

Find out in this report how the two AI Data Analysis solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
In the first couple of years, I would not expect a return on investment because the initial setup will take more than a year if the process requires significant customization.
Automated exception handling cut manual process touchpoints by approximately 30 to 40%, saving approximately 15,000 to 20,000 operational labor hours annually.
In my opinion, there's a positive return on investment.
Thanks to Qdrant's open-source nature, our initial licensing and setup costs were nearly zero, allowing for swift testing and launch of our RAG prototype.
This lowers our LLM input token consumption by roughly 30 to 40 percent, translating directly into lower monthly OpenAI API bills.
The time saved is substantial, with nearly three weeks or more for projects deployed with Qdrant Cloud in no-code platforms.
It took more than two weeks to receive a response.
Celonis customer support is really good; they investigate concerns thoroughly and provide solutions or troubleshooting steps, which I find helpful.
Other times I do not get much clarity on the support from the team.
It's open source, so we house it on our server.
The documentation provided by Qdrant covers most queries effectively.
Qdrant's customer support is responsive and developer-focused.
I recall that when we started using Celonis, we had a space of five terabytes and around one thousand users, and Celonis managed all of that easily.
I recommend focusing on recent data or perhaps five years of historical data along with live data for better visibility and stability in the process.
Consumption-based pricing models would be helpful because scaling can get costly quickly.
In the recruiting agency project, the reliance on the vector database has expanded from storing hundreds of resumes to thousands.
When Qdrant is deployed in Docker, it scales really fast, and you can assign multiple CPUs to enhance performance.
Qdrant is highly scalable, supporting both vertical and horizontal scaling across massive vector data sets.
It's super stable.
Celonis is stable.
Built in Rust, it delivers sub-15 millisecond response times and rock-solid update and write-ahead logging to guarantee that newly indexed data is immediately searchable without dropping queries or producing inconsistent context for LLMs.
You need to patch Qdrant as soon as patches are released.
It is easy to use whether on LangChain or on its own.
Ultimately, I need niche expertise, combining strong SAP knowledge with Celonis competency.
It is essential for the Celonis solution to have their services and solution models integrated with GenAI.
The most important area for improvement is the automation part.
Fast large-scale filtering operations could be implemented, such as automatic index suggestions, adaptive query planning, and smart indexing of metadata fields, which would make Qdrant even more efficient.
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.
Incorporating embedding features directly in Qdrant Cloud would eliminate the need to depend on external solutions.
I think it's relatively expensive, but it's also good.
Based on client feedback, I have heard that the pricing for Celonis is considered high.
creating a data model for one process will differ in cost if you add more data models for additional processes.
The core product is open source under Apache 2.0, so initial experimentation and local integration testing cost nothing.
Using Qdrant is free.
Regarding pricing, setup costs, and licensing, since I am using only the free tier of Qdrant Cloud, there are no setup costs involved.
Celonis is also beneficial for its built-in apps that streamline tasks from legacy applications, facilitating daily operations and improving efficiency.
The standout feature of Celonis is definitely the Process Intelligence Graph, which creates a living digital twin across multiple systems and provides the operational context needed for effective AI and automation.
It provides a visualization of the process itself, giving a very good synthesis of performance and helping me find improvements.
The ability of Qdrant to handle high-dimensional vectors for my AI projects is pretty fast, and I think it's the best we have used so far.
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 supports high-dimension vectors and cosine similarity, which any vector database should have, and it is pretty fast.
| Product | Mindshare (%) |
|---|---|
| Qdrant | 0.4% |
| Celonis | 0.4% |
| Other | 99.2% |


| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 6 |
| Large Enterprise | 47 |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 2 |
Celonis empowers businesses with process mining, offering automation and AI features that visualize processes, identify bottlenecks, and optimize operations. With seamless integration and user-friendly tools, Celonis adds significant value to businesses aiming for operational efficiency.
Celonis is a leading tool for process mining and optimization, seamlessly connecting with SAP and Oracle systems through pre-built connectors. Its capabilities include visualizing processes, identifying inefficiencies, and optimizing workflows with comprehensive dashboards and action flows. While Celonis scores high on functionality, integration with Microsoft, Azure connectivity, and an improved pricing model are areas for improvement. Training resources and an intuitive interface are essential for users managing frequent updates and complex programming needs. With robust process analysis and automation features, Celonis enhances decision-making and resource allocation.
What key features does Celonis offer?In finance, procurement, and supply chain, Celonis is utilized to monitor and optimize entire business processes, analyzing data from systems like SAP and Oracle to uncover inefficiencies. Organizations leverage its process analysis and automation triggers for improved performance and resource allocation.
Qdrant is a powerful tool for efficiently organizing and searching large volumes of data. It is particularly useful for tasks such as data indexing, similarity search, and recommendation systems.
With fast and accurate results, it is suitable for various applications including e-commerce, content management, and data analysis. Users appreciate Qdrant's efficient search capabilities, high performance, and ease of use.
Its quick and accurate retrieval of relevant information allows for easy navigation and analysis of large datasets.
The intuitive interface and straightforward setup process make it accessible to users with varying levels of technical expertise.
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