Switch AI Models at Runtime on Telnyx Edge Compute Telnyx has released a new example application, multi-model-inference-switcher, that allows developers to switch AI models at runtime on its Edge Compute platform without redeploying code. The TypeScript app uses Telnyx KV Storage to store the active model, which is read on each chat request, enabling instant model changes via UI or API. The example supports models like moonshotai/Kimi-K2.6, zai-org/GLM-5.2, and meta-llama/Llama-3.3-70B-Instruct. Most AI examples hardcode the model name. That is fine until you actually want to compare models. If every model change requires a code edit and redeploy, experimenting gets annoying fast. The multi-model-inference-switcher example turns model choice into runtime configuration instead. Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/multi-model-inference-switcher https://github.com/team-telnyx/telnyx-code-examples/tree/main/multi-model-inference-switcher This is a TypeScript app running on Telnyx Edge Compute with the Agent SDK. It gives you: active-model flagThe active model is read from Telnyx KV Storage every time /chat is called. When you switch the model from the UI or API, the next message uses the new model immediately. No redeploy. php GET / - admin UI POST /model - validate model - write active-model to KV POST /chat - read active-model from KV - SwitcherAgent.process text, model - Telnyx AI Inference - return reply + model The sample includes these models: moonshotai/Kimi-K2.6 zai-org/GLM-5.2 meta-llama/Llama-3.3-70B-Instruct Model choice is product behavior. Changing the model can affect: So it helps to make the active model observable and switchable without mixing that decision into application deploys. Switch the active model: curl -X POST https://multi-model-inference-switcher-