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Conifer

Run supported models locally and route other requests through a multi-provider gateway with Conifer.

Pricing
Runtime and local inference free; cloud tokens billed at model rates with no Conifer markup; provider charges apply for your own keys
Free plan
Yes; runtime,router and local inference
Platforms
macOS desktop; CLI on macOS and Windows; Linux thin client; cross-platform cloud API

Tool Information

Conifer
Conifer Solutions, Inc.
Updated: September 2026
Tool type: Local And Cloud AI Inference Router
Pricing: Runtime and local inference free; cloud tokens billed at model rates with no Conifer markup; provider charges apply for your own keys
Free plan: Yes; runtime,router and local inference
Platforms: macOS desktop; CLI on macOS and Windows; Linux thin client; cross-platform cloud API
Login required: Not required for local inference; account/key required for Conifer cloud gateway
API: Yes; compatible cloud gateway and local serving endpoint
Browser extension: No official browser extension verified
Mobile app: No official native mobile app verified
AI models: 200+ cloud models advertised; Qwen, DeepSeek, GLM, Kimi, Llama and other supported families; local architecture support varies
Developer: Conifer Solutions, Inc.

About Conifer

Conifer connects local model execution with a cloud inference gateway. Developers can run supported weights on their own hardware or send requests through a shared interface to hosted models. Routing can choose where a request runs, while an explicit model choice gives the caller more direct control over the result and provider path.

One interface for different compute

The gateway works with existing OpenAI and Anthropic clients when configured with the appropriate endpoint and credentials. Conifer also provides a command-line runtime and a macOS desktop application. The official platform details distinguish full local engine support on Apple Silicon and Windows from the Linux thin client, which connects to the gateway or another Conifer host. Windows and Linux desktop applications are not presented as currently available.

The model catalogue includes frontier providers and open-weight families. Local execution depends on the engine’s support and available hardware, so a catalogue entry should not be taken as proof that every model can run on a particular computer. The CLI manages model installation and local serving, giving other tools an endpoint for accessing the runtime.

Routing and privacy boundaries

Conifer describes query-based routing that considers the needs of each request. Users can also bring their own provider keys or connect compatible endpoints. A local request and a cloud request have different privacy properties: local inference keeps its prompt and response on the machine, while cloud execution sends the required context to the chosen endpoint. Pinning a route to local is therefore different from merely allowing local execution as one option.

The privacy documentation separates usage telemetry from optional content sharing. Teams should review those controls before processing sensitive code or documents. A routing policy should be tested with the actual client, model and credentials used in production.

Costs and evaluation

The runtime, router and local inference are advertised as free. Cloud usage is prepaid and charged at the model’s rate without a Conifer markup; bringing provider keys does not remove the provider’s own charges. Start with a small known task, inspect the selected route and compare quality, latency and cost before enabling automatic routing for broader workloads.

Key features
  • Run supported models on local hardware.
  • Access cloud models through one gateway.
  • Route requests by task needs.
  • Bring your own provider credentials.
  • Serve a local API endpoint.
  • Inspect routing and spending information.
Use cases
Local AI inference,Multi-model applications,Coding assistant routing,Provider failover experiments,Inference cost management
How to use
  1. Review the platform support for your machine.
  2. Install the official runtime or macOS app.
  3. Choose a supported local model or cloud provider.
  4. Configure credentials only for intended cloud access.
  5. Select an explicit model or routing policy.
  6. Run a small known prompt.
  7. Inspect the response and route information.
  8. Adjust privacy and spending controls before wider use.
Best for
AI Developers, Local Model Users, Engineering Teams
Integrations
OpenAI-compatible and Anthropic clients,Claude Desktop,Codex Desktop,Cursor,VS Code,OpenCode
Commercial use
Commercial inference subject to service terms and the licences of selected models and providers

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