

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.
In my opinion, there's a positive return on investment.
I have seen a return on investment, especially in time saved for my clients; in the incident management process, the average cycle time for handling tickets was over ninety-eight hours, but after identifying root causes, such as tickets being held due to wrong group allocation, the cycle time reduced to approximately thirty-eight hours.
The clearest financial metric is probably this: the cost of Pinecone, which is a few hundred dollars monthly, is easily offset by the productivity gains from not having analysts spend hours manually searching documents.
I have achieved a 30 to 40% reduction in time to go through the documentation because now I can ask a query from the chatbot, and it provides the result with the appropriate source link.
DevOps is relieved because they don't have to manage a vector database and security and all the things related to the vector database.
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.
For production issues where you need quick solutions, having more responsive support channels would be beneficial.
The customer support of Pinecone is very good; you send an email and receive a response within a few hours, typically four to five hours.
I haven't needed support because the documentation is good enough to help developers get up to speed.
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.
At the moment, I'd rate scalability six or seven out of ten.
It splits vector data into shards, and each shard can be independently indexed and queried, helping with parallel query execution.
We are storing close to around 600K items or entries in the database, and our indexing and retrievals are within seconds, often in microseconds.
Scalability has been solid. I have grown from around 10,000 vectors to 500,000 without hitting any hard times or performance issues.
It's super stable.
Celonis is stable.
It is able to withstand the enormous data load and manage it effectively.
I have had excellent uptime and cannot recall any significant outages affecting my production indexes over the past year.
Pinecone is stable, excelling in managed production scaling.
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.
When we started two years ago, there weren't any vector databases on AWS, making Pinecone a pioneer in the field.
In LangSmith, end-to-end API calls can be analyzed, showing what request came from the customer, what vector search was performed, what prompt was created, what call was given to the LLM, and what response was received from the LLM to the UI.
Regarding needed improvements, I would like to see more regional endpoints, particularly serverless regional endpoints, as that's the most important one, along with multi-modality support.
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.
For my setup, initial costs were low since I started small, but as I scaled to 500,000 vectors, the monthly bill grew noticeably.
The setup cost for us is nil, and the licensing and pricing are pretty decent.
Pricing was handled by the procurement team, but it follows a usage-based pricing model, and I have to pay for storage, read operations, and write operations.
Celonis is also beneficial for its built-in apps that streamline tasks from legacy applications, facilitating daily operations and improving efficiency.
It provides a visualization of the process itself, giving a very good synthesis of performance and helping me find improvements.
It's the first solution that combines business competence and capabilities with technological capabilities.
The namespaces feature allows us to break down or store data for each user separately, reducing interference and maintaining privacy as an important feature.
Pinecone has positively impacted my organization by helping people in needle-in-a-haystack situations, as previously they had to grind through PDF documents, PowerPoint documents, and websites, but now with Pinecone, they can ask questions and receive references to documents along with the page numbers where that information exists, so they can use it as a reference or backtrack, especially for things such as FDA approvals where they can quote the exact page number from PDF documents, eliminating hallucination and providing real-time data that relies on an external vector database with enough guardrails to ensure it won't provide information not in the vector database, confining it to the information present in the indexes.
Pinecone, on the other hand, is pay-as-you-go on the number of queries. You only pay for the queries that you hit.
| Product | Mindshare (%) |
|---|---|
| Celonis | 0.5% |
| Pinecone | 0.5% |
| Other | 99.0% |

| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 6 |
| Large Enterprise | 45 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 2 |
| Large Enterprise | 8 |
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.
Pinecone is a powerful tool for efficiently storing and retrieving vector embeddings. It is highly praised for its scalability, speed, and ease of integration with existing workflows.
Users find it particularly useful for similarity search, recommendation systems, and natural language processing.
Its efficient search capabilities, seamless integration with existing systems, and ability to handle large-scale datasets make it a valuable tool for data analysis and retrieval.
We monitor all AI Data Analysis reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.