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Know when your data is enough.

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  9. 9

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  10. 10

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  11. 11

    A dedicated cloud machine for bots and software

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  12. 12

    Deploy AI/ML/data apps for free.

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  13. 13

    The easiest way to use cloud GPUs

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  14. 14

    Develop failure-proof applications

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  15. 15
    Taipy 3.0304

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  16. 16PC

    Hi HN! Porter Cloud (https://porter.run/porter-cloud) is a Platform as a Service (PaaS) like Heroku, but we make it easy for you to migrate to AWS, Azure, or GCP when you're ready. Like Heroku, Porter takes care of a lot of generic DevOps work for you (like setting up CI/CD, containerizing your applications, autoscaling, SSL certificates, setting up a reverse proxy) and lets you deploy your apps with a few clicks — saving you a lot of time while developing. However, as you probably know, there’s a downside: platforms like this become constraining if and when your app…

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  17. 17
    Pi164

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  19. 19
    Pulumi74

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    Seeknal57

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  22. 22TC

    Hello HN! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! https://www.tensordock.com/product-core Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to…

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  23. 23

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  24. 24

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