Serverless LLM Deployment Services
Packages & Pricing.

Serverless LLM deployment on RunPod, Modal and Lambda

Serverless LLM deployment lets you pay for GPU time only while requests run, but the first request after idle can wait a minute while the container loads. Omeiro cuts that cold start by caching model weights, snapshotting containers and keeping a small warm pool.

We deploy on RunPod or Modal and show before and after timings, so the improvement is measured.

Serverless LLM deployment: deciding if the traffic pattern fits

Serverless pays off when traffic is uneven. Serverless LLM deployment charges for GPU time only while requests run, so it suits bursts, internal tools and prototypes, while steady all-day load can cost less on a dedicated card. Before ordering, estimate requests per day, peak concurrency and the wait users will tolerate on the first request after idle. Those three numbers show whether the savings are real and how much warm capacity you may need.

Cold starts have several ingredients: starting the container, loading weights into GPU memory and warming the runtime. Serverless LLM deployment improves them by caching weights close to the GPU and snapshotting what can be restored quickly, but a warm pool costs money while idle. Set a target for acceptable first-response time, then balance it against cost. Tell us your model size and provider preference, and watch provider limits such as concurrency caps and regional GPU availability.