Item: Astra DB
Rating: 10
Author: Verified User

Overall Satisfaction with Astra DB

Use Cases and Deployment Scope

We use Astra DB as our primary vector database. It is used to store vector embeddings for unstructured data as well as other business metrics that can be fetched with a quick semantic search.

Pros and Cons

Pros

Semantic search.
MMR
Langhian integration.

Cons

Text search

Return on Investment

Faster time to market.
Cutting edge tech to build faster.
Exceptional support.

As a developer, working with Astra has been very easy and intuitive. It has helped me build and iterate faster. As a manager, it has helped me make sure that my team has the best and most reliable tools to build with.

Scalability

We work with 100s of 1000s of unstructured data files, which amount to 10s of GBs. We have never faced a scalability problem with Astra.

Alternatives Considered

Pinecone and Azure Cognitive Search

Astra provides all the features that the above products do while also giving us access to cutting-edge features faster.

Key Insights

Do you think Astra DB delivers good value for the price?

Yes

Are you happy with Astra DB's feature set?

Yes

Did Astra DB live up to sales and marketing promises?

Yes

Did implementation of Astra DB go as expected?

Yes

Would you buy Astra DB again?

Yes

Other Software Used

Azure Databricks, Azure DevOps Services, GitHub Copilot

Likelihood to Recommend

Astra DB is very well suited for scenarios where you need to use vector search. It offers all the functionality that other databases do and is always on the cutting edge of new technology. The fact that it is based on Cassandra makes it even easier for people looking for a do-it-all database. The only reason you might not want to consider it is if your existing database solution also provides vector search capabilities along with traditional DB features.

Comments

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Astra DB will help you break into GenAI and give you a technical advantage.