# Online store


The online store is a low-latency, high-availability data store that provides real-time lookup of features. It is typically used for machine learning (ML) model serving. You can chose between the standard online store (`Standard`) or an in-memory tier online store (`InMemory`), at the point when you create a feature group. In this way, you can select the storage type that best matches the read and write patterns for a particular application, while considering performance and cost. For more details about pricing, see [Amazon SageMaker Pricing](https://aws.amazon.com/sagemaker/pricing/).

The online store contains the following `StorageType` options. For more information about the online store contents, see [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OnlineStoreConfig.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OnlineStoreConfig.html). 

## Standard tier storage type


The `Standard` tier is a managed low-latency data store for online store feature groups. It provides fast data retrieval for ML model service for your applications. `Standard` is the default storage type.

## In-memory tier storage type


The `InMemory` tier is a managed data store for online store feature groups that supports very low-latency retrieval. It provides large-scale real-time data retrieval for ML model serving used for high throughput applications. The `InMemory` tier is powered by Amazon ElastiCache (Redis OSS). For more information, see [What is Amazon ElastiCache (Redis OSS)?](https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/WhatIs.html).

The online store `InMemory` tier supports collection types, namely list, set, and vector. For more information about the `InMemory` collection types, see [Collection types](feature-store-collection-types.md).

Feature Store provides low latency read and writes to the online store. The application latency is primarily made up of two primary components: infrastructure or network latency and Feature Store API latency. Reduction of network latency helps with getting the lowest latency reads and writes to Feature Store. You can reduce the network latency to Feature Store by deploying AWS PrivateLink to Feature Store Runtime endpoint. With AWS PrivateLink, you can privately access all Feature Store Runtime API operations from your Amazon Virtual Private Cloud (VPC) in a scalable manner by using interface VPC endpoints. An AWS PrivateLink deployment with the `privateDNSEnabled` option set as true:
+ It keeps all Feature Store read/write traffic within your VPC.
+ It keeps traffic in the same AZ as the client that originated it when using Feature Store. This avoids the “hops” between AZs reducing the network latency.

Follow the steps in [Access an AWS service using an interface VPC endpoint](https://docs.aws.amazon.com/vpc/latest/privatelink/create-interface-endpoint.html) to setup AWS PrivateLink to Feature Store. The service name for Feature Store Runtime in AWS PrivateLink is `com.amazonaws.region.sagemaker.featurestore-runtime`.

The `InMemory` tier online store scales automatically based about storage usage and requests. The automated scaling can take a few minutes to adapt to a new usage pattern if it changes rapidly. During automated scaling:
+ Write operations to the feature group may receive throttling errors. You should retry your requests a few minutes later.
+ Read operations to the feature group may receive throttling errors. Standard retry strategies are suitable in this case.
+ Read operations may see elevated latency.

The default `InMemory` tier feature group maximum size is 50 GiB.

Note that the `InMemory` tier currently supports online feature groups only, not online\$1offline feature groups, so there is not replication between online and offline stores for the `InMemory` tier. Also, the `InMemory` tier does not currently support customer managed KMS keys.