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Astra DB

Score8.2 out of 10

52 Reviews and Ratings

What is Astra DB?

Astra DB from DataStax is a vector database for developers that need to get accurate Generative AI applications into production, fast.

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Categories & Use Cases

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Astra DB Key Features

Learn about the best (and worst!) features Astra DB has to offer, as determined by TrustRadius' reviewers.
Based on 52 ratings of Astra DB's features
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Top Performing Features

  • Attribute Management

    Allows users to add, edit, and manage attributes associated with vector data, such as names, descriptions, or classification tags.

    Category average: 9.1

  • Vector Data Connection

    Users can connect the vector database software with various data sources to import and access vector data.

    Category average: 8.1

  • 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: 7.4

Areas for Improvement

  • Vector Data Visualization

    Provides options for visualizing vector data through maps, charts, or graphical representations.

    Category average: 6.3

  • 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: 6.3

  • Coordinate Reference System Management:

    Supports the management and transformation of vector data between different coordinate reference systems, ensuring accurate spatial positioning.

    Category average: 5.5

Who Buys & Uses Astra DB

Astra DB Reviews

What users are saying about Astra DB.
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Astra DB Handles Your RAG AI Needs

Incentivized
Rick Atkinson
CTO in Finance and Accounting at Bay Point (11-50 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

AstraDB Managed Cassandra Service in Enterprise Environment

Incentivized
SP (Saladi Pullaya) NaiduView profile
Director of Cloud and Data in Information Technology at Digitial River (501-1000 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

Astra DB carrying out customers work further

Incentivized
Knut-Magnar Aanestad
Legal process engineer in Product Management at Saga (11-50 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

Astra DB made it easier to focus on driving customer value

Incentivized
Verified User
C-Level Executive (1-10 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

Astra DB - great for fluctuating workloads and consistently fast

Incentivized
Verified User
Vice-President in Product Management (51-200 employees)

Use Cases and Deployment Scope

We use Astra DB as the primary data store for our Enrich and Engage products. Enrich is an AI-driven financial transaction enrichment product composed of several models that identify valuable information from data provided either by core banking or digital banking platforms used by financial institutions or retrieved using open banking connectivity. Engage is a set of tools that help financial institutions improve their digital channels with a focus on financial literacy, financial management and making better decisions based on enriched data.

Pros

  • We need to be able to process a lot of data (our biggest clients process hundreds of milions of transactions every month). However, it is not only the amount of data, it is also an unpredictable patterns with spikes occuring at different points of time - something athat Astra is great at.
  • Our processing needs to be extremaly fast. Some of our clients use our enrichment in a synchronous way, meaning that any delay in processing is holding up the whole transaction lifecycle and can have a major impact on the client. Astra is very fast.
  • A close collaboration with GCP makes our life very easy. All of our technology sits in Google Cloud, so having Astra in there makes it a no-brainer solution for us.

Cons

  • We feel that some monitoring/observability improvements could be made - we have lots of different data streams in multiple regions, and being able to monitor consistently across all (incl. the storage) makes life easier.
  • As with any solution deployed at a large scale, cost is always a major factor

Return on Investment

  • Moving from previous solution (a self-hosted Cassandra) enabled us to move towards less compex, less labour-intense and ultimately less expensive solution.
  • Compared to the previous solution, we have been able to move from servicing clients that had dozens of thousands of end users to clients serving millions - operationally, that would have been very difficult to achieve without Astra, especially with the latencies and level of service required.

Alternatives Considered

Apache Cassandra, Scylla and Redis Cloud

Other Software Used

Google BigQuery, Looker, Google Gemini, MySQL, Perplexity

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