

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.
It provides a positive return on investment for those who can connect multiple data sources and make data-driven decisions easily.
If you don't need to write a whole ETL to make the data virtualization, you need way fewer people to write a query instead of writing an ETL pipeline.
I have seen a return on investment, which showed up in improved customer satisfaction scores.
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.
The task that was happening before developing this product was taking around one hour, but now it is done in hardly one or two minutes.
I have seen a return on investment with Pinecone, as the application we built received positive feedback from internal stakeholders about how much it's helping them make business decisions and access information quickly at their fingertips.
They have a good methodology for learning how to use the tool.
Denodo's customer support team is very competent and responsive.
If we raise a ticket, it can be resolved or addressed within a reasonable time frame, so support is good.
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 would rate the customer support a nine out of ten.
For huge data requests, it cannot scale automatically; admin action is required.
Denodo's scalability comes into play specifically when there is data transfer.
My client has almost 100 million records, and the performance was impacted in a way that required optimization.
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.
We've rolled out the early version as a beta access to a few, maybe twenty to thirty customers.
I would rate it nine out of ten because it is very reliable, always performing as expected.
If JVM does not function properly, it may cause Denodo to fail to connect to different sources.
Denodo is stable and good.
It is able to withstand the enormous data load and manage it effectively.
Ensuring data caching is up to date is critical.
Denodo needs better communication on how the product can be deployed for specific solutions.
The system has dependencies on other environments, like JVM, which can affect its performance.
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.
One major issue I have noticed with Pinecone is that it does not allow me to search based on metadata.
For small companies, it's not a solution that most small companies can afford.
Denodo is considered pricey, limiting its use to large enterprises or government organizations that can afford its comprehensive setup.
Denodo's pricing is not affordable for small companies and is more suitable for medium to large enterprises.
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.
Denodo's ability to connect to multiple data sources and perform extract-transform-load (ETL) operations on the fly is noteworthy.
The most valuable feature of Denodo is its uniformity of self-site data access types, which allows it to connect to almost any storage technology and feed it transparently.
Denodo supports SQL base, so if you want to join two tables or two views, you can use SQL, which is an advantage as most developers or business people know SQL.
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 (%) |
|---|---|
| Denodo | 0.9% |
| Pinecone | 0.5% |
| Other | 98.6% |


| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 6 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 2 |
| Large Enterprise | 8 |
Denodo is a leading data integration, management, and delivery platform that uses a logical approach to enable data science, hybrid and multi-cloud data integration, self-service BI, and enterprise data services. Organizations of different sizes across various industries utilize the product to get above the data silos. The solution offers organizations the freedom to migrate data to the cloud, or logically unify data warehouses and data lakes, without affecting business. This can ultimately result in the evolution of data strategies.
The platform accelerates data provisioning through reduced data replication, provides business users the freedom to select their preferred applications, and enables consistent security and governance across multiple systems. The solution offers one of the leading logical data fabric solutions by initiating data virtualization and eliminating the complexity and exposing the data in business-friendly formats.
Denodo also offers modern data integration and management for hybrid and multi-cloud environments for Denodo Platform for Cloud. This service can be purchased through the bring-your-own-license (BYOL) option. Users seeking faster deployment can license the product to popular cloud providers, including Amazon AWS, Google Cloud Platform, and Microsoft Azure. The solution integrates, manages, and delivers data in complex environments with high performance, governance, and security. It also offers additional solutions, such as the Denodo Platform for Cloud Modernization, the Denodo Platform for Cloud Data Integration, and the Denodo Platform for Cloud Analytics, which overcome common cloud data challenges.
Denodo Features
At the beginning of 2022, version 8.0 of Denodo introduced several new key features of the platform. These include:
Denodo Benefits
Denodo offers various benefits for its users through its services. Some of the greatest advantages of using this platform include:
Reviews from Real Users
Naresh M., a senior application developer at a financial services firm, appreciates Denodo because it offers quick and simple web services creation.
Alisson M., a senior BigData DevOps engineer at Schaeffler, says that Denodo is great for queries and scouting data.
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.
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