We performed a comparison between Elastic Search and Pinecone based on real PeerSpot user reviews.
Find out in this report how the two Vector Databases solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The forced merge and forced resonate features reduce the data size increasing reliability."
"It's a stable solution and we have not had any issues."
"ELK Elasticsearch is 100% scalable as scalability is built into the design"
"It provides deep visibility into your cloud and distributed applications, from microservices to serverless architectures. It quickly identifies and resolves the root causes of issues, like gaining visibility into all the cloud-based and on-prem applications."
"A good use case is saving metadata of your systems for data cataloging. Various systems, like those opened in metadata and similar applications, use Elasticsearch to store their text data."
"The product is scalable with good performance."
"It is stable."
"I am impressed with the product's Logstash. The tool is fast and customizable. You can build beautiful dashboards with it. It is useful and reliable."
"We chose Pinecone because it covers most of the use cases."
"The semantic search capability is very good."
"The most valuable features of the solution are similarity search and maximal marginal relevance search for retrieval purposes."
"The product's setup phase was easy."
"Elastic Enterprise Search could improve its SSL integration easier. We should not need to go to the back-end servers to do configuration, we should be able to do it on the GUI."
"The solution must provide AI integrations."
"There is another solution I'm testing which has a 500 record limit when you do a search on Elastic Enterprise Search. That's the only area in which I'm not sure whether it's a limitation on our end in terms of knowledge or a technical limitation from Elastic Enterprise Search. There is another solution we are looking at that rides on Elastic Enterprise Search. And the limit is for any sort of records that you're doing or data analysis you're trying to do, you can only extract 500 records at a time. I know the open-source nature has a lot of limitations, Otherwise, Elastic Enterprise Search is a fantastic solution and I'd recommend it to anyone."
"They could improve some of the platform's infrastructure management capabilities."
"It should be easier to use. It has been getting better because many functions are pre-defined, but it still needs improvement."
"This product could be improved with additional security, and the addition of support for machine learning devices."
"Elastic Enterprise Search could improve the report templates."
"Could have more open source tools and testing."
"For testing purposes, the product should offer support locally as it is one area where the tool has shortcomings."
"The tool does not confirm whether a file is deleted or not."
"Onboarding could be better and smoother."
"The product fails to offer a serverless type of storage capacity."
Elastic Search is ranked 1st in Vector Databases with 59 reviews while Pinecone is ranked 6th in Vector Databases with 4 reviews. Elastic Search is rated 8.2, while Pinecone is rated 8.0. The top reviewer of Elastic Search writes "Played a crucial role in enhancing our cybersecurity efforts ". On the other hand, the top reviewer of Pinecone writes "Helps retrieve data, relatively cheaper, and provides useful documentation". Elastic Search is most compared with Faiss, Milvus, Azure Search, Amazon Kendra and Qdrant, whereas Pinecone is most compared with OpenSearch, Faiss, Qdrant, Redis and LanceDB. See our Elastic Search vs. Pinecone report.
See our list of best Vector Databases vendors.
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