Overview
Pinecone serverless is the most popular vector database, used by engineering teams to solve two of the biggest challenges in deploying GenAI solutions - data security and hallucinations - by allowing them to store, search, and find the most relevant information from company data and send only that context to Large Language Models (LLMs) with every query.
This workflow is called Retrieval Augmented Generation (RAG), and with the Pinecone vector database, it aids in providing relevant, accurate, and fast responses from search or GenAI applications to end users.
Vector databases are purpose-built for storing and searching through vector embeddings, AI representations of data. This method of information retrieval (IR) is called vector search. Vector search is the new standard for finding the most relevant data for GenAI applications or any kind of search application.
Pinecone Vector Database Usage-based Billing: Charges are calculated by pod price multiplied by pod count. Invoices reflect total index runtime, rounded to 15-minute increments.
Learn more about pricing at pinecone.io/pricing
Highlights
- The Pinecone serverless vector database is the developer-favorite vector database that is easy to use at any scale, with a large user community. Fully managed vector database with intuitive API, console, and SDKs.
- The Pinecone serverless vector database provides best-in-class performance with 50x lower cost at any scale. Pinecone delivers fast vector search with filtering, live index updates, and keyword boosting (hybrid search).
- Pinecone is the most popular vector database for AI search, recommenders, and Retrieval Augmented Generation (RAG) applications. <br><br> Enterprise-grade security and compliance: SOC 2 Type II and HIPAA certified and built to keep data from your Vector Database secure
Details
Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Cost/unit |
|---|---|
Pinecone Billing Unit | $0.01 |
Vendor refund policy
Please contact support@pinecone.io
Custom pricing options
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Support
Vendor support
After creating your organization through the AWS Marketplace and signing into Pinecone, you may need to switch to your new organization. You can do so via the Switch Organization toggle in the left-side panel of the Pinecone console, directly above Settings.
After accessing your organization, you must create a new project if you wish to create non-starter indexes (docs.pinecone.io/docs/create-project).
If your AWS organization already has a subscription, please request an organization admin to invite you via the Pinecone console. You do not need to create a new Pinecone organization to join your team.
This is a fully managed service with technical support included with Standard and Enterprise plans. For more information regarding support SLAs, please see each plan's details on the pricing page (pinecone.io/pricing).
https://docs.pinecone.io/troubleshooting/how-to-work-with-support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Functionality
Ease of use
Customer service
Cost effectiveness
Standard contract
Customer reviews
complicated set-up
When I tried to use the Pinecone standard plan connected with AWS Marketplace, the setup process looped between Pinecone and AWS Marketplace. I am unable to start a standard plan. It still showing current plan as starter eventhough the pinecone documents says AWS Marketplace don't support it.
Apart from the chatbot, there is no help from the pinecone side. There has been no response to my sales query also.
Very easy to use, minimal setup effort required
I decided to use Pinecone DB as the vector database for Amazon Bedrock Knowledge Bases. My application required that I use Retrieval Augmented Generation (RAG) to answer questions about PDF business documents that I have stored in an Amazon S3 bucket. Pinecone DB is incredibly high performance and also offers a free tier, along with centralized billing through AWS Marketplace. I would highly recommend using Pinecone DB!
Great Results!
We recently made the switch to Pinecone database for our vector search needs, and we couldn't be happier with the results! The latencies are lower than we expected, making it a fast and reliable solution for us. Additionally, the metadata filtering works out of the box which is crucial for e-commerce. Overall, we highly recommend Pinecone to anyone in need of an efficient and user-friendly vector search solution.
Great Vector Database
Pinecone is a great service for anyone who needs fast and accurate vector search for their applications. I have been using Pinecone for a few months now and I am very impressed by its features and performance.
It is easy to manage multiple user accounts with Pinecone. I can invite my team members to join my projects and assign them different roles and permissions. I can also monitor their activities and usage through the web console.
It is good to have a support experience with Pinecone. They have a friendly and responsive team that is always ready to help me with any issues or questions. They also have a comprehensive documentation and a community forum where I can find answers and tips.
Fast & Reliable
The Pinecone pay as you go marketplace offering made it easy for us to quickly get up and running to do our evaluation of the tool. The pricing link to their website in the marketplace description is easy to understand and accurate based on our usage of the tool.