2026.09.14
ORBIT: A Self-Hosted Gateway for Private Data, Models, and Tools
ORBIT is a self-hosted AI gateway for teams that want one OpenAI-compatible API across private data sources, model providers, and MCP tools. This overview covers its capabilities, audience, and deployment considerations.
What it is for
ORBIT is designed for teams building applications around private retrieval-augmented generation, natural-language data access, and tool-using agents. It sits between applications, data sources, tools, and model providers, exposing an OpenAI-compatible API. That architecture may help teams avoid implementing a separate integration for every model or backend.
The README presents ORBIT as deployable on premises, in a private cloud, or in an air-gapped environment. It also lists local, self-hosted, and hosted model choices. Therefore, deploying ORBIT inside your infrastructure does not by itself mean every request stays local: the actual boundary depends on the configured provider and adapters.
Known capabilities
Adapters are configured with YAML and can connect files, SQL and NoSQL databases, vector stores, Elasticsearch, REST/GraphQL APIs, and MCP tools. The README describes multi-turn questions over uploaded PDFs, documents, and images. It also presents voice-enabled assistants and natural-language exploration of Elasticsearch logs. These examples make ORBIT more relevant to data-connected internal assistants than to a basic chat front end.
The project lists Ollama, llama.cpp, vLLM, OpenAI, Anthropic, Gemini, Bedrock, Microsoft Foundry, and OpenRouter among its model providers. Listed governance and operations features include RBAC, OIDC/SSO, identity allowlisting, per-key quotas, audit logs, moderation, file encryption, provider fallbacks, retries, circuit breakers, health checks, metrics, and an admin panel. These are project-documented capabilities, not independent assurances; they should be verified against the intended configuration and operating process.
Who may benefit
ORBIT may suit application teams that want one model contract, platform teams connecting enterprise data to assistants, and organizations that need control over deployment location. Teams with existing databases, search systems, or MCP tools can investigate its adapters and intent templates for SQL, GraphQL, MongoDB, Elasticsearch, and HTTP workflows.
For an individual who only wants a hosted chat service, the configuration and operational surface may be unnecessary. ORBIT’s value is primarily in data connectivity, access control, model routing, and administration rather than in being a standalone chat model.
Limits and deployment considerations
The README requires Python 3.11 or newer, with Python 3.12 preferred. The default inference configuration uses Ollama, so users choosing that setup must install and run Ollama separately. The supplied sources do not confirm minimum CPU, memory, GPU, or model-performance requirements, so they should not be used as a hardware sizing plan. Official release v2.17.9 was published on September 13, 2026. Its notes include a fix for valid file-query requests being rejected with HTTP 422, along with updates addressing several deprecated Python APIs.
Before deployment, define which data may reach external model providers and how provider credentials, initial administrator access, API keys, and audit records will be managed. Each adapter should be reviewed for data scope and permissions. Although the project documents several security and governance controls, the supplied sources do not establish an independent security audit or guarantee. Production adoption should therefore include permission testing, network isolation, log retention, backups, upgrade procedures, and recovery exercises.
ORBIT is licensed under Apache License 2.0. Because behavior depends on the selected version, models, providers, and adapters, start with non-sensitive data and validate the complete workflow before considering production use.
Sources
Sources checked: 2026-09-14