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By default, calls run on Nixflex’s built-in AI stack. With your own LLM, every conversation turn is answered by a model you choose, on your own provider account - and the per-minute price drops by $0.015, because Nixflex no longer pays for the thinking. Any OpenAI-compatible endpoint works: OpenAI, Anthropic (Claude), Google (Gemini), Groq, Together, OpenRouter, or your own self-hosted server - one protocol covers them all.
The connection is tested before it is saved: Nixflex runs a real completion AND a tool-calling round trip against your endpoint. A model that cannot do OpenAI-style tool calling is refused with a clear message - bookings and custom functions depend on it - so a saved configuration is a working configuration.

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How it works

1

Connect your model

In Settings → Your own LLM, or via the API below: base URL, model name, and your provider API key.
2

Your model answers every call

Applies account-wide - every number and every agent, whether added in the dashboard or through the API. Your full agent prompt goes to your model; your provider bills you for the tokens.
3

The rate drops automatically

Calls your model actually served bill at **0.065/min∗∗insteadof0.065/min** instead of 0.08. Nothing to configure - the engine records which model served each call and prices it honestly.
The opening line stays instant. The greeting is prepared by the platform before the call connects, so callers hear an immediate hello - your model takes over from the first real conversation turn. Tool turns (bookings, custom functions) also run on the platform’s stack, so the machinery that moves real appointments never depends on an unknown model.

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If your model fails

A call is never lost to a provider problem. If your endpoint errors, times out, or returns nothing:
  • The standard Nixflex stack takes over instantly - the caller never hears dead air.
  • Your endpoint is not retried for the rest of that call (no per-turn timeout tax). The next call tries it fresh.
  • The call’s Events tab records a PROVIDER_FALLBACK event with the reason.
  • That call bills at the standard rate - the discount applies only to calls your model actually carried, however the call ends. If your model served part of the call and the platform rescued the rest, the platform did the work: standard rate.

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Requirements

Model choice is a real latency decision. A large, slow model makes every reply late and the call feels broken - that is the trade you choose. Fast, small models suit voice best.

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Setting it up via the API

Read the configuration (the key is never included):
Disconnect (calls return to the standard stack and rate from the next call):

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Common endpoints