# Pinecone Reviews **Vendor:** Pinecone Systems **Category:** [Vector Database Software](https://www.g2.com/categories/vector-database) **Average Rating:** 4.6/5.0 **Total Reviews:** 39 ## About Pinecone Pinecone is the developer-favorite and most trusted vector database for building accurate and performant AI applications at scale in production. Fully managed, easy to use, with the best cost/performance at scale. ## Pinecone Reviews ### 1. Effortless Integration and Fast Queries with Pincone **Rating:** 4.5/5.0 stars **Reviewed by:** Ranu S. | Software Developer, AI and ML Engineer., Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** December 11, 2025 **What do you like best about Pinecone?** The service is self-managed by Pincone, so there is no need for separate billing; it can be handled directly through your cloud service provider, such as the AWS Marketplace. Defining and creating a vector instance according to the dimensions and parameters of your embedding models is straightforward. I found it quite simple to integrate with both AWS Bedrock and GCP Vertex AI services. In my experience, querying data is faster compared to other services I have used so far. This service is in our daily use as a backbone for our AI services. **What do you dislike about Pinecone?** If you are using the trial version, you are required to create your instance in the US only. However, since I work in banking, this presents a compliance issue regarding data location. They should offer trial access in other countries as well, or consider implementing different limitations instead of restricting by region. **What problems is Pinecone solving and how is that benefiting you?** We needed to implement a vector database for our question-answer RAG system, as well as for generating Credit Access Memos. At first, we used AWS OpenSearch, but found it to be very expensive. To cut costs, we switched to the Pinecone vector database for storing our documents. ### 2. Low-Latency Similarity Search with Scalable, Developer-Friendly APIs **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** February 18, 2026 **What do you like best about Pinecone?** Pinecone stands out for its low-latency similarity search, managed scalability, and developer-friendly APIs. It removes much of the operational burden of running vector databases, making production-grade semantic search significantly easier. **What do you dislike about Pinecone?** Pinecone delivers excellent performance, but improved cost predictability, more granular configuration options, and greater transparency in scaling behavior would further enhance the developer experience. **What problems is Pinecone solving and how is that benefiting you?** Pinecone solves the challenge of storing and searching high-dimensional vector data efficiently, enabling fast and accurate semantic retrieval for AI applications. This allows me to build smarter search and RAG-based systems without managing complex database infrastructure, ultimately accelerating development and improving application relevance. ### 3. Nice vector db easy to use **Rating:** 4.0/5.0 stars **Reviewed by:** Husain B. | Software developer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** October 02, 2025 **What do you like best about Pinecone?** its provide various of features and great vector db support **What do you dislike about Pinecone?** may be it is close source and needed some features which are not there yet. **What problems is Pinecone solving and how is that benefiting you?** The latency is very minimal and provide large search/retrieval with fully managed serverless infrastructure ### 4. Pinecone: The Backbone of Efficient Vector Search and Retrieval **Rating:** 5.0/5.0 stars **Reviewed by:** Stephen C. | Owner & Co-Founder, Small-Business (50 or fewer emp.) **Reviewed Date:** August 22, 2024 **What do you like best about Pinecone?** Pinecone excels in providing a seamless, high-performance vector search experience. Its ease of use, combined with powerful features like real-time updates and scalability, makes it a go-to solution for managing complex vector data. The ability to effortlessly integrate with existing workflows and its top-notch customer support are definite highlights. **What do you dislike about Pinecone?** While Pinecone is robust, the pricing can be a bit steep for smaller projects or startups. Additionally, more granular control over indexing options would enhance customization for advanced users. However, the benefits far outweigh these minor drawbacks. **What problems is Pinecone solving and how is that benefiting you?** Pinecone is solving the complex challenge of efficient and scalable vector search. In an era where managing large volumes of high-dimensional data is critical, Pinecone's ability to index, search, and retrieve vectors quickly and accurately is a game-changer. For us, this means faster query responses, enhanced data retrieval accuracy, and the ability to focus on building better products rather than managing infrastructure. Pinecone's solution has drastically reduced the time and effort required to manage and search vector data, allowing our team to be more productive and innovative. ### 5. Using Pinecone on production - 1 year later **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Staffing and Recruiting | Small-Business (50 or fewer emp.) **Reviewed Date:** August 22, 2024 **What do you like best about Pinecone?** Pinecone was our primary choice and we have not considered changing since. - High performance (upsert and search in the ms) - Simple integration via API and deployment and now after their recent release of serverless indexes it's very simple to maintain and scale (it's autoscaling). - Low price (relative to the number of vectors) and free limited indexes. Free indexes are great to run development environment data. For a while it was impossible to upgrade a free index to a paying one, but this is now addressed. - Incredible support (we had an issue and was not expecting getting this quality of support without paying the usual business support fees of an AWS for example) - The ability to assign metadata is very useful (we still maintain a traditional db to keep track of the vectors) - The single stage query vector/metadata is very useful and saves the headache of over-querying - One feature we have meant to use is the use of sparse vectors in combination with the dense vectors. So, can't really comment yet **What do you dislike about Pinecone?** Love most of it as is - The documentation using metadata and single stage queries is a bit light - They have a smart bot to help answer support questions. On the great side, it seems they use their own technology for RAG type of application, but on the other it often misses the mark. ChatGPT or Perplexity are surprisingly more effective. - There has been a few down times, but they are very communicative about them and maintain a server health page for each endpoint. It's usually related to a specific infrastructure (AWS or GCP) they run on. - They have been growing and improving the technology, and like with other player, sometimes to update their python library or the way to reference to the indexes. But each time it's been toward simplification, and I suspect it will stabilize. **What problems is Pinecone solving and how is that benefiting you?** Semantic matching ### 6. A great serverless DBaaS for vectors **Rating:** 5.0/5.0 stars **Reviewed by:** Roland A. | Co-Founder, Small-Business (50 or fewer emp.) **Reviewed Date:** August 22, 2024 **What do you like best about Pinecone?** Pinecode offers a simple API and lean management interface for a completely low maintenance vector storage and query solution. **What do you dislike about Pinecone?** I started using Pinecone when it was new and had some rough edges. But support was proactive and smart. In the last year I can say there is nothing to not like. It has been awesome. **What problems is Pinecone solving and how is that benefiting you?** We use Pinecone's serverless platform (on AWS) for vector search. Our vector dimension is 3072. Part of our use is user queries. The performance has been excellent and scalability is automatic. We also use the query capability in other parts of our stack where performance is not so important but reliability is a factor. ### 7. Effortless Vector Storage to Give Your AI App Infinite Intelligence **Rating:** 5.0/5.0 stars **Reviewed by:** James R. H. | Story Consultant, Small-Business (50 or fewer emp.) **Reviewed Date:** March 27, 2024 **What do you like best about Pinecone?** Pinecone is great for super simple vector storage, and with the new serverless option the choice is really a no-brainer. I've been using them for over a year now in production, and their Sparse-Dense offering made a huge impact on the quality of retrieval (domain-heavy lexicon). The tutorials and content on site are both extremely well-thought out and presented, and the one or two times I reached out to support they cleared up my misunderstandings in a courteous and quick manner. But seriously, with serverless now, I'm able to offer insane features to users that were cost-prohibitive before. **What do you dislike about Pinecone?** I can tell you what used to be challenging: which was cost monitoring and the web interface, both issues which have been drastically improved in the recent months. The web interface is still a bit cumbersome to use, but that's only because vector storage/search is not what you would expect coming from other "content" management systems. There isn't a lot of hand-holding like you might find elsewhere, but really—if you're in this space, you do have to do a lot of work on your own anyways. Hard to find something to dislike when it "just works." **What problems is Pinecone solving and how is that benefiting you?** My app leverages decades of internal and external content around the business of writing great stories. Pinecone's vector database makes it easy to store all of this knowledge in a way that is easily and QUICKLY recovered based on semantic meaning. And now with serverless (and its wild affordability), I can now extend that knowledgebase to ALL of my user's stories and creations such that everyone can have their own expert assistant tailored to their particular style. ### 8. ideal for machine learning, AI applications and similarity search **Rating:** 5.0/5.0 stars **Reviewed by:** Mohit G. | Business Analysis Module Lead, Telecommunications, Mid-Market (51-1000 emp.) **Reviewed Date:** September 11, 2024 **What do you like best about Pinecone?** It is specialised in AI driven use cases with real time and low latency search giving seamless integration into machine learning workflows with scalable infrastruture optimized for unstructured and semi-structured data in AI applications. **What do you dislike about Pinecone?** It has limited focus that is related only with the vector data with no major focus on Business intelligence in data transformation tool. Also it's use case is little complex with lack of ecosystem integration. **What problems is Pinecone solving and how is that benefiting you?** It is solving the issue related with AI vector data generated from the app. ### 9. Solid option for vector DB **Rating:** 5.0/5.0 stars **Reviewed by:** Carlos O. | Data Scientist, Small-Business (50 or fewer emp.) **Reviewed Date:** August 28, 2024 **What do you like best about Pinecone?** Easy to use. very reliable and fast. Competitive price **What do you dislike about Pinecone?** Maybe some extra features would be nice, and some more clarity into its AKNN algo, which is hidden from the user **What problems is Pinecone solving and how is that benefiting you?** Finding scientific documents in very large volumes of Data. ### 10. Pinecone assistant beta user **Rating:** 5.0/5.0 stars **Reviewed by:** Satwik L. | Freelancer, Small-Business (50 or fewer emp.) **Reviewed Date:** September 10, 2024 **What do you like best about Pinecone?** I have been using pinecone for embeddings and it is cheaper and reliable compared to other embedding services. **What do you dislike about Pinecone?** I dislike the overall feel which feels lightweighed for the product service documentation. I love to see pinecone assistant in deployable version because it is powerful yet it is in the beta version only for testing not for production **What problems is Pinecone solving and how is that benefiting you?** Creating embeddings at ease without any big pricing. Good support from team. ### 11. God of creating embeddings **Rating:** 4.0/5.0 stars **Reviewed by:** Akhil G. | Freelancer, Small-Business (50 or fewer emp.) **Reviewed Date:** September 11, 2024 **What do you like best about Pinecone?** when iam creating embeddings,compared to other products,it feels hassle free& cheap. **What do you dislike about Pinecone?** I am the beta tester of pinecone AI assiatant,it is not production ready so it feels like only for testing,i am expecting for the production ready version. **What problems is Pinecone solving and how is that benefiting you?** hassle free functions and embeddings data sets ### 12. A fast service that allowed us to implement RAGs in a brink **Rating:** 4.5/5.0 stars **Reviewed by:** Alejandro S. | Software Engineering Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2024 **What do you like best about Pinecone?** I like their pace of innovation because they allowed us to start testing RAGs since the beginning and they have been enabling new use cases since. This is a team that grows with our platform and that keeps us up to date. **What do you dislike about Pinecone?** One thing we had to do is add additional destinations to our internal systems, and building the syncronization flows was the most difficult part of it. **What problems is Pinecone solving and how is that benefiting you?** Allows us to build semantic search and recommendation products. ### 13. Best and affordable vector database **Rating:** 5.0/5.0 stars **Reviewed by:** Alok K. | Co-Founder, Small-Business (50 or fewer emp.) **Reviewed Date:** April 22, 2024 **What do you like best about Pinecone?** Pinecone's new serverless pricing is very affordable for small startups. It support large embeddings size, sparse & dense embedding and fast queries. It suited my needs. **What do you dislike about Pinecone?** It has 10,000 namespace limit on serverless instance. It should be increased. **What problems is Pinecone solving and how is that benefiting you?** I use it to store embeddings of PDF files and then ask questions using LLM models. ### 14. Using Pinecone for Semantic Search **Rating:** 5.0/5.0 stars **Reviewed by:** Val J. | Small-Business (50 or fewer emp.) **Reviewed Date:** December 04, 2023 **What do you like best about Pinecone?** Pinecone made it easy for my team to significantly accelerate our AI services through vector search. While vector databases have become more commonplace, they continue to introduce new features to stay on the cutting edge and add support new applications. The service is easy to setup and maintain. Theirservice is faster and more stable than some open-source alternatives that we considered. **What do you dislike about Pinecone?** While Pinecone can be hosted on both GCP and AWS, it would be great if they also suppoted Azure. We have tested both and had the highest uptime when running PineCone on AWS. **What problems is Pinecone solving and how is that benefiting you?** We use PineCone to accelerate vector search and cachine for nearly all our AI services. It reduces both speed and cost by reducing the need to recompute embeddings, ### 15. I really like the product and satisfied from the ease-of-use and performance **Rating:** 4.5/5.0 stars **Reviewed by:** Itamar N. | CTO, Small-Business (50 or fewer emp.) **Reviewed Date:** March 27, 2024 **What do you like best about Pinecone?** I like the ease-of-use. Super easy to build index, populate with data and test it. **What do you dislike about Pinecone?** Some security-related features are missing. We need VPC peering in GCP, in order to unlock deals with companies that require this feature. Also, Serverless in GCP is missing. **What problems is Pinecone solving and how is that benefiting you?** Vector DB for multi-tenant system. ### 16. A Reliable and Consistent Performance **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** November 16, 2023 **What do you like best about Pinecone?** Pinecone has been a game-changer for our company, especially in the realm of vector embeddings. What stands out the most is its robust performance and reliability. Over the six months of our usage, we have not encountered any downtime, which is crucial for our operations. The consistency in performance has been remarkable, ensuring that our data-driven processes run smoothly and efficiently. Its seamless integration have made it an indispensable tool in our tech stack. **What do you dislike about Pinecone?** As of now, we haven't encountered any significant issues or drawbacks with Pinecone. It has met all our expectations and requirements efficiently. However, we are always on the lookout for new features and improvements that can further enhance our experience and capabilities with the platform. **What problems is Pinecone solving and how is that benefiting you?** Pinecone has been instrumental in efficiently managing vector embeddings, a critical component in our applications like similarity search and recommendation systems. Its scalability and consistent performance, coupled with zero downtime, have significantly improved our operational efficiency and user experience. By simplifying infrastructure management and enabling rapid integration, Pinecone has allowed us to focus on core business functions, accelerating development cycles and enhancing overall service quality. This reliability and efficiency have been key to maintaining high service levels and staying competitive in our market. ### 17. Efficient and user-friendly, Ideal for vector database newcomers **Rating:** 5.0/5.0 stars **Reviewed by:** Jimmie A. | Founder & CEO, Small-Business (50 or fewer emp.) **Reviewed Date:** November 15, 2023 **What do you like best about Pinecone?** I recently started using Pinecone and was impressed with how user-friendly it is, especially for someone new to vector databases. Its standout feature is its focus on doing one thing exceptionally well. The documentation is clear and easy to follow, making the setup process smooth. Both indexing and query times are impressively fast, which significantly enhances efficiency. I chose Pinecone over other options because it supports larger vector sizes, a key requirement for my needs. Highly recommend Pinecone for its simplicity, speed, and capabilities. **What do you dislike about Pinecone?** There are a couple of areas where Pinecone could improve. First, the options for datacenter hosting are limited. For instance, if using AWS, it currently only supports the us-east-1 region, which can be restrictive. Second, the console lacks robust security measures for critical actions. Adding a Multi-Factor Authentication (MFA) verification for deleting indexes and projects would enhance security and prevent accidental data loss. **What problems is Pinecone solving and how is that benefiting you?** Pinecone plays a crucial role in our workflow by efficiently storing vectors from OpenAI Embeddings. This capability allows us to effectively identify and link related content across various features of our platform. The result is a more cohesive and intuitive user experience, as we can seamlessly connect relevant information and offerings. This not only enhances our platform's functionality but also significantly improves user engagement and satisfaction. ### 18. fast and easy to setup vector database **Rating:** 4.5/5.0 stars **Reviewed by:** Cristian V. | Data Scientist, Small-Business (50 or fewer emp.) **Reviewed Date:** November 16, 2023 **What do you like best about Pinecone?** The things I mostly like are: - that is easy to set up by following the docs - fast for loading and updating embeddings in the index - easy to scale if needed **What do you dislike about Pinecone?** - that is not open source - I cannot query the full list of ids from an index (I needed to build a database and a script to track what products I have inside the index) - customer support by mail takes too much time **What problems is Pinecone solving and how is that benefiting you?** I built a deep learning model for product matching in the ecommerce industry. One of the steps for the system is to find candidates that are potential matches for the searched product. Becase of this, I needed a vector database to store the embeddings (texts and image) for the products for doing a similarity search as a first step of the product matching system. ### 19. Vector database that just works **Rating:** 5.0/5.0 stars **Reviewed by:** Aleksey S. | Backend Team Lead, Small-Business (50 or fewer emp.) **Reviewed Date:** November 15, 2023 **What do you like best about Pinecone?** We did a lot of research on vector databases at Refsee.com and tried many things: embedded db into the docker image served at AWS Lambda (believe me, that's not what you want), Milvus, Pinecone etc. We always had problems and necessity of extra tuning before, both with self-hosted OSS dbs and managed ones, but Pinecone really did the trick! It just works! **What do you dislike about Pinecone?** As usual, if you choose managed solution you get a vendor lock. Probably can be costly if you scale and no option for on-prem installation **What problems is Pinecone solving and how is that benefiting you?** We do vector search over our own datasets – basically a "google images" on our own data ### 20. First and Last Stop for a Vector Database **Rating:** 5.0/5.0 stars **Reviewed by:** Timothy N. | Small-Business (50 or fewer emp.) **Reviewed Date:** April 03, 2024 **What do you like best about Pinecone?** Excellent user interface, excellent supporting materials and literature to learn, very easy to use, improving quite quickly. It is quite easy to implement it in integration with our existing workflow. I use it for all vector database operations. **What do you dislike about Pinecone?** I have some very technical questions, like: will hybrid search ALWAYS be limited to dot product? But these are quite few. **What problems is Pinecone solving and how is that benefiting you?** Making it easy to implement a vector database for semantic search in RAG applications ### 21. Easy to use and powerful vector database **Rating:** 4.5/5.0 stars **Reviewed by:** Jiří N. | Visiting Lecturer at Faculty of Law, Small-Business (50 or fewer emp.) **Reviewed Date:** November 19, 2023 **What do you like best about Pinecone?** It is very easy to integrate the Pinecone API with a text generation application using LLM. Semantic search is very fast and allows more complex queries using metadata and namespace. I also like the comprehensive documentation. **What do you dislike about Pinecone?** For organizations that need only a little more capacity than is available in a single free pod, the pricing may be more favorable. **What problems is Pinecone solving and how is that benefiting you?** We use Pinecone as a vector database containing almost 150,000 of decisions of the Supreme Court of the Czech Republic and approximately 50 legal statutes. Pinecone serves as the backbone for the knowledge retrieval (RAG) of our legal research application. ### 22. I couldn't be more pleased **Rating:** 5.0/5.0 stars **Reviewed by:** Rich D. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 15, 2023 **What do you like best about Pinecone?** I have a Pinecone index that I've had to double in size three times now to handle the nearly 10 million vectors I have stored. Despite the increase in size, the search speed has remained constant, and upsert speed has actually increased. **What do you dislike about Pinecone?** This may not be unique to Pinecone, but you need to make sure you figure out your data schema up front because it requires some work to change records at scale if you want to add or modify metadata. **What problems is Pinecone solving and how is that benefiting you?** Fast speed and fully managed. I don't have to worry about anything other than paying the bill. ### 23. Easy and Dependable Vector Database **Rating:** 5.0/5.0 stars **Reviewed by:** Nikodem G. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 19, 2023 **What do you like best about Pinecone?** I really appreciate how Pinecone makes it easy to integrate vector search into applications. Its cloud-native setup and simple API mean I don't have to worry about infrastructure issues. Also, the performance is fantastic, even with massive amounts of data, and the low latency is a huge plus. **What do you dislike about Pinecone?** Being relatively new, it lacks some features and integrations compared to more established databases. And, there's a bit of a learning curve to fully leverage its capabilities. Additionally, there are some limitations regarding customization and exportability of vectors outside of Pinecone. **What problems is Pinecone solving and how is that benefiting you?** Semantic Search: Pinecone excels in understanding the context and meaning of queries, which is essential for accurately retrieving relevant information during meetings. Recommendation Systems: Its ability to handle complex data makes it suitable for suggesting relevant topics or actions based on the meeting's context. ### 24. Pinecone fails to give accuare results **Rating:** 1.5/5.0 stars **Reviewed by:** Alok K. | Internet, Small-Business (50 or fewer emp.) **Reviewed Date:** November 16, 2023 **What do you like best about Pinecone?** Pinecone is fast and fully managed. It also allows you to duplicate your index and create a new one. It was well suited for us. **What do you dislike about Pinecone?** It provides inaccurate search results even for simple semantic search. **What problems is Pinecone solving and how is that benefiting you?** We use it to build a conversational chatbot over users documents. A user can upload thousands of documents and we can build a chatbot for them using Pinecone. ### 25. A great option for Vector databases **Rating:** 4.0/5.0 stars **Reviewed by:** Ryan R. | Senior Application Development Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2024 **What do you like best about Pinecone?** The ease of use to get integrated with Pinecone was pretty incredible. We were up and running with a vector database in no time. **What do you dislike about Pinecone?** At first, the UI lacked some features that seemed like a must, but they've added a lot of what we were looking for and seem to be actively developing it. **What problems is Pinecone solving and how is that benefiting you?** To perform semantic search on our documents. ### 26. User and Developer-friendly Vector Database that has helped our company scale **Rating:** 5.0/5.0 stars **Reviewed by:** James Kwon L. | Founder, Small-Business (50 or fewer emp.) **Reviewed Date:** November 15, 2023 **What do you like best about Pinecone?** Pinecone has helped our company, fevr.io, scale our semantic chat functionality across three key regional markets. The responsiveness and ease of implementation has been a huge plus for our developers. The documentation has been very helpful as well, especially in terms of integrations with products like OpenAI and Langchain. Add to that, the customer support has been tremendously useful. **What do you dislike about Pinecone?** While not necessarily a negative feedback, having even more research data on how different dimensions and pods affect various responses would be a helpful resource to have as a reference. **What problems is Pinecone solving and how is that benefiting you?** Storing embeddings of documents is quite costly and difficult to manage. Pinecone solves this with solutions that are easy to implement with OpenAI's API. It allows for rapid prototyping of custom chat models. ### 27. quite good and easy to implement. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in E-Learning | Small-Business (50 or fewer emp.) **Reviewed Date:** March 28, 2024 **What do you like best about Pinecone?** it is good in search of similarity. also managing vectors. **What do you dislike about Pinecone?** i had difficulty to manage metadata for my vectors. **What problems is Pinecone solving and how is that benefiting you?** we are storing vetors pf our data into the pine cone. so previously we were using sql to store cobntents. now by using the pinecone we can easily extracts soimilar content throughout the applications. ### 28. User-friendly enterprise grade vector database **Rating:** 4.5/5.0 stars **Reviewed by:** Oscar B. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 16, 2023 **What do you like best about Pinecone?** We started using Pinecone pretty early on. I like the light UI on top of an API-first approach. We have been using it now for millions of daily queries, and it has rarely, if ever, gone down or giving us trouble. Highly recommended! **What do you dislike about Pinecone?** Not sure what to say here. It's been a good experience overall. If I had to say something, the pricing was tricky to groc. **What problems is Pinecone solving and how is that benefiting you?** Fast retrieval of multi-modal search queries ### 29. The fastest in production VectorDB yet **Rating:** 5.0/5.0 stars **Reviewed by:** Yash C. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 16, 2023 **What do you like best about Pinecone?** The speed. Hands down. QPS and the throughput is just the best in the industry. Easiest to get started with. Good support for parallel processing and batching. **What do you dislike about Pinecone?** Nothing, just could release more complex document related retrieval systems. **What problems is Pinecone solving and how is that benefiting you?** Semantic search is hands down a new way to search which is extremely efficient. Pinecone does a great job at not only providing the vector DBMS but giving the oppurtunity for scale. ### 30. GWI on Pinecone **Rating:** 4.0/5.0 stars **Reviewed by:** Archontellis Rafail S. | Mid-Market (51-1000 emp.) **Reviewed Date:** November 16, 2023 **What do you like best about Pinecone?** Easy of use and metadata filtering. Pinecone is one of the few products out there that is performant with a query that contains metadata filtering. **What do you dislike about Pinecone?** The pricing doesn't scale well for companies with millions of vectors, especially for p indexes. We experimented with pgvector to move our vectors in a postgres but the metadata filtering performance was not acceptable with the current indexes it supports. **What problems is Pinecone solving and how is that benefiting you?** Semantic search for now. ### 31. Production-ready vector database to get you started quickly **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** November 16, 2023 **What do you like best about Pinecone?** - Good documentation and usage examples - Easy-to-use Python SDK - Production-ready with low latency at our scale (10-20M vectors) - Good integration with the AI/LLM ecosystem **What do you dislike about Pinecone?** - did not find an easy way to export all vectors that we needed for data science/cleaning - will get expensive when hosting 100s of millions of vectors **What problems is Pinecone solving and how is that benefiting you?** We use Pinecone as a vector database for retrieval augmented generation using LLMs. ### 32. Ease to use and implementation **Rating:** 5.0/5.0 stars **Reviewed by:** Joseph Y. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 16, 2023 **What do you like best about Pinecone?** Quick to signup and implement and use it as daily basis. Performance is stable and very good. **What do you dislike about Pinecone?** I don't have anything bad about Pinecone. **What problems is Pinecone solving and how is that benefiting you?** We are building the RAG application. ### 33. Great dev experience **Rating:** 4.0/5.0 stars **Reviewed by:** Arda E. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 16, 2023 **What do you like best about Pinecone?** Easy to use Good documentation Easy to implement **What do you dislike about Pinecone?** Couldn't delete an entire vector within a namespace **What problems is Pinecone solving and how is that benefiting you?** Vector index storage provider. We store embedded indices on Pinecone. ### 34. One of the most convenient way for you to build a LLM-based Application **Rating:** 4.5/5.0 stars **Reviewed by:** wenbo j. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 20, 2023 **What do you like best about Pinecone?** You can deploy pinecone very fast without caring about the backend things like docker,storage etc. with an account you can directly building your app with the offical API and python code. **What do you dislike about Pinecone?** the price is relatively high comparing to some opensourced alternative. **What problems is Pinecone solving and how is that benefiting you?** We are building a LLM-based Application. Pinecone is the essential part of RAG solution. ### 35. Solid Hosted Vector DB **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** November 15, 2023 **What do you like best about Pinecone?** Ease of deployment! It takes just a few minutes to get an index set up and deployed. **What do you dislike about Pinecone?** The web-based API console could be improved, for example for experiments with metric (cosine vs dotproduct vs euclidean). **What problems is Pinecone solving and how is that benefiting you?** Storing embeddings for RAG. ### 36. Useful product for those who know what to do with it. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** November 15, 2023 **What do you like best about Pinecone?** It is a fast and efficient vector database. **What do you dislike about Pinecone?** The web-interface leaves many features to be desired. It is quite a bit on the pricier side. **What problems is Pinecone solving and how is that benefiting you?** We use it to hold educational material ### 37. Great vector database that has the necessary compliance **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Small-Business (50 or fewer emp.) **Reviewed Date:** November 15, 2023 **What do you like best about Pinecone?** It's very reliable, easy to set up and has both SOC 2 and HIPAA compliance. **What do you dislike about Pinecone?** No way to see the list of all the IDs in your collection. **What problems is Pinecone solving and how is that benefiting you?** Handling similarity searches ### 38. Best vector DB **Rating:** 5.0/5.0 stars **Reviewed by:** Rajan G. | Machine Learning Engineer II, Enterprise (> 1000 emp.) **Reviewed Date:** October 26, 2023 **What do you like best about Pinecone?** - Its retrieval process is good compared to other vector DB - We can visualize it in UI **What do you dislike about Pinecone?** It could have been open source which can make it easily usable with high demand. **What problems is Pinecone solving and how is that benefiting you?** Doc search and embedding storage and text retrival ### 39. Vector Database **Rating:** 5.0/5.0 stars **Reviewed by:** Prashanth D. | Lead Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 05, 2023 **What do you like best about Pinecone?** Pinecone used for indexing or searching of duplicate documents or similarity search score with our query. It helps to detect the anamolies easily. Mostly i liked this database to store my data as a vector form. **What do you dislike about Pinecone?** Pinecone premium subscription for various indexes and pods control. **What problems is Pinecone solving and how is that benefiting you?** Helps me to easily upsert vectorized data to pinecone vector Db. - [View Pinecone pricing details and edition comparison](https://www.g2.com/products/pinecone/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-14+02%3A36%3A21+-0500&secure%5Bsession_id%5D=f788f357-b52b-4bef-8311-0d25f538b1a7&secure%5Btoken%5D=32b997e7933c25b8eb0f802b118a2f084c46e4bec5abb2abd598229ad5fca8d2&format=llm_user) ## Pinecone Integrations - [AWS Bedrock](https://www.g2.com/products/aws-bedrock/reviews) - [Grok](https://www.g2.com/products/xai-grok/reviews) ## Pinecone Features **Data Indexing** - Semantic Search - Indexing Data **Retrieval intelligence - AI Search & Retrieval Infrastructure Platforms** - Advanced relevance tuning - Query understanding & expansion - Multistage retrieval & re-ranking - Context-aware & personalized search **Embedding & model management - AI Search & Retrieval Infrastructure Platforms** - Embedding versioning & lifecycle management - Multimodal search support - Pluggable embedding & LLM providers **Filters** - Accurate Search - Single Stage Filtering - Vector Database **LLM retrieval & RAG optimization - AI Search & Retrieval Infrastructure Platforms** - Retrieval pipeline orchestration - LLM-aware retrieval optimization - Hybrid retrieval strategy optimization **Data Enrichment & Index Intelligence - AI Search & Retrieval Infrastructure Platforms** - Incremental & streaming index updates - Built-in data enrichment **Security & governance - AI Search & Retrieval Infrastructure Platforms** - Fine-grained access controls - Data residency & retention policies - Audit logs & retrieval traceability **Operations, observability & reliability - AI Search & Retrieval Infrastructure Platforms** - Search analytics & relevance debugging - High availability & disaster recovery ## Top Pinecone Alternatives - [Weaviate](https://www.g2.com/products/weaviate/reviews) - 4.6/5.0 (29 reviews) - [Algolia](https://www.g2.com/products/algolia/reviews) - 4.5/5.0 (428 reviews) - [Supabase](https://www.g2.com/products/supabase-supabase/reviews) - 4.7/5.0 (41 reviews)