Serverless Infrastructure for AI Applications. No VMs, No DevOps. 3x Faster than Baseten, Cerebrium & Lightning AI at a fraction of the cost.
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Maker
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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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How does billing work for serverless AI usage? Is it pay per inference or subscription based?
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Maker
@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
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Is there a free tier or trial available for testing out deployments?
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Maker
@sadie_scott Yes, there is a free trial. We currently give first 10 hours free for new users, and more credits if you are a company. Let me know if you would like a personalised demo.
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@hosea_ng Are there any ready made integrations available for popular ML frameworks like PyTorch, TensorFlow or Hugging Face?
I love how simple it is. Is there a free trial available to test out the performance?
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Maker
@jacob_hernandez4 Yes. First 10 hours free for new users. Feel free to try it out!
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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?
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Maker
@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.
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I love the concept. Does it work with all the major AI frameworks?
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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.
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
Is there a free tier or trial available for testing out deployments?
@sadie_scott Yes, there is a free trial. We currently give first 10 hours free for new users, and more credits if you are a company. Let me know if you would like a personalised demo.
@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
I love how simple it is. Is there a free trial available to test out the performance?
@jacob_hernandez4 Yes. First 10 hours free for new users. Feel free to try it out!
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.
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
Such an innovative solution. How do you manage monitoring and logging without using VMs?
@jude_gray we provide a dashboard for users to monitor performance. Feel free to try it out for yourself!
Is there built in support for versioning and rolling back AI models? Great potential!
@matteo_rider Right now, not yet. But it is on our roadmap
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!