Many startups start constructing AI merchandise with a single closed mannequin as a quick strategy to prototype. However mannequin choice for software workloads is a call that compounds throughout a product’s lifecycle and shapes future prices and product differentiation.
Open fashions allow you to make that decision extra intentionally: you decide the mannequin, tune it to your use case, and form the habits that units your product aside. With Fireworks AI on Microsoft Foundry now usually accessible, you may serve high-performance, low-latency open mannequin inference immediately in Azure. You don’t must construct your personal inference infrastructure to run open fashions right here; Fireworks serves them on Foundry, so you can begin shortly and scale that footprint as you go.
To assist, we’re introducing new sources for AI-native startups on tips on how to deploy and serve Fireworks fashions on Foundry and scale from prototype to manufacturing.
Implementation blueprint designed for AI-native startups
The implementation blueprint for deploying Fireworks AI fashions exhibits how founding engineers and small groups can transfer from concept to MVP to product-market match (PMF) utilizing a repeatable, Azure-native method. The expertise begins easy and grows along with your wants, so that you personal your intelligence from the start.
The stack runs solely inside your Azure setting and solely requires a mannequin endpoint in your software infrastructure or harness. Begin by deploying a single mannequin, routing visitors via API Administration, and monitor latency, utilization, and value metrics alongside the way in which. When prepared, you may scale by utilizing Azure Cache for Redis to scale back redundant inference, introducing efficiency tuning primarily based on workload and deploying a number of mannequin variants for A/B testing.
Fireworks fashions are deployed via Foundry inside your Azure subscription, so mannequin discovery, governance, and billing all stay inside a single management airplane.
Why startups want versatile AI inference structure
Inference is likely one of the largest controllable price drivers for AI-native firms. Early choices about how fashions are served can create long-term constraints in price, latency, and suppleness. This structure is designed to handle these challenges upfront. Serving open fashions this manner retains these choices in your fingers, so you may select, optimize, and change the fashions behind your product as your price and efficiency wants change.
Optimize price from day one
- Use serverless, pay-per-token inference via Foundry with a number of open fashions
- Match workloads to probably the most cost-effective mannequin, and keep away from being tied to at least one mannequin supplier
- Cache repeated requests with Azure Cache for Redis to scale back compute utilization
- Monitor price per million tokens as a core engineering metric
Remove infrastructure overhead
- No want to face up or handle GPU clusters
- Fireworks offers high-throughput inference, whereas Foundry offers governance, safety, and lifecycle administration
Keep flexibility as you scale
- Experiment with and change fashions via constant APIs and deployment workflows, so swapping takes much less rework
- Help customized or bring-your-own mannequin weights the place wanted
- Transfer from experimentation to manufacturing on the identical platform
- As soon as a workload is properly understood, your eval suites, immediate libraries, and graded manufacturing visitors are coaching information: groups can fine-tune and optimize a mannequin by way of Fireworks Coaching after which import to Azure by way of convey your personal weights.
Construct and take a look at AI functions with much less upfront price strain
For groups within the Microsoft for Startups program, this structure unlocks a big benefit. You may apply your Startup credit to Fireworks mannequin deployments utilizing Information Zone Normal (provisioned throughput models, or PTUs, are reserved capability and never coated by Startup credit), in addition to the supporting Azure infrastructure.
This implies you may construct and take a look at production-grade AI functions, experiment with a number of fashions to search out the place open fashions provide the proper price and efficiency benefit earlier than scale, and iterate shortly towards product-market match, with out introducing speedy infrastructure price strain.
Construct with Microsoft for Startups
Should you’re constructing AI functions on Azure, we’d like to study extra about your imaginative and prescient and assist speed up your journey.
Microsoft for Startups helps founders construct quick, scale sensible, and promote extra with Startup credit, Azure AI infrastructure, technical steerage, and go-to-market sources designed to assist startups transfer from prototype to enterprise deployment sooner. Get began with Microsoft for Startups at present.
