Hyperpod
AI models to apps, fast
51 followers
AI models to apps, fast
51 followers
Serverless Infrastructure for AI Applications. No VMs, No DevOps. 3x Faster than Baseten, Cerebrium & Lightning AI at a fraction of the cost.
51 followers
51 followers
Playing around with new AI models is fun, but turning them into consumer apps? A nightmare. You waste hours setting up and debugging IAM roles, VMs and networking. You waste weeks after that trying to scale it or optimize costs. It kills momentum before ideas ever see the light of day.
What is it?
Hyperpod AI is a serverless inference platform that turns your AI models (custom or open source) into production-ready apps in minutes. No infra, no DevOps, no guessing game with cloud bills. Just drop in your model, and we handle auto-scaling, latency optimization, and cost efficiency. We are 3x faster than baseten, cerebrium and lightning AI at a fraction of the cost.
Why now?
There are new AI models released every 3 months, but infra hasn’t caught up. Startups and engineers still fight with deployment overhead when they should be shipping products. Hyperpod lets you skip the plumbing and focus on building.
How we keep your costs low
• Fewer wasted calculations — our compiler converts dynamic ML ops into static ones, unrolls loops, and reduces redundant operations so your model runs leaner without losing accuracy.
• Right hardware, every time — our algorithm benchmarks your model across different hardware options GPUs/CPUs (or a mix) to pick the best price-to-performance fit for your specific model.
How it helps you win
• Get a live endpoint in minutes
• Auto-scales to handle spikes without draining your wallet
• Benchmarked 3x faster and ~1/5th the cost of existing platforms
• Speed up experimentation and MVPs, while being robust for production workloads
How it works (in practice)
• Upload Your Model
• Select the combination of price and speed you prefer
• Connect to your app using HTTP
Would love your thoughts, requests, or sharp feedback. Ship your AI models live today at hyperpodai.com.
@hosea_ng Are there any ready made integrations available for popular ML frameworks like PyTorch, TensorFlow or Hugging Face?
@abigail_martinez1 Yes, there are quick integrations to all of the above frameworks you mentioned it's all in our documentation here: https://docs.hyperpodai.com/category/exporting-models-to-onnx
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Serverless AI infrastructure is just what the market needs. How does your system manage large scale AI model deployments compared to traditional cloud setups?
@gracebates Our system handles automatic scaling for variable workloads. The algorithm is also able to analyze usage over time and adapt its own scaling policies. All of this is activated for our users by default.
Speed claims are impressive. How do you manage to deliver performance that's three times faster than your competitors?
@nathaniel_cook2 our compiler optimizes the model for significant cost savings, and our algorithm is able to adapt hardware choices and orchestration policies to optimise costs automatically.
Three times faster is incredible but does that benchmark apply to really large models like GPT sized architectures or is it mostly for smaller deployments?
@grayson_parker2 We tested on a couple of models ranging from smaller models to larger models. Smaller models tend to experience gains way higher than 3x but diminishes slightly for larger models. If there's a specific model you have in mind I could check it for you.
This looks like it could save developers a ton of time. Can it handle models that are GPU-intensive?
@charlotte_richardson1 Yes. It's tested on a ton of different models. We have GPU or even ARM CPUs supported. It all depends on your model!
Hyperpod AI lets you deploy your AI model as an API in minutes, without the need for VMs or DevOps. Upload your model (ONNX, PyTorch, TensorFlow), drag and drop, and you're done: automatic scaling, transparent pricing, and hassle-free.