
Apache Karaf
Docker
Google App Engine
Amazon S3
AWS Elastic Beanstalk
Apache ServiceMix
Cisco CloudCenter
GlusterFS
Lambda Face Recognition API
Mattermost
Vast.ai
ipinfo.io
Grafana
Platform.sh
PostHog
Causal App
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Based on our record, Lambda Face Recognition API seems to be a lot more popular than Apache Karaf. While we know about 27 links to Lambda Face Recognition API, we've tracked only 1 mention of Apache Karaf. 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.
Apache Karaf with OSGi works pretty nice using annotation based dependency injection with the declarative services, removing the need to mess with those hopefully archaic XML blueprints. Too bad it's not as trendy as spring and the developers so many of the tutorials can be a bit dated and hard to find. Karaf also supports many other frameworks and programming models as well and there's even Red Hat supported... Source: over 5 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
Docker - Docker is an open platform that enables developers and system administrators to create distributed applications.
Mattermost - Mattermost is an open source alternative to Slack.
Google App Engine - A powerful platform to build web and mobile apps that scale automatically.
Vast.ai - GPU Sharing Economy: One simple interface to find the best cloud GPU rentals.
Amazon S3 - Amazon S3 is an object storage where users can store data from their business on a safe, cloud-based platform. Amazon S3 operates in 54 availability zones within 18 graphic regions and 1 local region.
ipinfo.io - Simple IP address information.