As an AI startup founder, you know that scaling is not just about growth, it’s how you grow efficiently. AI models demand high-performance GPUs but the cost of cloud computing can quickly go out of control. Traditional hyperscalers come with premium pricing, unpredictable billing and rigid contracts, making it difficult to scale without financial risk.
You need a cloud solution that prioritises performance and cost-efficiency- one that allows you to scale AI workloads without being burdened by unnecessary costs. In this blog, we’ll explore the common challenges AI startups face when scaling, why traditional cloud providers fall short, and how to optimise cloud costs without compromising performance.
Scaling AI Without Overspending is a Challenge
Scaling an AI startup requires a decent balance between cost, performance and flexibility. While traditional cloud providers offer the infrastructure you need, they often have hidden inefficiencies that make scaling expensive and unpredictable:
- High GPU Costs: AI models require powerful hardware but the price of accessing premium GPUs on hyperscalers can be prohibitive. Running large-scale inference or training jobs often results in unexpectedly high bills, reducing your ability to scale as a startup.
- Rigid Pricing Models: Most hyperscalers lock you into long-term pricing structures or require high upfront commitments to access discounts. This may force you to over-provision resources, paying for capacity you don’t always use.
- Lack of Cost Transparency: Billing on hyperscalers can be difficult to track, with unpredictable costs due to data transfer fees, idle resource charges and complex pricing tiers. This makes financial planning and forecasting a major challenge.
- Infrastructure Limitations: You need a cloud GPUaas platform that scales as fast as your AI innovation does. However, traditional cloud platforms may introduce networking bottlenecks, slow storage speeds or inefficient workload allocation, which may limit your performance.
If these challenges sound familiar, you’re not alone. Many AI startups struggle to scale efficiently under these constraints. Fortunately, there’s a better way.
How to Optimise Cloud Costs Without Compromising Performance
We understand the challenges AI startups face. That’s why we’ve built a cost-effective, high-performance cloud platform designed to handle scaling AI workloads. Here’s how Hyperstack helps you scale and optimise cloud costs:
Flexible Pricing Models That Work for Startups
Unlike hyperscalers, we don’t force you into rigid pricing models. We offer instant access to powerful cloud GPUs for AI at a lower cost than legacy providers. Our infrastructure is designed to maximise efficiency without passing unnecessary overhead costs onto you.
We offer the following pricing models, so you can choose either that suits your needs:
- On-Demand Pricing: Pay only for what you use, with per-minute billing that gives you cost control. This ensures you’re never overcommitted. For example, you can access moderate GPUs for AI such as the NVIDIA RTX A6000 for just $0.50 per hour.
- Contract Pricing: If you have predictable AI workloads, you can lock in long-term discounts, reducing your hourly rates and guaranteeing resource availability. For example, you can reserve the cutting-edge NVIDIA H100 SXM GPU for $1.90 per hour.
Transparent Billing and Cost Management
With clear insights into your cloud spend, you can make better financial decisions without fear of hidden costs. This is why we offer:
- Detailed usage reports that give you real-time insights into GPU consumption and spending.
- Historical data access so you can track trends and make data-driven scaling decisions.
- Organisation Billing for startups with multiple users, allowing you to manage costs across teams with Role-Based Access Control (RBAC) and shared resource tracking.
- Not all of your AI workloads need to run 24/7. With our Hibernation option, you can pause workloads when they’re idle, cutting operational costs without sacrificing progress.
High-Performance AI Without the Complexity
While optimising cloud costs is important, you cannot afford to sacrifice performance. AI workloads require fast, reliable and optimised infrastructure to run efficiently. We ensure you get:
Private Flavours for AI-Optimised Performance
Every AI workload has unique requirements and a one-size-fits-all infrastructure can lead to wasted resources and suboptimal performance. This is why we also offer private flavours that provide pre-configured, AI-optimised environments, ensuring your models run on hardware specifically tailored to your needs.
This means you get:
- Faster model deployments with pre-tuned environments eliminate setup time.
- Optimised resource allocation ensures you use only what’s necessary, reducing waste.
- Lower costs by running workloads on hardware suited to your AI models, you avoid paying for over-provisioned or inefficient setups.
Please note that this feature is only available to our contracted customers.
Enterprise-Grade Performance at Startup Costs
If you are scaling AI, you know you need powerful compute. But does it stop there? To scale AI workloads, faster networking and high-speed data access are equally important. Our real cloud environment is built to eliminate bottlenecks that slow down AI model training and inference:
- Low-Latency Networking: AI workloads require rapid data transfer, whether for training deep learning models or running real-time inference. Our high-speed networking of up to 350 Gbps significantly reduces latency, ensuring your AI models train faster and inference runs without delays.
- NVMe Storage: Traditional cloud storage can slow down data processing, leading to inefficiencies. Our NVMe-based storage delivers ultra-fast read/write speeds, ensuring that large datasets load instantly and AI models access the data they need without lag.
- AI-Optimised GPUs: If you are training large language models or deploying AI-powered applications, you need GPUs designed for maximum throughput and efficiency. We provide access to cutting-edge GPUs, including NVIDIA A100 PCIe and NVIDIA H100 PCIe with NVLink, allowing you to scale performance.
Seamless AI Deployment with DevOps Tools
You've secured the necessary compute power but how do you manage complexity to ensure workloads stay efficient as they scale? Many AI startups face challenges, including:
- Slow, manual infrastructure provisioning, delaying time to market.
- Lack of automation leads to inefficient resource allocation and higher costs.
- Challenges in managing large-scale AI workloads, especially for inference and continuous training.
To help you overcome the above challenges, we provide DevOps-ready tools to help you scale AI deployments while optimising cloud costs:
- Terraform Provider: Automate infrastructure provisioning with Infrastructure as Code (IaC), ensuring deployments are fast, consistent and easily repeatable as you scale.
- SDKs for Python and Go: Manage and monitor workloads programmatically, reducing manual intervention and improving operational efficiency.
- LLM Inference Toolkit: Deploy and run large language models effortlessly, ensuring seamless inference at scale. Check how to get started with our LLM Inference Toolkit here.
Conclusion
As an AI startup, your success depends on your ability to scale efficiently while keeping costs under control. Traditional cloud providers may offer the infrastructure you need, but they come with high costs, rigid pricing and hidden inefficiencies that can limit your growth. However, with Hyperstack’s AI-optimised infrastructure, startups can focus on building the future of AI without the cost barriers of traditional cloud platforms.
Optimise AI Costs Without Sacrificing Performance With Hyperstack
FAQs
How can I reduce cloud GPU costs while scaling AI workloads?
You can lower costs by using flexible pricing models like on-demand and contract pricing, tracking expenses with transparent billing, and leveraging features like workload hibernation to avoid idle resource charges.
What GPUs does Hyperstack offer for AI startups?
Hyperstack provides high-performance GPUs like NVIDIA RTX A6000, NVIDIA A100 PCIe, NVIDIA H100 PCIe with NVLink and NVIDIA H100 SXM, ensuring scalable AI training and inference at lower costs than traditional hyperscalers.
How does Hyperstack ensure cost transparency?
With real-time usage reports, historical spending insights, and organisation-wide billing, Hyperstack allows AI startups to manage costs effectively without hidden fees or unpredictable charges.
Can I optimise performance without overpaying for cloud resources?
Yes, Hyperstack offers private flavours with AI-optimised configurations, ensuring your workloads run on tailored hardware without over-provisioning or wasted resources.
How does Hyperstack improve AI model training and inference speed?
Hyperstack provides low-latency networking (up to 350 Gbps), ultra-fast NVMe storage, and AI-optimised GPUs to accelerate AI workloads without bottlenecks.
What DevOps tools are available for managing AI workloads on Hyperstack?
Hyperstack supports Terraform for infrastructure automation, SDKs for Python and Go for workload management, and an LLM Inference Toolkit for seamless AI deployment.