Best Self Hosted Alternatives to Botpress Cloud

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.

Tiledesk is an open-source conversational platform combining multichannel live chat, visual bot-building, and AI-powered assistants. It provides tools to create LLM-enabled chatbots, manage human handoffs, and integrate knowledge bases for retrieval-augmented responses.

Key Features

• Multichannel messaging: web chat widget, WhatsApp, and other channels with unified conversation context
• Visual no-code Design Studio to build conversational flows and prompt chains for AI agents
• AI Agents and RAG: multiple knowledge bases, hybrid full-text + semantic search, and prompt chaining for contextual answers
• Human-in-the-loop: smart escalation, agent dashboard, and AI copilots that assist human operators in real time
• Deployment options: Docker Compose and Kubernetes (Helm charts) with a microservice stack including server, dashboard, chat clients, and MongoDB
• Extensible integrations: APIs and webhooks for connecting external services, calendars, email, and ecommerce platforms
• Self-learning workflows: extract insights from conversations to update knowledge and improve agent responses

Use Cases

• Automated customer support that answers common queries, creates tickets, and escalates complex issues to human agents
• Conversational commerce and product assistants that present catalogs, build carts, and handle ordering across channels
• Internal help desks and knowledge retrieval where AI agents surface documents and context from company KBs

Limitations and Considerations

• Helm charts and provided deployment templates are intended as starting points and include an embedded MongoDB container; they require customization and hardening for production use
• Some enterprise features and private Docker images are gated behind paid credentials and are not available in the community distribution
• Advanced AI capabilities depend on configuring LLM providers or external/model-serving infrastructure, which requires additional setup and resource planning

Tiledesk is suitable for teams that need an integrated conversational platform combining bots, human agents, and knowledge-driven AI. It emphasizes extensibility and multi-agent workflows while expecting operators to adapt deployments and integrations for production environments.