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Prophecy vs Qdrant comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Prophecy
Ranking in AI Data Analysis
296th
Average Rating
8.0
Reviews Sentiment
5.8
Number of Reviews
3
Ranking in other categories
Data Preparation Tools (16th)
Qdrant
Ranking in AI Data Analysis
10th
Average Rating
8.8
Reviews Sentiment
5.4
Number of Reviews
9
Ranking in other categories
Open Source Databases (8th), Vector Databases (4th)
 

Mindshare comparison

As of August 2026, in the AI Data Analysis category, the mindshare of Prophecy is 0.0%. The mindshare of Qdrant is 0.4%, down from 2.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Data Analysis Mindshare Distribution
ProductMindshare (%)
Qdrant0.4%
Prophecy0.0%
Other99.6%
AI Data Analysis
 

Featured Reviews

MM
Data Measurement & Reporting Advisor at Evernorth/Cigna
Automated data collection has replaced manual emails and now connects business forms to back-end reports
Prophecy can be improved, as right now the form is very limited. We have to do a lot of manual entry with the form to get it set up. We have a lot of things that change year over year, so I have to go in there and manually play around with it, manually changing this and that. Having dynamic forms, having the forms connect to back-end data, and basically making the forms more streamlined would be my need from Prophecy. Everything else on the back end, the pipelines, and those types of things have been pretty easy to understand. I think once you get things set up, I've been very impressed with it. The only improvement at least for my side would be the form.
Chirag Morajkar - PeerSpot reviewer
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
Building accurate no-code resume screeners has saved weeks in document search workflows
I see room for improvement in Qdrant based on what another platform called Weaviate offers. Qdrant provides an excellent vector database with a solid searching method. However, it could elevate its offering by integrating embedding features. Currently, for the workflow automation I build, I rely on other platforms for embedding, so incorporating this feature directly in Qdrant Cloud would eliminate the need to depend on external solutions. A pain point I have encountered was the inactive expiration of the cloud created for certain projects. If the cloud is not used for a week, it gets terminated, which is frustrating. I think increasing that inactivity window in the free tier would be beneficial, as I have faced limitations due to this seven-day inactivity rule, requiring me to reset up the cloud after its termination.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The form feature of Prophecy is so valuable for our team because it saves us a lot of time."
"Prophecy has positively impacted my organization by greatly speeding up development time and providing capabilities of using DBT alongside functionalities associated with that, making it a very good tool to avoid coding much and utilize AI with the graphical tools available without needing to code."
"Prophecy has positively impacted our organization by allowing us to offer it as an ETL layer for our incentive management application, which has improved our story and demos to customers during the sales cycle."
"Qdrant is a good and scalable vector database, and it is free."
"Qdrant has positively impacted my organization by consuming much less time than building systems through coding."
"Qdrant has positively impacted my organization mainly by reducing usage costs for AI because in the older system, in order to get the context, we were just dumping everything into the system, which would inflate the cost of the API, whatever AI we were using, such as OpenAI or Claude."
"We saw a clear return on investment from Qdrant, particularly in the engineering time saved and the empowerment of team members to handle self-service tasks instead of reducing headcount."
"An accuracy boost was definitely observed from 45 to 50% using Faiss to around 85 to 95% using Qdrant, and the users are really happy as they are getting suggested really good schemes that would take a lot of time to find."
"Qdrant is an excellent vector database that anyone would want to use with RAG AI."
"Using Qdrant's hybrid search capability has improved my search results."
"Qdrant is one of the best vector databases out there that is also open source."
 

Cons

"Prophecy can be improved, as right now the form is very limited."
"Prophecy is pretty stable, but there are often some unexpected issues that arise, especially performance issues or problems with Airflow on deployments."
"Prophecy is generally stable, although it does have a few issues at times, such as the Fabric connection randomly dropping and the environment freezing occasionally."
"One of the key limitations is that Qdrant does not have built-in role-based access control, and while being self-hosted is a benefit, it can also be improved."
"Qdrant is available through a containerized Docker, but a normal deployment in Qdrant is not there, and that can actually be worked out."
"The file system lock in Qdrant prevents the API and scripts from hitting it directly, and to surpass this limitation, I have to run Qdrant client as a service, which incurs additional costs for running it continuously, so if something about that could be done, it would be really amazing."
"There can always be improvements needed for Qdrant, but I do not want to add anything at this time."
"Architectural complexity was a key friction point, as our primary database was set in Supabase, necessitating synchronization of two separate systems for user data, permissions, and states."
"A lot of our work is agentic right now, and we have also segmented the content to be logical, so there's not a lot of vector search anymore."
"A pain point I have encountered was the inactive expiration of the cloud created for certain projects; if the cloud is not used for a week, it gets terminated, which is frustrating."
"The area for improvement in Qdrant is its clustering capability. While it has clustering functionality, it is not easy to set up, and not everyone can configure the clustering, so there is room for improvement in the clustering configuration."
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Top Industries

By visitors reading reviews
Outsourcing Company
59%
Healthcare Company
23%
Manufacturing Company
5%
Construction Company
3%
Comms Service Provider
12%
Manufacturing Company
11%
Financial Services Firm
10%
Computer Software Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business10
Midsize Enterprise1
 

Questions from the Community

What is your experience regarding pricing and costs for Prophecy?
My experience with Prophecy's pricing, setup cost, and licensing is that the tool can be expensive, and the licensing adds more to that. We try to add the licensing and increase the number only whe...
What needs improvement with Prophecy?
There are a few things to improve in Prophecy, such as scheduling the models and jobs; with the introduction of Prophecy Automate, I think that will improve that. I also see that there are some mis...
What is your primary use case for Prophecy?
I have been using Prophecy for the last year and a half. My main use case for Prophecy is to perform data transformations in Snowflake. A quick specific example of a data transformation I use Proph...
What is your experience regarding pricing and costs for Qdrant?
My experience with pricing, setup cost, and licensing for Qdrant is that it is quite straightforward.
What needs improvement with Qdrant?
I would want Qdrant to support image vectorization, as I personally have that need within our organization, and since we have been using Qdrant for a while, it seems like a relevant feature to add....
What is your primary use case for Qdrant?
My main use case for Qdrant is as a vector database. As a vector database, I use Qdrant in a sequence and pipeline automation software that needs to look up information about certain items, each of...
 

Comparisons

 

Overview

 

Sample Customers

JP Morgan Chase, Marks & Spencer, Ralph Lauren, Pfizer, Acxiom
1. Airbnb 2. Amazon 3. Apple 4. BMW 5.Cisco 6. CocaCola 7. Dell 8. Disney 9. Google 10. HP 11. IBM 12. Intel 13. JPMorgan Chase 14. Kraft Heinz 15. L'Oreal 16. McDonalds 17. Merck 18. Microsoft 19. Nike20. Oracle 21. PG 22. PepsiCo 23. Procter and Gamble 24. Samsung 25. Shell 26. Sony 27. Toyota 28. Visa 29. Walmart 30. WeWork
Find out what your peers are saying about Prophecy vs. Qdrant and other solutions. Updated: July 2026.
909,725 professionals have used our research since 2012.