Best Self-hosted Alternatives to Microsoft Copilot

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.

Khoj is an open-source personal AI platform that combines chat, semantic document search, custom agents and scheduled automations. It can run locally or as a cloud-hosted service and integrates with local or remote LLMs to answer questions, generate content and automate research.

Key Features

• Multi-client access: web, desktop, Obsidian, Emacs, mobile (PWA) and chat integrations (e.g., WhatsApp).
• Model-agnostic LLM support: connect local GGUF models or remote OpenAI-compatible, Anthropic and Google-compatible endpoints; supports on-device and cloud models.
• Semantic search and embeddings: document ingestion (PDF, Markdown, Word, org-mode, Notion, images) with vector storage and retrieval for fast, contextual search.
• Custom agents and automations: build agents with distinct personas, tools and knowledge bases; schedule research tasks and email newsletters.
• Document processing and code tools: built-in extractors, simple code execution sandbox support (local Terrarium or remote sandboxes) and image generation features.
• Enterprise & self-hosting options: deploy via Docker or pip, use Postgres with pgvector for embeddings, and configure authentication and domains.

Use Cases

• Personal knowledge management: query a private document corpus and get grounded answers across notes, PDFs and files.
• Research automation: schedule recurring research queries and receive summarized results by email.
• Team/private deployments: host a private assistant for a team with custom agents, model selection and on-premise data control.

Limitations and Considerations

• Some optional integrations require extra setup or external services (e.g., code sandboxes, email providers); self-hosting needs correct environment configuration.
• A few plugins/integrations may be unmaintained or platform-specific; users should check the chosen connectors and follow the docs for compatibility and maintenance status.

Khoj is designed to be extensible and model-agnostic, emphasizing private data control and flexible deployment. It is suited for individuals and teams who need a searchable, automatable assistant that can run with local or cloud language models.

Onyx Community Edition is an open-source, self-hostable AI platform that combines a team chat UI with enterprise search and retrieval-augmented generation (RAG). It is designed to work with a wide range of LLM providers as well as locally hosted models, including deployments in airgapped environments.

Key Features

• AI chat interface designed to work with multiple LLM providers and self-hosted LLMs
• RAG with hybrid retrieval and contextual grounding over ingested and uploaded content
• Connectors to many external knowledge sources with metadata ingestion
• Custom agents with configurable instructions, knowledge, and actions
• Web search integration and deep-research style multi-step querying
• Collaboration features such as chat sharing, feedback collection, and user management
• Enterprise-oriented access controls including RBAC and support for SSO (depending on configuration)

Use Cases

• Company-wide AI assistant grounded in internal documents and connected tools
• Knowledge discovery and enterprise search across large document collections
• Building task-focused AI agents that can retrieve context and trigger actions

Limitations and Considerations

• Some advanced organization-focused capabilities may differ between Community and Enterprise editions
• Retrieval quality and permissions mirroring depend on connector availability and configuration

Onyx CE is a strong fit for teams that want an extensible, transparent AI assistant and search layer over internal knowledge. It emphasizes configurable retrieval, integrations, and deployability across diverse infrastructure setups.

Leon is an open-source personal assistant designed to run on your own server and help you automate tasks through a modular “skills” system. It supports both text and voice interaction, with optional offline operation to keep data under your control.

Key Features

• Modular skills architecture to add and share new capabilities
• Text-based assistant via a web interface
• Voice assistant capabilities with speech-to-text and text-to-speech (cloud or offline options)
• Local/offline mode to reduce reliance on third-party services
• CLI-based installation and management
• Hybrid stack with a Node.js core and Python integration for certain NLP/skill components

Use Cases

• Personal workflow automation (recurring tasks, quick actions, utility commands)
• Building and sharing custom assistant skills for a household or team
• Privacy-conscious local assistant for text/voice interactions

Limitations and Considerations

• The project is undergoing a major architectural rewrite; the develop branch may be unstable and documentation may lag behind implementation

Leon is a good fit for users who want a customizable, privacy-oriented assistant they can extend with their own skills. Its modular design makes it suitable as a long-term personal automation hub as capabilities evolve.

Paperless-AI is an AI-powered extension for Paperless‑ngx that automates document classification, metadata extraction and semantic search. It integrates with OpenAI-compatible APIs and local model backends to provide chat-style Q&A over a Paperless‑ngx archive.

Key Features

• Automated document processing: detects new documents in Paperless‑ngx and extracts title, tags, document type, and correspondent.
• Retrieval-Augmented Generation (RAG) chat: semantic search and contextual Q&A across the full document archive.
• Multi-backend model support: works with OpenAI-compatible APIs, Ollama (local models), DeepSeek-r1, Azure and several other OpenAI-format backends.
• Manual review UI: web interface to manually trigger AI processing, review results, and adjust settings.
• Smart tagging and rule engine: configurable rules to control which documents are processed and what tags are applied.
• Docker-first distribution: official Docker image and docker-compose support for containerized deployment and persistent storage.

Use Cases

• Quickly find facts across scanned bills, contracts and receipts via natural-language Q&A instead of manual search.
• Automatically tag and classify incoming documents to reduce manual filing and speed up archival workflows.
• Create structured metadata from free-text documents for downstream automation or reporting.

Limitations and Considerations

• Quality and consistency of automatic tags and correspondents varies by model and prompt; some users report noisy or incorrect tags that require manual cleanup.
• Resource behavior with local model backends (e.g., Ollama) can be heavy; users have reported long-running sessions or elevated GPU/CPU usage depending on model choice and volume.
• Processing can halt on model/API errors (for example, context-length or API failures); robust retry/monitoring may be required in large archives.
• Requires a running Paperless‑ngx instance and appropriate API credentials and model/back-end configuration to operate.

Paperless-AI provides an accessible way to add AI-driven classification and semantic search to a Paperless‑ngx archive, with flexible backend choices and a modern web UI. It is best suited for users who want automated tagging and conversational access to large document collections but should be configured and monitored to manage resource use and tag quality.

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.