Model Status
Why a Segmind model responds slowly on its first call: how cold boots and warm boots work, and what each means for request latency.
Model status can be cold and warm refer to how long it takes to launch a machine learning model to accept requests.
- Cold Boot: When a model hasn't been used in a while, it gets turned off to conserve resources. This is similar to completely turning off your computer. When you make a request to use the model again, it needs to be fully loaded and started up, which can take several minutes for large models. This is a cold boot.
- Warm Boot: If a model has been used recently, it stays loaded and ready to accept requests. This is similar to putting your computer in sleep mode. When you use a warm model, the response is much faster because the model is already up and running.
Here's why this happens:
- Segmind has a large library of models, and keeping them all running all the time would use a lot of resources.
- Only the models actually being used are kept running.
- Cold boots happen more often for less frequently used models.
Segmind only charges you for the time the model is actually processing your request, not the boot time. So, cold boots don't affect your costs.
Seeing it in your own traffic
Cold boots are visible in API Health: a p50 in seconds beside a p95 in minutes is the usual signature — most calls hitting a warm model, a few paying for a boot. The By model table there breaks latency down per model, so you can tell an occasional cold start apart from a model that is consistently slow.
Async Inference (V2)
Submit inference requests asynchronously and poll for results. Ideal for long-running models like video generation, image upscaling, and LLMs.
Webhooks
Receive real-time HTTP POST callbacks from Segmind when a PixelFlow run changes state, so you can react to node and graph events without polling.