Sold by: Pinecone
Pinecone is a fully managed vector database that makes it easy to add vector search to production applications. The Pinecone Vector Database combines state-of-the-art vector search libraries, advanced features such as filtering, and distributed infrastructure to provide high performance and reliability at any scale. No more hassles of benchmarking and tuning algorithms or building and maintaining infrastructure for vector search.
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. The Standard tier starts at $25/month and includes $15/month of usage credits.
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. 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
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Dimension | Cost/unit |
|---|---|
Pinecone Billing Unit | $0.01 |
Vendor refund policy
Please contact support@pinecone.io
Custom pricing options
Request a private offer to receive a custom quote.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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.
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In Databases, Embeddings, Generative AI
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In ML Solutions, Databases & Analytics Platforms
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AI generated sentiment from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
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AI generated from product descriptions
Vector Search Capability
Advanced vector search technology for storing and searching through vector embeddings using AI representations of data
Distributed Infrastructure
Scalable infrastructure supporting high-performance vector search with distributed computing capabilities
Hybrid Search Functionality
Supports advanced search features including filtering, live index updates, and keyword boosting for enhanced search precision
Data Security Framework
Enterprise-grade security with SOC 2 Type II and HIPAA certification for protecting sensitive data
Retrieval Augmented Generation (RAG) Support
Specialized database design enabling context-aware information retrieval for generative AI applications
Vector Data Processing
Capability to process billions of vector data with high-speed performance
Embedding Generation
Support for converting unstructured data into vector embeddings using multiple model providers
Search Capabilities
Advanced search functionalities including sparse and dense embeddings, filtering, and range search techniques
Database Architecture
Fully-managed vector database built on open-source Milvus framework with distributed indexing capabilities
Model Integration
Seamless integration with vectorization models from multiple providers like OpenAI, Cohere, and HuggingFace
Multi-Model Database
Supports multiple data models including search, JSON, TimeSeries, Bloom filters, and Graph capabilities
Vector Database Technology
Enables semantic caching and vector management with in-memory data structure and real-time vector operations
Geo-Distributed Architecture
Provides Active-Active geo-replication with up to 99.999% uptime SLA and high availability across global infrastructure
Caching Mechanism
Utilizes advanced in-memory data store technology for offloading database reads and accelerating application performance
Scalability Framework
Supports deployment of multiple Redis instances on a single cluster node with flexible hybrid and multi-cloud implementations
Standard contract
No
No
Customer reviews
Vibhu
complicated set-up
Reviewed on Dec 09, 2024
Purchase verified by AWS
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.
Leave a comment1 comments
Mohit G.
ideal for machine learning, AI applications and similarity search
Reviewed on Sep 12, 2024
Review provided by G2
What do you like best about the product?
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 the product?
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.
Also it's use case is little complex with lack of ecosystem integration.
What problems is the product solving and how is that benefiting you?
It is solving the issue related with AI vector data generated from the app.
Akhil G.
God of creating embeddings
Reviewed on Sep 11, 2024
Review provided by G2
What do you like best about the product?
when iam creating embeddings,compared to other products,it feels hassle free& cheap.
What do you dislike about the product?
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 the product solving and how is that benefiting you?
hassle free functions and embeddings data sets
Satwik L.
Pinecone assistant beta user
Reviewed on Sep 10, 2024
Review provided by G2
What do you like best about the product?
I have been using pinecone for embeddings and it is cheaper and reliable compared to other embedding services.
What do you dislike about the product?
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
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 the product solving and how is that benefiting you?
Creating embeddings at ease without any big pricing.
Good support from team.
Good support from team.
Carlos O.
Solid option for vector DB
Reviewed on Aug 28, 2024
Review provided by G2
What do you like best about the product?
Easy to use. very reliable and fast. Competitive price
What do you dislike about the product?
Maybe some extra features would be nice, and some more clarity into its AKNN algo, which is hidden from the user
What problems is the product solving and how is that benefiting you?
Finding scientific documents in very large volumes of Data.