Tandemn Tuna Skillv0.0.1

A startup wants to deploy a custom LLM for a chatbot MVP without managing infrastructure. They use Tuna to quickly launch on Modal with serverless-only mode, enabling fast iteration and pay-per-second billing while keeping costs low during early testing.

A large company runs high-volume LLM inference for internal tools and needs to reduce costs. They deploy with Tuna's hybrid spot fallback on AWS, shifting traffic to cheaper spot instances after serverless cold starts, achieving 3-5x savings without downtime.

A university research team needs to serve multiple open-source models like Llama and Mistral for experiments. They use Tuna to deploy on RunPod with tensor parallelism for large models, leveraging GPU price comparisons to select the most cost-effective provider.

An e-commerce platform integrates an LLM for automated customer support queries. They deploy on Azure Container Apps using Tuna, ensuring high availability with public endpoints and scaling policies to handle traffic spikes during sales events.

A cloud consultancy uses Tuna to deploy and manage LLM endpoints for clients, reselling GPU resources from providers like Modal or Azure with a markup. They offer hybrid spot fallback to reduce client costs and provide maintenance services.

A developer builds a niche LLM application and uses Tuna to host it on Baseten or Cloud Run, exposing an OpenAI-compatible endpoint. They charge per API call or offer tiered subscriptions, leveraging Tuna's serverless scaling to handle variable demand.

A company creates a platform that simplifies LLM deployment for non-technical users, using Tuna under the hood for multi-provider orchestration. They offer a dashboard for cost tracking and automated deployments, charging a platform fee on top of infrastructure costs.