Serverless services let developers use storage, compute, and databases through APIs without managing the underlying clusters. Yet the common serverless stack still has one cluster-shaped component: the cache.
The gap in a serverless stack
Cloud architectures are progressing toward serverless-first designs. Serverless databases, event-driven functions, and microservices help applications meet customer demand without requiring developers to operate every layer of infrastructure.
Amazon S3 was one of the first serverless cloud services when it launched in 2006. It provides access to object storage without servers to manage or scale. Amazon DynamoDB followed in 2012 and changed the game with its ease of use and ability to serve massive-scale applications.
Older “serverful” database processes have trouble keeping up with the performance and scaling complexity of cloud-scale applications. As a result, data models are transitioning to serverless services across relational, key-value, document, graph, and time-series data stores. Developers want to access data through APIs rather than manage a database cluster, even through a managed service.
The launch of AWS Lambda in 2015 brought serverless into the mainstream and established it as a common architectural practice. Event-driven services such as AWS Lambda and Google Cloud Functions automate compute for specified events and actions. That reduces the time developers spend on infrastructure management and operations.
With the rise of Functions as a Service (FaaS), the gold-standard serverless stack in the AWS ecosystem combines Amazon API Gateway, AWS Lambda, and either Amazon DynamoDB or Amazon Aurora Serverless v2. The Google Cloud equivalent combines Google Cloud API Gateway, Google Cloud Functions, and Google Cloud Datastore. Both stacks are missing the same piece: a serverless cache.
Why cluster-based caches break the model
A managed cache still requires you to provision and manage nodes in a cluster. A serverless service lets developers consume the data model through APIs without managing that infrastructure.
Workarounds for serverless caching existed, but there was no straightforward solution. Legacy caching services are cluster-based and cannot truly autoscale. A typical cache node also has a fixed maximum client connection rate relative to its total cache size.
Those constraints lead teams to overprovision caches. Client connections can also become a weak point when they cannot meet bandwidth demands, resulting in outages in the worst cases.
Fill the gap with Momento Cache
Momento Cache fills this gap. It is the world’s first genuinely serverless cache and automatically adjusts to handle traffic bursts, cache hit rates, and tail latencies.
Momento Cache automatically manages hot keys, shards, replicas, and nodes. That helps keep applications available and responsive at any scale. You can deploy a cache in minutes instead of spending weeks testing and overprovisioning inefficient cluster-based services.
Choose your cloud and accelerate serverless tools such as Amazon DynamoDB, AWS Lambda, Amazon Aurora Serverless v2, Google Cloud Functions, and Google Cloud Datastore. If the cache is the last cluster in your stack, try Momento Cache through the self-service CLI.
