Matillion Data Productivity Cloud vs Pinecone comparison

Matillion Data Productivity...

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Executive Summary

We performed a comparison between Matillion Data Productivity Cloud and Pinecone based on real PeerSpot user reviews.

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.

To learn more, read our detailed Matillion Data Productivity Cloud vs. Pinecone Report (Updated: December 2025).

Matillion Data Productivity Cloud vs. Pinecone

December 2025

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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

Matillion Data Productivity...

Ranking in AI Data Analysis

10th

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Cloud Data Integration (10th)

Pinecone

Ranking in AI Data Analysis

14th

Average Rating

8.4

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Vector Databases (6th), AI Content Creation (3rd)

Mindshare comparison

As of January 2026, in the AI Data Analysis category, the mindshare of Matillion Data Productivity Cloud is 1.1%, down from 3.8% compared to the previous year. The mindshare of Pinecone is 0.6%, down from 6.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Data Analysis Market Share Distribution
Product	Market Share (%)
Matillion Data Productivity Cloud	1.1%
Pinecone	0.6%
Other	98.3%

AI Data Analysis

Featured Reviews

Jitendra Jena

Director Axtria - Ingenious Insights! at Axtria - Ingenious Insights

Easy integration and workflow proposals streamline processes

The predefined connectors eliminate the need to write code for connectivity. If you have a predefined connector, it is easy to use with plug and play functionality. The processing time and ease of use are significant benefits. As everyone is moving into AI integration, it will definitely help. When creating workflows, they can propose solutions directly.

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Pradeep Gudipati

Chief Technology Advisor at Kovaad technologies Pvt Ltd

Faced challenges with metadata filtering but have achieved reliable long-term memory for chat applications

We were looking at multiple options for a vector database, and we found Pinecone to be the easiest to integrate into our solution. Plus, it has a very generous free tier, which helps us as a startup. The best features Pinecone offers are quick setup and good indexing for us. The retrieval mechanisms are fast, and the integration with Python as with JavaScript and TypeScript libraries that Pinecone provides are very robust. Authentication is also very good. 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 our organization by enhancing efficiency for the team, and the long-term effect has been that the chats have become much more personalized due to the memory added through a vector database. We are seeing that the trainees getting trained on the platform are more satisfied with the results or messages generated by AI.

Read full review

Quotes from Members

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

Pros

"It can scale to a great extent. It can handle the load that we are putting on it, which is about 5TBs."

"The simplicity of this tool is nice. It has a good graphical user interface. You can also do a lot of generic stuff in the tool. If there is good connectivity to a cloud database, such as Snowflake, and you can have a lot of Snowflake functionality in the tool."

"Matillion ETL is one hundred percent stable."

"The most valuable feature of Matillion ETL is the ETL. The solution is open-source which provides advantages, such as good performance and high efficiency. Additionally, it supports three data types which eliminates predefining the data, and we can write script models in Python."

"It has helped us to get onto the cloud quickly."

"The tool's middle-dimensional structure significantly simplifies obtaining the right data at the appropriate level. This feature makes deploying our applications easier since we utilize a single source without publishing data from various sources."

"The product is quite stable and can handle complex data integration tasks well."

"It has good integrations with Amazon Redshift and other AWS services."

More Matillion Data Productivity Cloud pros

"Pinecone has positively impacted our organization by enhancing efficiency for the team, and the long-term effect has been that the chats have become much more personalized due to the memory added through a vector database."

"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."

"The best thing about Pinecone is its private local host feature. It displays all the maintenance parameters and lets us view the data sent to the database. We can also see the status of the CD and which application it corresponds to."

"The most valuable features of the solution are similarity search and maximal marginal relevance search for retrieval purposes."

"The most valuable feature of Pinecone is its managed service aspect. There are many vector databases available, but Pinecone stands out in the market. It is very flexible, allowing us to input any kind of data dimensions into the platform. This makes it easy to use for both technical and non-technical users."

"We chose Pinecone because it covers most of the use cases."

"The semantic search capability is very good."

"Pinecone's integration with AWS was seamless."

More Pinecone pros

Cons

"The current version is a bit more limited because it's on a virtual machine, and everything executes on that one virtual machine."

"One of the features that's in development is data privacy in the cloud, along with further SAP integration. For connectivity to SAP systems."

"While the UI is good, it could be improved in its efficiency and made easier to use."

"Our main challenge currently is that Matillion runs on an EC2 instance, limiting us to running only two processes simultaneously at the entry level."

"When using the SQL loader type there were not a lot of pre-processing features for the data. For example, if there is a table with twenty columns, but we only want to load ten columns. In that case, we can use a security script to select the specific columns needed. However, if we want to perform extensive pre-processing of the data, I faced some challenges with Matillion ETL. I did not encounter many challenges, but my overall experience is limited as I only have three years of experience."

"Going forward, I would like them to add custom jobs, since we still have to run these outside of Matillion."

"The cost of the solution is high and could be reduced."

"Sometimes, we have issues with the solution's stability and need to restart it for three weeks or more."

More Matillion Data Productivity Cloud cons

"For testing purposes, the product should offer support locally as it is one area where the tool has shortcomings."

"One major issue I have noticed with Pinecone is that it does not allow me to search based on metadata."

"Onboarding could be better and smoother."

"Pinecone is good as it is, but had it been on AWS infrastructure, we wouldn't experience some network lags because it's outside AWS."

"One major issue I have noticed with Pinecone is that it does not allow me to search based on metadata."

"Pinecone can be made more budget-friendly."

"I want to suggest that Pinecone requires a login and API key, but I would prefer not to have a login system and to use the environment directly."

"If Pinecone gave us RAG as a service, we'd be more than happy to use that."

More Pinecone cons

Pricing and Cost Advice

"It was procured through the AWS Marketplace because it keeps things simple. They offer retail-like checkout and bill through your existing Amazon Web Services account."

"The prices needs to be lower."

"I have heard from my manager and other higher ups, "This product is cheaper than other things on the market," and they have done the research."

"The solution is very cheap. You're paying $2.50 an hour and if you set your service up, which you can do, you're not getting charged. Currently, our ETL process is just an overnight process that runs for about an hour. I can start and stop my server just for an hour if I want to and spent $2.50 a day for an ETL solution. There are no additional costs."

"The solution's pricing is not based on the licensing cost but on the running hours when the Matillion instance is up and running."

"The cost of the solution is high and could be reduced."

"The price of Matillion ETL is reasonable."

"It is not necessarily a cheap solution. However, it's reasonable priced, especially with the smaller machines that we run it on."

More Matillion Data Productivity Cloud pricing and cost advice

"I think Pinecone is cheaper to use than other options I've explored. However, I also remember that they offer a paid version."

"Pinecone is not cheap; it's actually quite expensive. We find that using Pinecone can raise our budget significantly. On the other hand, using open-source options is more budget-friendly."

"I have experience with the tool's free version."

"The solution is relatively cheaper than other vector DBs in the market."

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Top Industries

By visitors reading reviews

Financial Services Firm

14%

Computer Software Company

11%

Manufacturing Company

11%

Retailer

Computer Software Company

14%

Manufacturing Company

Comms Service Provider

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	10
Large Enterprise	11

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	3

Questions from the Community

What do you like most about Matillion ETL?

The new version with the Productivity Cloud is very simple. It's easy to use, navigate, and understand.

What is your experience regarding pricing and costs for Matillion ETL?

The pricing is managed by the tooling team. The pricing is moderate, neither expensive nor cheap.

What needs improvement with Matillion ETL?

The main areas for improvement are AI features and scalability.

What do you like most about Pinecone?

We chose Pinecone because it covers most of the use cases.

What needs improvement with Pinecone?

I give Pinecone a nine out of ten because I hope it provides an end-to-end agentic solution, but currently, it doesn't have those agentic capabilities, meaning I have to create a Streamlit applicat...

What is your primary use case for Pinecone?

My main use case for Pinecone is creating vector indexes for GenAI applications. A specific example of how I use Pinecone in one of my projects is utilizing a RAG pipeline where I take text from PD...

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Product Reports

Matillion Data Productivity Cloud

January 2026

Matillion Data Productivity Cloud report

Download Matillion Data Productivity Cloud product report

Download Pinecone product report

January 2026

Also Known As

Matillion ETL for Redshift, Matillion ETL for Snowflake, Matillion ETL for BigQuery

No data available

Overview

Matillion Data Productivity Cloud features an intuitive graphical interface, seamless AWS integration, and efficient data management. Its tools streamline complex tasks for SFDC, RDS, Marketo, Facebook, and Google AdWords.

Matillion Data Productivity Cloud provides fast transformations with built-in verification, easy scheduling, and sampling. With automatic scalability and diverse data source support, it simplifies complex data tasks. Users benefit from cloud data warehousing and integrating data into Snowflake while appreciating its ease of use by non-technical teams. Enhancements can focus on frequent API adjustments, improved documentation, faster performance with less latency, and better error handling.

What are the key features of Matillion Data Productivity Cloud?

User-Friendly Interface: Simplifies interaction for non-technical users with a graphical interface.
Seamless Integration: Works smoothly with AWS, SFDC, RDS, Marketo, Facebook, and Google AdWords.
Automatic Scalability: Efficiently manages varying workloads with scale-up and down capabilities.
Efficient Data Handling: Streamlines transformations, scheduling, and sample data verifications.
Diverse Data Source Support: Easily integrates multiple data sources for streamlined processes.

What benefits and ROI should users seek in reviews?

Ease of Use: Reduces complexity for non-technical teams with intuitive operation.
Performance Efficiency: Fast data processing and transformations enhance productivity.
Cloud Compatibility: Supports integration with cloud platforms for versatile data solutions.
Time-Saving Scheduling: Automates data tasks, allowing users to focus on other priorities.
Scalability: Automatically adjusts to workload needs, optimizing resource use and cost.

In industries such as technology, finance, and healthcare, Matillion Data Productivity Cloud is implemented to streamline ETL processes, optimize data pipeline construction, and enhance data migration efforts. It supports efficient data loading and integration between cloud and on-premises databases, aiding industries in managing data-driven projects.

Matillion

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.

Sample Customers

Thrive Market, MarketBot, PWC, Axtria, Field Nation, GE, Superdry, Quantcast, Lightbox, EDF Energy, Finn Air, IPRO, Twist, Penn National Gaming Inc

1. Airbnb 2. DoorDash 3. Instacart 4. Lyft 5. Pinterest 6. Reddit 7. Slack 8. Snapchat 9. Spotify 10. TikTok 11. Twitter 12. Uber 13. Zoom 14. Adobe 15. Amazon 16. Apple 17. Facebook 18. Google 19. IBM 20. Microsoft 21. Netflix 22. Salesforce 23. Shopify 24. Square 25. Tesla 26. TikTok 27. Twitch 28. Uber Eats 29. WhatsApp 30. Yelp 31. Zillow 32. Zynga