Fireworks AI on Microsoft Foundry is now generally available, enabling startups to serve high-performance, low-latency open model inference directly in Azure without building their own inference infrastructure. New resources for AI-native startups detail how to deploy and serve Fireworks models on Foundry and scale from prototype to production. The implementation blueprint shows founding engineers and small teams how to move from idea to MVP to product-market fit using a repeatable, Azure-native approach. The stack runs entirely inside your Azure environment and only requires a model endpoint for your application infrastructure. Start by deploying a single model, routing traffic through API Management, and tracking latency, usage, and cost metrics. Scaling involves using Azure Cache for Redis to reduce redundant inference, performance tuning, and deploying multiple model variants for A/B testing. Fireworks models are deployed through Foundry within your Azure subscription, keeping model discovery, governance, and billing within a single control plane. Components include Microsoft Foundry with Fireworks AI models, Azure Container Apps, Azure Container Registry, Azure Key Vault, and optional Azure Monitor. Inference is one of the largest controllable cost drivers for AI-native companies, and this architecture addresses cost, latency, and flexibility challenges upfront. Use serverless, pay-per-token inference through Foundry with a selection of open models to optimize cost from day one.