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.
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IBM watsonx.ai
Score 8.7 out of 10
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Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models, and traditional machine learning into a studio spanning the AI lifecycle. Watsonx.ai can be used to train, validate, tune, and deploy generative AI, foundation models, and machine learning capabilities, and build AI applications with less time and data.
We've been super happy with Astra DB. It's been extremely well-suited for our vector search needs as described in previous responses. With Astra DB’s high-performance vector search, Maester’s AI dynamically optimizes responses in real-time, adapting to new user interactions without requiring costly retraining cycles.
I have built a code accelerator tool for one of the IBM product implementation. Although there was a heavy lifting at the start to train the model on specifics of the packaged solution library and ways of working; the efficacy of the model is astounding. Having said that, watsonx.ai is very well suited for customer service automation, healthcare data analytics, financial fraud detection, and sentiment analysis kind of projects. The Watsonx.ai look and feel is little confusing but I understand over a period of time , it will improve dramatically as well. I do feel that Watsonx.ai has certain limitations from cross-platform deployment flexibility. If an organization is deeply invested in a multi-cloud environment, Watson's integration on other cloud platforms may not be seamless comported to other AI platforms.
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.
The support team sometimes requires the escalate button pressed on tickets, to get timely responses. I will say, once the ticket is escalated, action is taken.
They require better documentation on the migration of data. The three primary methods for migrating large data volumes are bulk, Cassandra Data Migrator, and ZDM (Zero Downtime Migration Utility). Over time I have become very familiar will all three of these methods; however, through working with the Services team and the support team, it seemed like we were breaking new ground. I feel if the utilities were better documented and included some examples and/or use cases from large data migrations; this process would have been easier. One lesson learned is you likely need to migrate your application servers to the same cloud provider you host Astra on; otherwise, the latency is too large for latency-sensitive applications.
I still don't have enough experience, but i have seen a lot of demos and i have made some real world scenarios and so far so long every thing looks fine. I was at IBM Think 2025 and IBM TechXchange 2025 and the labs were really usefull and simple to understand.
I needed some time to understand the different parts of the web UI. It was slightly overwhelming in the beginning. However, after some time, it made sense, and I like the UI now. In terms of functionality, there are many useful features that make your life easy, like jumping to a section and giving me a deployment space to deploy my models easily.
Their response time is fast, in case you do not contact them during business hours, they give a very good follow-up to your case. They also facilitate video calls if necessary for debugging.
I still don't have enough experience, but i have seen a lot of demos and i have made some real world scenarios and so far so long every thing looks fine. I was at IBM Think 2025 and IBM TechXchange 2025 and the labs were really usefull and simple to understand.
Graph, search, analytics, administration, developer tooling, and monitoring are all incorporated into a single platform by Astra DB. Mongo Db is a self-managed infrastructure. Astra DB has Wide column store and Mongo DB has Document store. The best thing is that Astra DB operates on Java while Mongo DB operates on C++
IBM watsonx.ai has been far superior to that of Chat GPT AI. the UI elements prompt responses and overall execution of the AI was much better and more accurate compared to the competition. I can not recommend using this platform enough. Great job IBM. I hope the team behind this project continues to grow and prosper.
We are well aware of the Cassandra architecture and familiar with the open source tooling that Datastax provides the industry (K8sSandra / Stargate) to scale Cassandra on Kubernetes.
Having prior knowledge of Cassandra / Kubernetes means we know that under the hood Astra is built on infinitely scalable technologies. We trust that the foundations that Astra is built on will scale so we know Astra will scale.
I still don't have enough experience, but i have seen a lot of demos and i have made some real world scenarios and so far so long every thing looks fine. I was at IBM Think 2025 and IBM TechXchange 2025 and the labs were really usefull and simple to understand.
