About Astra DB Serverless

DataStax Astra DB provides the tools developers need to create robust AI applications, including APIs, real-time data processing, and integrations.

Astra DB Serverless is a serverless, multi-cloud database service (DBaaS) built on Apache Cassandra® that is optimized for real-time applications that require large data volume, low latency, and flexible data models.

Astra DB offers a consumption-based model for operations and billing, and your databases automatically scale up and down based on usage. With Astra DB DBaaS, you can spend your time developing, instead of provisioning compute units, tuning clusters, or specifying compaction

To explore some real-world examples, see Code examples and DataStax use cases.

Serverless databases

Astra DB Serverless offers Serverless (Vector) and Serverless (Non-Vector) databases.

Serverless (Vector) databases are perfect for managing mission-critical AI workloads. They’re a highly-scalable, reliable, and powerful all-in-one data storage solution, ideal for generative AI projects.

Serverless (Non-Vector) databases are designed for generic applications, such as content management, user authentication, and transactional applications.

For more information, see Create an Astra DB Serverless database and What are vector databases?.

Database terminology

Depending on your background and experience, you might be familiar with various terminology for database components. For example, structured databases use terms like tables, rows, and columns. Whereas semi-structured databases use collections, documents, and fields to refer to functionally similar or identical components.

In Astra DB, the terminology you encounter depends on the database types and features that you use. The following table explains some of these terms. Each set of terms describe similar components, but these components are not necessarily functional equivalents. For example, a single field of vector data doesn’t necessarily translate directly to a single column of structured, non-vector data.

Serverless (Vector) databases Serverless (Non-Vector) databases Description

Serverless (Vector) databases	Serverless (Non-Vector) databases	Description
Keyspace	Keyspace	A container for one or more collections or tables within a database.
Collection or table	Table	A container for data within a keyspace. The primary difference is the schema type: Collections use dynamic schemas and store data in documents. With a dynamic schema, each document can have different fields. Collections are best for semi-structured data. Collections are available only in Serverless (Vector) databases. Tables use fixed schemas and store data in rows. With a fixed schema, all rows must have the same columns, and every column must have a value. Tables are best for structured data. For more information, see Manage collections and tables.
Document or row	Row	A contiguous piece of data, having one or more properties (values), that is stored in a collection or table in a database. Documents belong to collections, and document properties are stored in fields. Rows belong to tables, and row properties are stored in columns.
Field or column	Column	The individual values, properties, or attributes that make up a complete document or row of data. For example, a table containing purchase history for an online store might include values like `name`, `address`, `customer_id`, `purchase_date`, and `order_id` in each row. Properties can be stored as various data types, like text, numbers, arrays, dates, booleans, and vectors.

Keyspace

A container for one or more collections or tables within a database.

Collection or table

Table

A container for data within a keyspace.

The primary difference is the schema type:

Collections use dynamic schemas and store data in documents. With a dynamic schema, each document can have different fields. Collections are best for semi-structured data.

Collections are available only in Serverless (Vector) databases.
Tables use fixed schemas and store data in rows. With a fixed schema, all rows must have the same columns, and every column must have a value. Tables are best for structured data.

For more information, see Manage collections and tables.

Document or row

Row

A contiguous piece of data, having one or more properties (values), that is stored in a collection or table in a database.

Documents belong to collections, and document properties are stored in fields.

Rows belong to tables, and row properties are stored in columns.

Field or column

Column

The individual values, properties, or attributes that make up a complete document or row of data. For example, a table containing purchase history for an online store might include values like name, address, customer_id, purchase_date, and order_id in each row.

Properties can be stored as various data types, like text, numbers, arrays, dates, booleans, and vectors.

Insert and find data

You can insert and find data in your Astra DB Serverless databases through the Astra Portal and multiple programmatic options, including APIs, clients, drivers, and CLIs. For more information, see the following:

Vectorize

With Astra DB vectorize, you can integrate external and Astra-hosted embedding providers to automatically generate an embedding from text for any operation that requires a vector.

If you configure your collections to automatically generate embeddings this way, then you can perform vector searches by providing a natural-language text string. Vectorize generates an embedding from your text string, and then runs the vector search.

Astra Streaming

Astra Streaming is a modern cloud data streaming and event stream processing service tightly integrated into the Astra Portal and powered by Apache Pulsar™.

Change Data Capture (CDC) for Astra Streaming provides connectivity for Astra DB Serverless databases with Astra Streaming enabled.

Connect and integrate

In addition to the Astra Portal, you can interact with your databases through Astra CLI, the DevOps API, the Data API, and Cassandra Query Language (CQL) queries.

DataStax offers clients and drivers to facilitate programmatic interactions. For more information, see Connect to a database.

Astra DB Serverless also offers a range of integrations with third-party tools.

Administer and secure

By default, Astra DB encrypts data.

Astra supports RBAC, SSO, IP access lists, application tokens, customer keys, and other security and administration functionality.

For a programmatic approach to administration, you can use the DevOps API.

Get started with Astra DB Serverless

When you’re ready to take the next step, DataStax offers several subscription plans. For information about each plan, including purchasing options and support plans, see the pricing page.

For information about migrating your data and applications to Astra DB, see Migrate to Astra DB Serverless.