
Helm.sh
Kubernetes
Rancher
Docker Compose
Google App Engine
Amazon S3
Kustomize
AWS Elastic Beanstalk
Lambda Face Recognition API
Mattermost
Vast.ai
ipinfo.io
Grafana
Platform.sh
PostHog
Causal App
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Based on our record, Helm.sh should be more popular than Lambda Face Recognition API. It has been mentiond 181 times since March 2021. 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 know there's no such thing as a unique name anymore, but https://helm.sh/ is rather popular. - Source: Hacker News / 4 months ago
Self-managed BYOC is the highest-control option. The vendor distributes their software as binaries, container images, Helm charts, or Terraform modules, and the customer's platform engineering team handles the full operational lifecycle. This model is common among organisations with strict air-gap or no-internet requirements, teams that need deep customisation of configuration and network topology, and regulated... - Source: dev.to / 5 months ago
Helm 4 is the most significant release since Tiller was removed. New templating engine, dependency resolution changes, and the question everyone's asking: what breaks? The maintainers themselves walk through the migration path. - Source: dev.to / 5 months ago
Ready to try it out? Getting started with the operator is straightforward. You can use a local Kubernetes cluster such as minikube or kind and use Helm for installation. - Source: dev.to / 9 months ago
To get to a working deployment of the proposed app, though, you would probably need to learn at least a dozen different k8s concepts. Hereโs a short list of what you might need: a Deployment to describe Pods in a ReplicaSet along with a Service, Ingress and Ingress Controller to hook up your domain. Helm to install Cert Manager so you can get SSL working. Youโll likely need to learn about plenty more along the way. - Source: dev.to / 9 months 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
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
Mattermost - Mattermost is an open source alternative to Slack.
Rancher - Open Source Platform for Running a Private Container Service
Vast.ai - GPU Sharing Economy: One simple interface to find the best cloud GPU rentals.
Docker Compose - Define and run multi-container applications with Docker
ipinfo.io - Simple IP address information.