Astra DB, now part of IBM watsonx.data

Score8.8 out of 10

64 Reviews and Ratings

What is Astra DB, now part of IBM watsonx.data?

Astra DB is a vector database for developers. In 2025 Datastax, the developer and supporter of Astra DB, was acquired. Astra DB is now available as a component of the IBM watsonx.data Multicloud offering.

Categories & Use Cases

Videos

Vector Data Editing
Provides tools for users to create, modify, and delete vector data, such as points, lines, and polygons.
Category average: 8.5
Symbolization and Styling
Provides options for symbolizing and styling vector data, such as changing colors, line thickness, or symbol types.
Category average: 8.3
Vector Data Connection
Users can connect the vector database software with various data sources to import and access vector data.
Category average: 8.3

Data Import/Export
Allows users to import vector data from various formats (e.g., shapefile, GeoJSON) and export vector data to compatible formats for interoperability.
Category average: 7.7
Attribute Management
Allows users to add, edit, and manage attributes associated with vector data, such as names, descriptions, or classification tags.
Category average: 7.9
Data Sharing and Collaboration
Enables users to share vector data with others and collaborate on projects or datasets, with options for access control and versioning
Category average: 7.3

Most Frequent Users

Top 3 industries using Astra DB, now part of IBM watsonx.data.

Based on HG Insights installation data

View all Reviews

#1 most frequent

Professional, Scientific, and Technical Services

22.6%

19 installations of 84

#2 most frequent

Information

17.9%

15 installations of 84

#3 most frequent

Educational Services

9.5%

8 installations of 84

Pros

Exceptional scalability and speed for large datasets
Efficient processing of vast and unpredictable data volumes
Robust vector data storage and querying capabilities

SP (Saladi Pullaya) Naidu View profile

Director of Cloud and Data in Information Technology at Digitial River (501-1000 employees employees)

Use Cases and Deployment Scope

We migrated all of our 6 business critical self managed DB clusters to AstraDB. Main business problem we are solving with Astra DB is to utilize On Demand capacity pay per use model for our seasonal traffic /capacity patterns. Second problem we are addressing is seamless maintenance and upgrade feature that comes with Managed Service. Scope of our use case is business critical transactional DB's for high availability and low latency usecases
Astra DB is able to scale up and scale down during our peak holiday season without any prior preparatory changes

Pros

Pay per use vs. fixed cost model
Maintenance and Security patching
Seamless Upgrades
Automatic scale up and Scale down
New features - CDC, Streaming are very useful

Cons

When there is a lag between regions for replication, there is no monitoring in place
Maintenance cycles are not planned in advance and many times with very short period of notice, maintenance is planned
Support team maturity is still requires more improvements

Return on Investment

Positive impact is through ROI - saving $250k/year with migration from self managed to Astra DB managed service
Team members spending less time on maintenance, patching and upgrades
Number of additional features such as Streaming, Vector Search are additional features available
Auto scaling feature takes out stress of estimating and preparing for critical peak periods

Other Software Used

Confluent

Verified User

C-Level Executive in Information Technology (51-200 employees employees)

Use Cases and Deployment Scope

It all comes down to how fast we can deliver value to our customers. Too often, great ideas are held back by technical limitations or the time it takes to build a scalable tech foundation. That’s why solutions like DataStax Astra DB catch my attention. Having a platform that provides a high-performance, globally scalable database within minutes completely changes the game. It gives product and engineering teams the freedom to test hypotheses, iterate quickly, and scale with agility, something that not long ago seemed out of reach. Technology serving business strategy.

Pros

Deliver value to our customers.
High-performance
Globally scalable database

Return on Investment

High-performance
Globally scalable database

Rick Atkinson

CTO in Finance and Accounting at Bay Point (11-50 employees employees)

Use Cases and Deployment Scope

We use Astra DB to store vectorized data from all of our company's unstructured files. This allows us to query these documents similar to how we query our structured SQL databases. One use case is the storage of vector data for all documents used in secure data rooms that typically contain hundreds of documents. It allows us to provide our investors with the ability to ask questions about the documents without opening each one. This saves them time and leads to a better customer experience.

Pros

API is straightforward and simple to use
Astra DB Dashboard is simple and easy to understand
The vectorize functionality simplifies the document embedding process
The document querying process is really good at extracting the necessary embeddings to send to an LLM

Cons

In some cases, the Astra Dashboard could be more intuitive, especially when creating new collections and assigning an embedding LLM

Return on Investment

Astra DB has improved our customer experience by providing a new and innovative way to review documentation of our offerings.
Astra DB brings the ChatGPT experience to our internal documents and system.

Alternatives Considered

Azure Databricks

Other Software Used

Microsoft Visual Studio Code, Azure App Service, Azure SQL Database

Knut-Magnar Aanestad

Legal process engineer in Product Management at Saga (11-50 employees employees)

Use Cases and Deployment Scope

Core in our Legal work assistant application used by thousands of lawyers. Used to be able to work with large context which is crucial in legal work, consisting of factual information and legal sources that need different ways of handling,. Also lawyers need to see the different type of information in relation to create final deliverables that are grounded in the context information.

Pros

Handles many documents
Accurate in retrieval
Fast

Cons

Relevance on specific criteria
More speed
Evaluation functionality

Return on Investment

Quite well proportioned cost and performance
Possible to include much information at ok price
Great partnership communication

Alternatives Considered

Azure AI Bot Service

Other Software Used

Microsoft Azure, Slack, Microsoft 365

Verified User

C-Level Executive (1-10 employees employees)

Use Cases and Deployment Scope

Maester leverages Astra DB’s advanced vector search and retrieval capabilities to create an adaptive AI system that continuously refines its responses based on real-world user interactions. Maester also relies on Astra’s performance for semantic clustering. Maester can suggest relevant prompts and related analyses by grouping semantically similar user queries. This approach improves user experience and drives feature adoption, highlighting capabilities such as advanced forecasting and custom financial reporting.

Pros

Superior Vector Search and Performance
Unified Architecture
Hands-On Developer Support
Low Latency & High Throughput

Return on Investment

We leverage Astra DB to support our self-optimization functionality. Our product delivers up to 50% greater accuracy with minimal overhead—no fine-tuning of large models is required.

Alternatives Considered

Pinecone

MongoDB

Amazon DynamoDB

Azure Cosmos DB

Astra DB, now part of IBM watsonx.data

What is Astra DB, now part of IBM watsonx.data?

Categories & Use Cases

Videos