Skip to content Skip to sidebar Skip to footer
Home / Lyft㢂¬„¢s $300 Million Deal With Aws: 5 Things You Need to Know

Lyft㢂¬„¢s $300 Million Deal With Aws: 5 Things You Need to Know

Post a Comment

5 Types of AWS Optimization Lyft is Already Using for that $300 One thousand thousand Cloud Bill

AWS optimization might be on your mind if you saw last week's headlines that Lyft has committed to spend $300 one thousand thousand with Amazon Web Services (AWS) per yr over the next three years. This information was revealed in Lyft's IPO prospectus, filed last Friday.

Lyft isn't the offset startup to generate attention from its massive public cloud bills — Snap and Spotify'south Google Cloud bills are but ii other examples.

And this level of spend is no surprise, either. Lyft was born and scaled to "unicorn" condition in the cloud, from the outset three EC2 servers that powered their outset ride to the massive infrastructure of microservices that now powers the ride sharing giant. The question is, how do they use those resource efficiently — with a mindset of AWS optimization?

Several example studies from AWS equally well as an AWS printing release put out last week tell united states how Lyft is already using cloud services — and give united states of america insight into how they're already well-versed in AWS optimization.

The fact that Lyft has such commitments at all tells us that they're taking reward of AWS's Enterprise Discount programme — as we would expect for any company with that scale of infrastructure. An EDP is a private agreement with AWS with a minimum spend commitment in substitution for discounted pricing — a smart move, as Lyft anticipates no slowing down in its use of AWS.

When you larn that Lyft does eight times as many rides on a Saturday night as they practise on Sun morning, yous realize the importance of machine scaling — scaling up to meet demand, and back down when the infrastructure is no longer needed.

AWS has a published case study with Lyft about their use of Spot Instances — AWS's offering of spare capacity at steeply discounted prices, which are interruptible and therefore simply useful in certain circumstances. By using Spot Instances for testing, Lyft reduced testing costs past 75%.

Lyft runs more than 150 microservices that use Amazon DynamoDB, Amazon EKS, and AWS Lambda — allowing private workloads to scale as needed for the myriad processes involved in the on-demand ride sharing service.

In addition to Amazon EKS, Lyft uses Amazon EC2 Container Registry (ECR) to store container images and evangelize these images to exam and deployment systems. They likely have a skillful start on the battle for container optimization, though in general, this marketplace will mature greatly this year — so information technology's something they're sure to continue to optimize.

The case studies and press releases mentioned to a higher place, as well as Lyft'due south ain engineering weblog, give some insight into their tech stack and processes. Beyond that, there are several things they may well exist focusing on, that nosotros would highly recommend equally they go along to calibration (and IPO):

Many deject customers we talk to name governance as their top priority. Automated policies and user roles are key for ensuring that no one tin can spend outside their premises. Sometimes, it's as elementary an idea as proper tagging — only one that can set automated processes in motion to assign resources access to team members, proper on/off schedules for non-production resources, and configuration management processes.

Our recent research showed that boilerplate CPU utilization for the instances in our information set (which leaned non-production) was less than 5%. Given that going i instance size downward can save 50% of the cost, and two sizes can salve 75%, this is a huge surface area for optimization that we recommend cloud users of all sizes focus on this year. At Lyft's calibration, this volition require automated policies to resize underutilized resource automatically.

With 150 microservices, blanket policies won't utilise to all cases. Each microservice needs to be evaluated confronting newer AWS offerings and price control techniques on an individual basis. Once each of the 150 has been evaluated, it's time to go back to the beginning of the listing and beginning over again — a mindset of continuous cost command would serve them well.

Lyft has gotten this far built and grown on AWS — and their "culture of cloud" has enabled the growth in platform adoption that has brought them to the brink of IPO. 1 affair is clear: upward to this point, growth at any cost has been the goal. That means that the mere amount of deject spend has not been of huge concern. Equally they transition into being a public company, margins and profit will showtime to matter more than, which will bring costs into focus. Information technology will shortly be of import for Lyft to continually optimize infrastructure — in the cloud and beyond the board.

Originally published at world wide web.parkmycloud.com on March v, 2019.

tylervestoing.blogspot.com

Source: https://jaychapel.medium.com/5-types-of-aws-optimization-lyft-is-already-using-for-that-300-million-cloud-bill-7d40413cd434

You may like these posts

Post a Comment for "Lyft㢂¬„¢s $300 Million Deal With Aws: 5 Things You Need to Know"