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Lambda Face Recognition APIPlatform.sh is especially recommended for medium to large enterprises, digital agencies, and development teams looking to streamline their deployment processes and infrastructure management. It's also suitable for projects that require multi-cloud support and high levels of customization and scalability.
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Platform.sh might be a bit more popular than Lambda Face Recognition API. We know about 36 links to it since March 2021 and only 27 links to Lambda Face Recognition API. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I have been participated in other Soroban challenges such as Sorobanathon and Sorobounties Spectacular, but this challenge has been different. I have improved my react skills and I've learned how to integrate Backend and Frontend using the Symfony UX initiative. Besides, I've also learned how to deploy an application to the Platform.sh cloud. I specially would like to mention that I have learned that web2 and... - Source: dev.to / about 2 years ago
You wrote get a 20G medium hosting package from platform.sh, did you mean 2G? Source: over 2 years ago
I personally *like* platform.sh better as a development tool but it's hard to make a strong objective case one way or the other. Source: about 3 years ago
Platform.sh | REMOTE | Full-Time | https://platform.sh Platform.sh is a unified, secure, enterprise-grade platform for building, running and scaling fleets of websites and applications. We serve thousands of customers worldwide including The United Nations, The Financial Times, Adobe Commerce/Magento, Shopware, Orange, University of Missouri and The British Council. We are among the momentum leaders in Cloud PaaS... - Source: Hacker News / over 3 years ago
With all that said... There will ALWAYS be edge case scenarios where Pantheon DOESN'T work, But I'm sure that would happen with Acquia also. Or platform.sh... Source: over 3 years ago
Setup time matters too. The delta between Runpod and bare-metal providers like Lambda Labs is large. Reaching an equivalent setup on a bare VM requires provisioning the instance, configuring the OS and CUDA drivers, installing Docker, setting up your orchestration layer (Kubernetes or Slurm), deploying your inference container, configuring autoscaling rules, and wiring up your load balancer. Thatโs a realistic... - Source: dev.to / 5 months ago
Let's do the math for a representative setup: GPT-OSS-120B via Together.ai ($0.15/$0.60) vs self-hosting on H100s from Lambda Labs at $2.99/hr ($2,183/mo). A single H100 running a 70B model produces roughly 50 tokens/second on average, which works out to about 130M tokens per month. - Source: dev.to / 6 months ago
How does this compare to https://lambdalabs.com/. - Source: Hacker News / about 3 years ago
Another option is to pay for AWS server with a beefy GPU and enough RAM. It's not too cheap, but isn't expensive either if you aren't planning to run it 24/7. Or get a GPU cluster from a company that offers stuff for ML specifically, it might be easier to set up compared to AWS and in some cases cheaper. Like, for example, lambdalabs that offers H100 gpu for 2 bucks per hour. Source: about 3 years ago
I used some of the cloud GPUs on Vast.ai, but I also tried Lambda Labs, and these days I have my own docker container setup which can be deployed to a VM on Google Cloud and used more programatically. Source: over 3 years ago
Pantheon - The professional website platform for Drupal & WordPress sites.
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Vast.ai - GPU Sharing Economy: One simple interface to find the best cloud GPU rentals.
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