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Haystack Enteprise Platform
The Haystack Enterprise Platform is an AI Orchestration solution for Gen AI applications and agents
Ratings and reviews
4
2 ratings
2 AWS reviews
Reviews (2)
reviewer2846073
Orchestration has made AI agents deliver grounded HR answers and personalized meeting insights
Reviewed on Jun 27, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Deepset AI Platform is utilizing it as an AI orchestration layer for RAG, search, and agentic workflows. I use this as an AI orchestration solution for GenAI applications and for AI agents.
A specific example of how I use Deepset AI Platform in my GenAI application is that our primary use case is helping AI agents retrieve the right information about meetings, culture, and HR and team context before answering. For that, I am using Deepset AI Platform's orchestration layer for fetching the right information.
I can confirm that Deepset AI Platform's main use case is helping AI agents retrieve relevant information.
What is most valuable?
Deepset AI Platform's best features include making our AI agents answer in the most grounded and actual company data instead of generic model output.
Deepset AI Platform helps make my AI agent's answers more grounded in company data by structuring retrieval pipelines and agentic workflows. There was a downside, as it added another orchestration layer that required engineering ownership.
Deepset AI Platform has had a positive impact on my organization by bringing stability, as our answers were previously generic. We were unable to use company data effectively in AI-generated answers. Now, with AI orchestration, I can get meaningful and personalized data from the company database, which allows me to answer more meaningfully.
What needs improvement?
Deepset AI Platform can be improved by simplifying pipeline management and providing easier debugging for complex Retrieval-Augmented Generation flows.
To improve my experience with Deepset AI Platform, a simpler dashboard for pipeline management and a dashboard for checking audit logs or easier debugging for complex flows would be hugely beneficial.
For how long have I used the solution?
I have been using Deepset AI Platform for a few months now.
What do I think about the stability of the solution?
Deepset AI Platform's accuracy and reliability of output are very good when the pipeline is simple and the data is already clean. However, when the data is not clean and the pipeline is complex, the quality and reliability of Deepset AI Platform decrease.
What do I think about the scalability of the solution?
Deepset AI Platform's scalability is strong for enterprise RAG use cases, especially when paired with the right vector database and the right data architecture.
How are customer service and support?
I am satisfied with the support and resources available from Deepset AI Platform when I need help, and the support has been helpful for architectural questions. I would rate Deepset AI Platform's customer support a 10.
Which solution did I use previously and why did I switch?
How was the initial setup?
It was fairly easy for my team to get started with Deepset AI Platform, and we did not face any significant onboarding challenges. Deepset AI Platform's documentation was good.
What was our ROI?
Specific outcomes since using Deepset AI Platform include ROI from fewer unsupported AI answers, faster context retrieval, and better manager trust in our quality answers. NPS is one of the major outputs from customers.
Which other solutions did I evaluate?
In comparison to other AI orchestration tools I have seen or used, such as LangChain, LlamaIndex, and Pinecone Assistant, Deepset AI Platform stands out in terms of the workflow I have. It is not very complex, and the pricing is good. For the workflow I have, Deepset AI Platform's quality is fairly good.
What other advice do I have?
My advice for others looking into using Deepset AI Platform is to know your use cases. There are many options in the market including LangChain, LlamaIndex, and Pinecone Assistant. It is crucial to understand your use case and the quality of your data. If the data quality is not good, I would prefer to use something else because Deepset AI Platform needs clean, good quality data with a proper vector database to produce high-quality output.
Deepset AI Platform does not have notable features for team collaboration or sharing workflows. I think they should include documentation about RAG design since our engineering team needs to figure out what kind of RAG design is most effective with Deepset AI Platform.
Regarding Deepset AI Platform's AI capabilities, I think the governance and security are very good as they have all the certificates needed for enterprise security.
I rate this product an 8 overall.
Clarence Ho
Pipeline framework has transformed how I evaluate RAG models and optimize vector search
Reviewed on May 07, 2026
Review from a verified AWS customer
What is our primary use case?
I have been using Deepset AI Platform for around six months. I use Deepset AI Platform mainly for Gen AI model evaluation for our RAG application. For example, we are using Deepset Haystack open-source platform with a Gen AI evaluation framework called Ragas. We use Haystack to quickly assemble our RAG application pipeline to take the prompts and then interact with our vector database, which is Pinecone, and then process the query, get the response, and then compare it with the reference results provided by humans. We then use the LLM as a judge to perform evaluation and output the score for developers to see in order to evaluate the chunking strategy of their vector database.
What is most valuable?
The best feature Deepset AI Platform offers is the pipeline feature that is very easy for me to compose the large language model as well as the vector database search and retrieval, allowing me to build the application and the evaluation script within a very short period of time.
The pipeline feature and the ease of composing with large language models and vector search save me a lot of time by not writing the code from scratch. I just build the pipeline because Deepset AI Platform provides the out-of-the-box integration with the tools and stack that I am using, including the OpenAI model as well as the Pinecone API. I do not need to implement the details; I just use the existing tools in Haystack, pulling it together for the pipeline. This allows me to avoid too much detailed coding and saves me a lot of work, enabling me to focus on the evaluation.
Deepset AI Platform positively impacts our organization because we previously did not use any framework for Gen AI applications, and the introduction of this stack provides a framework for our team. It lets our team think about it and shows that it is worth introducing a framework in the future.
What needs improvement?
No improvements are needed for Deepset AI Platform.
For how long have I used the solution?
I have been using Deepset AI Platform for around six months.
What other advice do I have?
I do not have anything else to add about my main use case or how I use Deepset AI Platform in my process. I do not want to add anything else about the features. My advice for others looking into using Deepset AI Platform would be to take a look at the documentation to understand how to build the pipeline, as well as the kind of components that are provided out of the box, including the model provider and the vector database provider. Taking a look at the examples and the documentation will help to gain more insight into how to better use Deepset AI Platform. I would rate this product an eight out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)