLM endpoint for apps, agents, and tooling
• Private on-device chat and document workflows using embeddings
• Prototyping and testing prompts and model variants with repeatable configurations

Limitations and Considerations

• Hardware requirements can be significant for larger models (RAM/VRAM usage varies by model size)
• Advanced capabilities depend on the specific model (for example, tool use or vision support)

Ollama is well-suited for teams and individuals who want a consistent way to run and integrate LLMs locally without relying on hosted inference. Its CLI-first workflow and straightforward API make it a practical foundation for building LLM-powered applications.

Open WebUI is a web-based, extensible AI interface that provides a unified GUI for interacting with local and cloud LLMs. It supports multiple LLM runners and OpenAI-compatible APIs, built-in RAG, artifact storage, and collaboration features.

Key Features

• Multi-runner support (Ollama and OpenAI-compatible endpoints) and built-in inference integrations for flexible model selection
• Local Retrieval-Augmented Generation (RAG) with support for multiple vector databases and content extractors
• Image generation and editing integrations with local and remote engines; prompt-based editing workflows
• Granular role-based access control (RBAC), user groups, and enterprise provisioning (SCIM, LDAP/AD, SSO integrations)
• Persistent artifact/key-value storage for journals, leaderboards, and shared session data
• Progressive Web App (PWA) experience, responsive UI, and multi-device support
• Native Python function-calling tools (BYOF) and a web-based code editor for tool/workspace development
• Docker/Kubernetes deployment options, prebuilt image tags for CPU/GPU and Ollama bundles
• Production observability with OpenTelemetry traces, metrics and Redis-backed session management

Use Cases

• Teams wanting a central, auditable chat interface to query multiple LLMs and manage permissions
• Knowledge workers and developers using local RAG pipelines to query private document collections securely
• Experimentation and model comparison workflows combining multiple models, image tools, and custom functions

Limitations and Considerations

• Advanced features (model inference, heavy image generation) require external runners or GPU resources; performance depends on the chosen backend
• Some enterprise integrations and optional storage backends require additional configuration and credentials
• Desktop app is experimental; recommended production deployment paths are Docker, Docker Compose or Kubernetes

Open WebUI is positioned as a flexible interface layer for LLM workflows, emphasizing provider-agnostic integration, RAG, and enterprise features. It is suited for teams that need a full-featured, customizable web UI for local and cloud model workflows.

AnythingLLM is a full-stack AI application for building a private ChatGPT-like experience around your own documents and content. It supports local and hosted LLMs, integrates with multiple vector database backends, and organizes content into isolated workspaces for cleaner context management.

Key Features

• Retrieval-augmented generation (RAG) to chat with PDFs, DOCX, TXT, CSV, codebases, and more
• Workspace-based organization with separated context and optional document sharing
• AI agents, including a no-code agent builder and MCP compatibility
• Supports local and commercial LLM providers (including Ollama and llama.cpp-compatible models)
• Multiple vector database options (default local-first setup, with external backends available)
• Multi-user deployment with permissions (Docker deployment)
• Embeddable website chat widget (Docker deployment)
• Developer API for integrations and automation

Use Cases

• Internal knowledge base chat for teams (policies, runbooks, product docs)
• Private document Q&A for sensitive datasets and client files
• Building agent-assisted workflows that reference curated business content

AnythingLLM is a strong choice when you want a configurable, privacy-conscious AI application that can run locally or on a server, while staying flexible about which LLM and vector database you use.

LibreChat is an open-source, self-hostable AI chat application that provides a ChatGPT-style interface while supporting many AI providers and OpenAI-compatible endpoints. It focuses on multi-user deployments, flexible model switching, and extensible agent/tool workflows.

Key Features

• Multi-provider model selection (including OpenAI-compatible APIs) with per-chat switching and presets
• Agents and tool integrations, including MCP support for connecting external tools
• Code Interpreter capabilities for sandboxed code execution and file handling
• Multimodal interactions: chat with files and analyze images (provider-dependent)
• Generative “artifacts” for creating code outputs (such as React/HTML) and Mermaid diagrams in chat
• Conversation and message search, plus import/export of conversations
• Multi-user authentication options (OAuth2, LDAP, and email login) and basic moderation/spend controls

Use Cases

• A unified internal AI chat portal for teams using multiple LLM vendors and endpoints
• Building no-code or low-code AI assistants that can call tools, search, and execute code
• Secure, self-hosted chat workflows for analyzing documents and iterating on code artifacts

Limitations and Considerations

• Some capabilities (multimodal, image generation, web search, specific tools) depend on configured providers and credentials
• Running code execution and tool integrations increases operational and security requirements and should be carefully sandboxed and access-controlled

LibreChat fits organizations and individuals who want a single, customizable chat UI for many models, with advanced features like agents, tool connectivity, and searchable conversation history. It is best suited for deployments that need multi-user access and flexible endpoint configuration.

SecureAI Tools is a self-hosted web app for private AI productivity, focused on AI chat and chatting with your own documents. It can run models locally via Ollama or connect to OpenAI-compatible providers, and includes built-in access controls for multi-user use.

Key Features

• Chat interface for interacting with LLMs
• Document Q&A (PDF support) with offline document processing
• Local model inference via Ollama, with optional GPU acceleration
• Support for remote OpenAI-compatible APIs as an alternative to local inference
• Built-in email/password authentication and basic user management
• Optimized self-hosting experience with Docker Compose and setup scripts
• Integrations including Paperless-ngx and Google Drive

Use Cases

• Private, family or small-team AI assistant with account-based access
• Ask questions and summarize PDFs and organized document collections
• Run local LLMs on a workstation or home server to keep data on-premises

Limitations and Considerations

• Document chat is currently focused on PDFs; broader file-type support is still evolving
• Local inference performance depends heavily on available RAM/GPU, especially on non-Apple systems

SecureAI Tools is a practical option for users who want a privacy-oriented AI chat experience combined with document Q&A, and the flexibility to choose between local models and OpenAI-compatible providers.

Recommendarr is a web application that generates personalized movie and TV show recommendations using data from your existing media library and watch history. It integrates with popular media managers and can use cloud or local LLM providers to tailor suggestions to your preferences.

Key Features

• AI-powered recommendations based on Radarr and Sonarr libraries
• Watch history analysis via Plex and Jellyfin, with optional Tautulli and Trakt integration
• Supports multiple AI backends, including OpenAI-compatible APIs and local LLMs
• Web UI with configurable recommendation settings (count and model parameters)
• Light/dark theme support and poster display with fallbacks
• Built-in authentication with optional OAuth login support

Use Cases

• Discover new movies and series that match your existing collection
• Generate recommendations based on what household members actually watch
• Run a local-LLM recommendation workflow for a privacy-focused media setup

Limitations and Considerations

• Recommendation quality depends heavily on the completeness of your library metadata and watch history
• External access should be deployed behind a properly configured reverse proxy and authentication

Recommendarr is a practical companion for Sonarr/Radarr-centric media stacks, combining library context with LLMs to produce tailored suggestions. It fits well in Plex or Jellyfin environments where you want recommendations driven by your own viewing habits.