Online serving types

Vertex AI Feature Store offers the following types of online serving that you can use to serve features for online predictions:

Bigtable online serving

Bigtable online serving is suitable for large data volumes (in the order of terabytes of data) with high data durability. It's comparable to online serving in Vertex AI Feature Store (Legacy) but isn't optimized to rapidly adjust to sudden bursts of traffic.

Generally, Bigtable online serving has higher latency than Optimized online serving, but is more cost-efficient.

To use Bigtable online serving, you need to perform the following steps:

  1. Create an online store for Bigtable online serving.

  2. Create a feature view instance.

  3. Fetch feature values using Bigtable online serving.

Optimized online serving

Optimized online serving lets you serve features at latencies that are significantly lower than Bigtable online serving. It provides an online serving architecture that's faster, more scalable, and more responsive to increased data volumes. Optimized online serving is suitable in scenarios where it's critical to serve features at ultra-low latencies.

With Optimized online serving, you can serve feature values from either a public endpoint or a Private Service Connect endpoint.

Optimized online serving with public endpoint

By default, an online store created for Optimized online serving lets you serve features with a public endpoint. To use Optimized online serving with a public endpoint, you need to perform the following steps:

  1. Create an online store for Optimized online serving with a public endpoint.

  2. Create a feature view instance.

  3. Fetch feature values using Optimized online serving from a public endpoint.

Optimized online serving with Private Service Connect endpoint

A Private Service Connect endpoint is a dedicated serving endpoint. Use a Private Service Connect endpoint if you want to serve features within a VPC network at lower latencies than a public endpoint. To use Optimized online serving with a Private Service Connect endpoint, you need to perform the following steps:

  1. Create an online store for Optimized online serving with a Private Service Connect endpoint.

  2. Create a feature view instance.

  3. Fetch feature values using Optimized online serving from the Private Service Connect endpoint.

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