Orbit
A self-hosted AI infrastructure for private RAG and multi-model applications.
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What is Orbit?
ORBIT (Open Retrieval-Based Inference Toolkit) is a modular, self-hosted AI infrastructure toolkit that provides a unified OpenAI-compatible API for open-source AI inference models. It enables organizations to run AI models on their own infrastructure—whether on-premise, cloud, or hybrid—maintaining complete control over their data while eliminating dependence on commercial AI APIs. The toolkit is designed for privacy, control, and independence from proprietary models.
Key features include privacy-first data sovereignty (no data sent to external services), flexible deployment options (cloud, on-premise, hybrid), full customization with custom model selection and domain-specific adaptations, RAG support with multiple vector database options (ChromaDB, Milvus, SQLite), multilingual support for 12+ languages with automatic language detection, content moderation with configurable safety checks, retrieval over SQL, NoSQL, HTTP, GraphQL, files, web content, and vector stores, circuit breakers and failover for production reliability, API key management with MongoDB, RBAC, audit logs, and rate limits. It also includes intent adapters, composite adapters, diagnostics, and reusable templates for production RAG systems.
ORBIT is ideal for government agencies requiring sovereign AI capabilities, organizations with strict data privacy requirements (HIPAA-compliant medical systems, secure financial advisory assistants, confidential legal research tools), enterprises needing to maintain control over AI infrastructure, customer support teams integrating AI with organizational knowledge bases, internal knowledge management teams building document-based Q&A systems, education teams creating interactive learning assistants tailored to course materials, and any team wanting AI connected to real business data without sending everything to a SaaS vendor.
The toolkit is open-source under the Apache 2.0 license, built with Python, FastAPI, TypeScript, and React, and gets running in 60 seconds via Docker or manual setup. It includes a web chatbot example, CLI example, Python client, admin panel at localhost:3000/admin, and supports GPU acceleration for accelerated inference.
Orbit pricing
Pricing model: Freemium
ORBIT is completely free and open-source under the Apache 2.0 license. There are no paid plans, subscription fees, or token-based APIs. The toolkit eliminates commercial API dependencies, meaning you do not pay per token or per request to third-party AI providers when using local models. You only incur infrastructure costs for hosting your own servers (cloud or on-premise), GPU hardware if using accelerated inference, and any external model providers you choose to connect to. Default API key is 'default-key' for local testing. The admin panel is free to access at localhost:3000/admin with default credentials (admin/admin123).
Orbit pros
- Complete data sovereignty—data never leaves your infrastructure
- No commercial API dependencies or paid subscriptions
- Open-source under Apache 2.0 license with full transparency
- One OpenAI-compatible API across local and hosted providers
- Flexible deployment: cloud, on-premise, or hybrid environments
- Supports 200+ models across 20+ providers including Anthropic, OpenAI, Google, AWS, Azure
- RAG support with retrieval over SQL, NoSQL, HTTP, GraphQL, files, web content, and vector stores
- Multilingual support for 12+ languages with automatic language detection
- Production controls: circuit breakers, failover, parallel fan-out, quota-aware throttling
- Configurable content moderation with strict/fuzzy modes and dedicated moderation models
- API key management with MongoDB, RBAC, audit logs, and rate limits
- Gets running in 60 seconds via Docker compose
- GPU acceleration support for accelerated inference
- Intent adapters, composite adapters, diagnostics, and reusable templates
- No vendor lock-in—freedom to choose inference providers and change models without rewriting apps
- Context-aware AI responses designed for accuracy and relevance
- Admin panel available at localhost:3000/admin for management
- Python, CLI, and web chatbot clients included
- Easily adaptable for domain-specific needs like healthcare, finance, legal, education
Orbit cons
- Requires self-hosting infrastructure (Python 3.12+, MongoDB, Ollama, ChromaDB)
- More complex setup than thin wrappers around single LLM providers
- Requires GPU for accelerated inference (optional but recommended for performance)
- Active development with no official releases yet on GitHub
- Only 9 GitHub stars indicating small community and limited third-party support
- Milvus support for vector search still coming soon (ChromaDB only currently)
- Requires technical expertise to configure adapters and intent templates
- No hosted SaaS option—must manage your own infrastructure and maintenance
- Documentation primarily via GitHub README, less formal than commercial products
Frequently asked questions about Orbit
What is ORBIT?
ORBIT (Open Retrieval-Based Inference Toolkit) is a modular, self-hosted toolkit that provides a unified OpenAI-compatible API for open-source AI inference models. It enables you to operate without paid APIs by hosting AI models on your own infrastructure, maintaining complete control over your data, and eliminating commercial dependency. It is an adaptable, open-source context-aware inference engine designed for privacy, control, and independence from proprietary models.
How do I install ORBIT?
You can install ORBIT via Docker by running: git clone https://github.com/schmitech/orbit.git && cd orbit/docker && docker compose up -d. The server will be available at http://localhost:3000. For manual setup, you need Python 3.12+, MongoDB for API key management, Ollama for inference, and ChromaDB or SQLite. Clone the repository, cd to orbit/server, run ./setup.sh, install Ollama models (gemma3:1b, nomic-embed-text), then run ./start.sh.
What are the system requirements for ORBIT?
ORBIT requires Python 3.12+, MongoDB for API key management, Ollama Server for inference, and ChromaDB or SQLite as vector database. Optional requirements include GPU for accelerated inference and Elasticsearch for logging. For Docker deployment, you need Docker and Docker Compose. For GPU acceleration, use docker compose -f docker-compose.yml -f docker-compose.gpu.yml up -d.
Is ORBIT free to use?
Yes, ORBIT is completely free and open-source under the Apache 2.0 license. There are no paid plans, subscription fees, or token-based APIs. The toolkit eliminates commercial API dependencies, so you don't pay per token or per request when using local models. You only incur costs for your own infrastructure (servers, GPU hardware) and any external model providers you choose to connect.
What use cases is ORBIT suitable for?
ORBIT is suitable for customer support (integrating AI with organizational knowledge base), internal knowledge management (intelligent document-based Q&A systems), education (interactive learning assistants tailored to course materials), healthcare (HIPAA-compliant medical information systems), financial services (secure financial advisory assistants), legal services (confidential legal research tools), government agencies requiring sovereign AI capabilities, and any organization needing AI connected to real business data without sending everything to a SaaS vendor.
How does ORBIT handle data privacy?
ORBIT is privacy-first by design. All data processing happens on your infrastructure, ensuring sensitive information never leaves your environment. No data is sent to external services. This makes it ideal for organizations with strict privacy requirements, compliance-ready for data residency requirements and privacy regulations, and perfect for regulated data in healthcare, finance, legal, and government sectors. It includes RBAC, audit logs, and rate limits for additional security.
What models does ORBIT support?
ORBIT supports 200+ models across 20+ providers including Anthropic, OpenAI, Google, AWS, Azure, and more. It works with local models via Ollama (like gemma3:1b) and can connect to hosted models. You can switch between local and hosted models without changing your app. The OpenAI-compatible API means you can use any model that works with OpenAI's API format.
What vector databases does ORBIT support?
ORBIT currently supports ChromaDB as the primary vector database for semantic search. SQLite is also available as an option. Milvus support is coming soon. The toolkit is designed with multiple vector database options in mind and has adaptable retrieval strategies for accuracy and relevance.
What languages does ORBIT support?
ORBIT supports 12+ major languages with automatic language detection. It provides native responses in the user's language and enables seamless language switching. This multilingual support makes it suitable for international organizations and diverse user bases without requiring separate deployments for different languages.