Switch AI Models at Runtime on Telnyx Edge Compute
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 What it…
Telnyx has developed an innovative solution to allow AI model switching at runtime on its Edge Compute platform. This eliminates the need for code modifications and redeployment when experimenting with different models.
The multi-model-inference-switcher example is a TypeScript application that runs on Telnyx Edge Compute using the Agent SDK. It provides an admin UI with a model dropdown, chat panel, durable conversation history, usage stats by model, and an active-model flag stored in Telnyx KV Storage.
When a user switches the model via the UI or API, the next message generated by the model is used immediately without a redeployment. The flow involves making a GET request to the admin UI, POSTing to the /model endpoint to validate the model and write the active-model flag to KV, and then POSTing to the /chat endpoint to read the active model, process the message through Telnyx AI Inference, and return the reply along with the model name.
The example includes three models: moonshotai/Kimi-K2.6, zai-org/GLM-5.2, and meta-llama/Llama-3.3-70B-Instruct. Model choice can significantly impact latency, cost, output quality, reasoning depth, tone, reliability, and other factors. Making the active model observable and switchable without mixing that decision into application deploys helps optimize these aspects.
To use the API, you can switch the active model with a POST request to the /model endpoint, providing the desired model name. Similarly, sending a chat message involves POSTing to the /chat endpoint with the input text. The response includes the generated reply and the model used.
The SwitcherAgent utilizes several key components, including durable message history for chat context, actor state for total requests and model usage, Telnyx binding for zero-credential AI Inference, and KV Storage for the global model flag. The inference call uses the active model pulled from KV, rather than a hardcoded constant.
Before deploying this solution, additional features are recommended, such as authentication on the admin UI, audit logging for model changes, a reviewed model allowlist, fallback behavior in case of model failure, latency and cost tracking per model, and scoped flags by environment, tenant, or cohort.
This approach enables AI applications to remain deployed while providing flexibility in model selection and operation. For more information, refer to the provided code repository, Agent SDK documentation, Edge Compute documentation, AI Inference documentation, and AI skills and toolkits on GitHub.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
