FlexAI News
New capabilities eliminate infrastructure bottlenecks while supporting any hardware and any cloud.
When we launched FlexAI in 2023, we had a simple mission: let AI startups focus on building models, not managing infrastructure. Today, we're taking a major step forward in that mission.
We're announcing significant new capabilities that make FlexAI the first truly hardware-agnostic AI compute platform. It’s designed specifically for founders who need to move fast without burning through resources.
Every AI founder we talk to faces the same challenge: the minute you move beyond calling an LLM API and start fine-tuning, serving low-latency endpoints, or building production pipelines, your best engineers become infrastructure firefighters instead of product builders.
Demand for AI compute continues to grow but supply has remained restricted. The supply versus demand ratio has hit 10 to 1, and while other hardware options existed, they sat unused due to software complexity.
That's the exact problem FlexAI was built to solve.
The future of AI infrastructure isn't about picking the "right" GPU vendor. It's about using the right accelerator for each specific workload and making that choice invisible to your engineering team.
Demand for AI compute continues to grow but supply has remained restricted. Supply has remained severely constrained: industry analysts reported 'huge supply shortages' in 2023, with one-year lead times becoming standard and alternative hardware options sitting unused due to software complexity.
Today, FlexAI is announcing three new capabilities to help AI-Native companies iterate faster.
If you're building an AI startup, our new features allow you to:
Your iteration loop is your lifeline. If infrastructure slows you down, you lose your edge.
Being locked into a single cloud or hardware provider doesn't just limit your choices—it actively slows your ability to innovate and ship product.
The reality for AI startups today: hyperscaler credits expire, pricing changes without warning, outages and region quotas cause work stoppages, and the GPUs you need aren't available where you need them. Want to shift to another provider? Expect countless hours on quota calls, custom refactoring for each platform, and zero visibility across your infrastructure.
FlexAI eliminates vendor lock-in by abstracting both cloud and hardware differences into a single unified platform.
Multi-Cloud Deployment: Run workloads on FlexAI Cloud, hyperscalers (AWS, Google Cloud, Azure), neo-clouds, or on-premise using the same code and workflows. Use your existing cloud credits or switch providers without refactoring.
Multi-Compute Hardware: Seamlessly orchestrate across NVIDIA (Blackwell, Hopper, Ampere), AMD (MI Instinct), and emerging accelerators (Tenstorrent Loudbox planned soon). Choose the right accelerator for each workload: NVIDIA for training with CUDA dependencies, AMD for 50% cheaper inference, emerging silicon for specialized tasks.
This isn't theoretical. Our existing SaaS (self-service at https://console.flex.ai) on FlexAI Cloud Service instances can seamlessly serve multiple models on both NVIDIA and AMD compute. Instead of being locked into one vendor's ecosystem and roadmap, teams can now choose the right compute for each specific workload while maintaining consistent performance.
Unified Control Plane: One API, one dashboard, one view of performance, cost, and utilization across all clouds and hardware. Seamlessly migrate workloads for latency, cost, or capacity without code changes.
Speed: When your preferred GPU isn't available in a region, you don't stop—you move to available capacity. No quota calls, no manual migrations, no downtime.
Cost: Optimize each workload independently. Training on the hardware it needs, inference on the most cost-effective option, without rebuilding your stack.
Control: Maintain data sovereignty and compliance across environments. Enforce privacy and model control uniformly, whether you're on FlexAI Cloud, a hyperscaler, or on-premise.
Innovation: Your engineers focus on building product, not debugging driver mismatches or managing infrastructure across providers.
When developers scale LLM workloads to production, there's one question that always comes up: Which GPUs should I use, how many will I need, and how much is this going to cost me?
This shouldn’t be a back-of-the-envelope guess but real numbers that reflect the latency you need, the model you're running, and the traffic you expect.
Using the Workload Co-Pilot, you choose a model, specify the requirements for input/output token sizes and your projected requests per second. We provide a variety of recommendations on deployment-ready configurations optimized for cost, latency, TTFT, and bandwidth.
What makes our Workload Co-Pilot different:
Example: Building a chatbot with LLaMA 3.1 8B? The Sizer recommends 1× L40 for 10 RPS at ~4 sec E2E latency. Need sub-800ms response? It shows you H200 will get you to 679ms versus H100's 934ms. And, it also shows you the cost premium for you to decide if you´re ready to accept the cost/performance trade-off
Why it matters: Most calculators stop at "does it fit in memory?" FlexAI goes from sizing to live endpoint in one flow—no sales calls, no guesswork.
Try the Workload Co-Pilot for inference sizing now
You shouldn’t need every team to rebuild the wheel. We've created pre-configured solutions for the most common AI workloads:
Why it matters: These blueprints compress weeks of infrastructure work into hours of configuration.
We're not launching capabilities in a vacuum. These features come from hard-won experience:
To celebrate this launch we’re offering €100 starter credits for first-time users!
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