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Hyperpod
AI models to apps, fast
42 followers
AI models to apps, fast
42 followers
Serverless Infrastructure for AI Applications. No VMs, No DevOps. 3x Faster than Baseten, Cerebrium & Lightning AI at a fraction of the cost.
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.
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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.
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.
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.
How does billing work for serverless AI usage? Is it pay per inference or subscription based?
@michael_davies5 it's pay subscription based. you can do cost estimations on the app itself before you even pay a single dollar. Let me know if you would like us to do a personalised demo for you
Going serverless for AI is amazing. How well does it scale for enterprise workloads?
@nicholas_anderson0 it is also built to scale. We have currently have users with ten of thousands of users at any one point of time with no issues! Would love to give you a demo if you are interested
I love the concept. Does it work with all the major AI frameworks?
@ayesha_akram4 Yes. Pytorch, ONNX, Tensorflow, Hugging face all work! We have tons of guides using them here: https://docs.hyperpodai.com/category/quickstart-guides