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.
Tech Lead developer in Information Technology at Mauden (Computer Software, 51-200 employees)
Chose IBM watsonx.ai
To be honest, for typical use cases such as RAG assistants, the core capabilities of the three platforms are quite similar on our opinion. All of them offer strong models, APIs, and tools for testing prompts. What we like about IBM watsonx.ai is that we are not tied to a …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose IBM watsonx.ai
Fascinating how it works fast and has the intelligence to data integration
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose IBM watsonx.ai
IT has a serviceability within support to pull impact searches forward for he team
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose IBM watsonx.ai
I think IBM watsonx.ai is more IBM products focused so it helps find information for IBM related products better. For example I would with IBM assembler, but this language doesn't have too much documentation in GitHub Copilot, so the results I get from it aren't as helpful
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
Verified User
Chose IBM watsonx.ai
I think Microsoft is getting behind on this technology (we did a comparison), so we are deciding to bet for IBM.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
Verified User
Chose IBM watsonx.ai
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 …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose IBM watsonx.ai
About the same features, but these two require extensions and plug-ins to have the same functionalities
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose IBM watsonx.ai
the governance of AI its very important for the develop and cycle of AI models
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Where IBM watsonx.ai is well suited, based on our team experience: Num#1: Internal knowledge assistants for large companies. Like GAIA already described, is a good example. IBM watsonx.ai works very well as the base for a RAG solution. Num#2: Projects where data privacy is important, because we know that with IBM watsonx.ai client data are not used to train the models and everything stays inside IBM Cloud. Num#3: Solutions that need flexibility on models or projects designed to last a long time, because models change during time, so it's important to use a service that permit a smooth change of model Less appropriate: Num#1: Large and complex agent-based systems. Because IBM watsonx.ai gives the models and the APIs, but you have to build everything else yourself. So agent logic, orchestration, tool calls, etc. For GAIA this was manageable, and building our own multi-agent layer gave us full control, it was ok. But for larger projects, with many agents, complex workflows and integrations with many business systems, this means a lot of custom development and maintenance. So better to use other services (like orchestrate)
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
I think IBM watsonx.ai is more IBM products focused so it helps find information for IBM related products better. For example I would with IBM assembler, but this language doesn't have too much documentation in GitHub Copilot, so the results I get from it aren't as helpful
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
For one of the usecase we were able to save 1.3 million dollar by creating the COB project in 2 months by leveraging the whole ecosystem and savibg of approximately 2800 hrs per annum
For the icd usecase wherre we have used the graph memgraph using docker to deploy it and created graphs and then created inference pipeline saved 20 minutes per medical coder and approximately of 3.5 million dollar savings as claims submission verifying on the payer side is a lengthy process
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
