Lyft plans to spend $300 million on Amazon Web Services through 2021

Over the past couple of years, companies ranging from small startups to large enterprises have shown a growing interest in public clouds such as AWS. So many AWS case studies are not surprising, given that both the operation expenses model of public clouds and their abundance of available managed services make it very easy to start a business in the cloud or migrate and existing one to it.

While we did have an idea of the scale at which some businesses were utilising AWS, some of the recent headlines still caught many by surprise. “Pinterest has spent $309 million on Amazon’s cloud since 2017 as part of a $750 million contract,” one read. “Slack to spend at least $250 million on Amazon Web Services over five years,” and “Lyft committed to spend $300 million on AWS between 2019 and 2021,” are others. This article will focus on why these major companies are paying such large sums to use AWS infrastructure, and why they are making such huge commitments to public cloud use.

Lyft

Lyft is a San Francisco-based ride sharing company that is on Fortune magazine’s “Unicorn” list of hot startups, with a valuation of $5.5 billion.With a 30% market share, Lyft is the second-largest ride sharing company in the United States after Uber.

Lyft is the fastest growing ride share company in the United States and is available in more than 200 cities, facilitating 14 million rides per month. Lyft uses AWS to move faster as a company and manage its exponential growth, leveraging AWS products to support more than 100 micro services that enhance every element of its customers’ experience. Lyft launched on AWS and dramatically expanded its use of AWS products as they became available. It uses products such as Auto Scaling to manage up to eight times more riders during peak times and Amazon Redshift to gain customer insights that power the company’s shared-ride product, Lyft Line. It uses Amazon Kinesis to funnel production events through the system, and leverages the scalability of Amazon DynamoDB for multiple data stores, including a ride-tracking system that stores GPS coordinates for all rides. As part of their microservices infrastructure on AWS, Lyft relies on Amazon EC2 Container Registry (ECR) to durably store container images for their applications and reliably deliver these images to downstream test and deployment systems.

The Challenge

• Runs a microservices infrastructure on AWS.
• Uses Jenkins orchestration of a Continuous Integration (CI) pipeline; Jenkins manages the builds and tests, automating the process.
• Deploying code into production involves a series of tests that require large pools of compute resources, which led to high costs for routine daily tasks

Why Amazon Web Services

• The Infrastructure team found it could save up to 90 percent on its CI processes by using Amazon EC2 Spot instances with more compute capacity instead of using higher-priced on-demand instances.
• Used four lines of code to modify a Salt module that launches new instances from the Spot market.
• CI processes do not require a lot of power for testing, enabling use of older-generation, lower-cost Amazon EC2 instances.

The Benefits

• Saves up to 75 percent monthly for testing processes compared to using on-demand instances.
• Can leverage older-generation instances with greater memory resources to run more containers, allowing tests to finish faster.

How Lyft is Already Optimizing AWS

Several case studies from AWS as well as an AWS press release put out last week tell us how Lyft is already using cloud services — and give us insight into how they’re already well-versed in AWS optimization.

1. Commitment

The fact that Lyft has such commitments at all tells us that they’re taking advantage of AWS’s Enterprise Discount program — 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 exchange for discounted pricing — a smart move, as Lyft anticipates no slowing down in its use of AWS.

2. Auto Scaling

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

3. Spot Instances

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 only useful in certain circumstances. By using Spot Instances for testing, Lyft reduced testing costs by 75%.

4. Microservices Architecture

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

5. Pre-Built Container Configuration

In addition to Amazon EKS, Lyft uses Amazon EC2 Container Registry (ECR) to store container images and deliver these images to test and deployment systems. They likely have a good start on the battle for container optimization, though in general, this market will mature greatly this year — so it’s something they’re sure to continue to optimize.

What Can Be Learned Here?

When startups begin to grow exponentially, the cloud seems to be an easy (if expensive) solution that provides new companies with the agility they need as well as the ability to scale almost infinitely very quickly. When companies reach these high levels of hardware requirements, owning and maintaining the necessary bare metal is typically not a viable solution. For all three of these companies, the advantages that AWS cloud provides seem to outweigh its enormous costs.

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