inference-gateway vs cli
LLM provider gateway and SandBase agent bridge compared
inference-gateway provides a Go-based, OpenAI-compatible API for routing requests to cloud and self-hosted LLM providers. cli is a TypeScript command-line tool and local MCP bridge that connects supported AI clients to SandBase models and APIs; the main difference is a provider-neutral routing layer versus a client setup and execution path for one platform.
inference-gateway: Unify LLM Providers Behind One API
A Go-based, cloud-native gateway that puts multiple LLM providers behind one OpenAI-compatible API. It supports cloud and self-hosted models, with integrations for MCP, A2A, guardrails and OpenTelemetry.
cli: Connect AI Agents to SandBase Models and APIs
SandBase CLI uses the Model Context Protocol (MCP) to connect 25 AI clients to SandBase’s catalog of models and APIs through a local bridge. It provides OAuth setup, discovery, inspection, execution, and configuration rollback.
| inference-gateway | cli | |
|---|---|---|
| Language | Go | TypeScript |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 215 | 185 |
| Forks | 48 | 17 |
| Last analyzed | Oct 23, 2026 | Nov 3, 2026 |
Key differences
- inference-gateway routes model requests to providers such as OpenAI, Anthropic, Ollama and DeepSeek; cli connects agents to SandBase’s catalog of models and APIs.
- inference-gateway offers an OpenAI-compatible API for applications; cli configures supported AI clients and exposes catalog discovery and execution through MCP and shell commands.
- inference-gateway is written in Go and can run as a container or standalone binary, with Kubernetes deployment support; cli is written in TypeScript and requires Node.js 20 or later.
- inference-gateway includes optional authentication, guardrails, telemetry, MCP integration and A2A delegation; cli focuses on OAuth setup, endpoint inspection, execution, run management and configuration rollback.
- Both projects use the Apache-2.0 license. inference-gateway has 215 stars and 48 forks, while cli has 185 stars and 17 forks.
- Both are actively changing; cli’s development is concentrated among two contributors, with most code from one, while inference-gateway’s work is not classified as concentrated in one person.
Choose inference-gateway if you…
- need one OpenAI-compatible API to route requests across cloud and self-hosted LLM providers.
- want to deploy a gateway as a container or standalone binary, including in Kubernetes.
- need optional authentication, policy checks, telemetry, MCP tools or A2A delegation in a shared routing layer.
Choose cli if you…
- want to connect supported AI clients to SandBase’s models and APIs through a local MCP bridge.
- need to inspect endpoint schemas and prices before execution, or manage synchronous and asynchronous runs.
- prefer guided OAuth setup and CLI or shell workflows for discovery, execution and configuration management.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.