Sold by: Pinecone
Deployed on AWS
Only for accepting private offers. Pinecone is a serverless vector database built to power production AI on AWS. It delivers fast, accurate retrieval with hybrid search, reranking, filtering, and real-time indexing - no infrastructure or tuning required. Purpose-built for scale, Pinecone handles billions of vectors with low latency and high reliability. Teams use Pinecone to power agents, semantic search, recommendations, and RAG pipelines without managing infrastructure or stitching together open-source tooling. With fully managed operations and predictable performance, developers can focus on building intelligent applications instead of operating vector infrastructure.
4.5
Overview
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Pinecone Demo
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Getting started on AWS
Pinecone's fully managed, serverless vector database makes it easy to build accurate AI applications in production. By combining hybrid search (semantic + keyword), integrated reranking, hosted embedding and inference models, and real-time indexing, Pinecone delivers fast, relevant results at any scale, from prototype to billions of vectors.
Vector workloads aren't one-size-fits-all. From bursty RAG pipelines to high-throughput, latency-sensitive search and recommendation systems, Pinecone supports a full range of production use cases on a single platform.
- On-Demand provides elastic, usage-based scaling for variable traffic
- Dedicated Read Nodes (DRN) provide provisioned read capacity for predictable latency and sustained throughput .
Together, On-Demand and DRN let you optimize price-performance for each workload without managing multiple systems.
Pinecone integrates deeply with the AWS ecosystem, including services like Amazon Bedrock and SageMaker, while also supporting the most popular AI frameworks and data platforms. Developers use Pinecone to power agents, semantic search, recommendations, and RAG pipelines through a simple, intuitive API.
No infrastructure to manage, no algorithms to tune - just the performance, security, and reliability production AI demands.
Billing
This listing is intended for customers purchasing Pinecone through a private offer with an annual commitment. Annual commitments provide volume-based pricing and additional commercial benefits based on your usage level.
To get started, please contact your Pinecone sales representative or visit https://www.pinecone.io/contact/ to discuss custom pricing and terms before subscribing through this page.
If you prefer to start without an annual commitment, use Pinecone's Pay As You Go product listing.
Note: The "Pinecone Billing Unit" displayed below is an AWS Marketplace requirement and does not reflect Pinecone's actual pricing model or metering.
Highlights
- Accurate, production-ready retrieval: Pinecone delivers low-latency search (20-100ms) on billion-vector datasets with hybrid search (semantic + keyword), integrated reranking, and real-time indexing. Built on a purpose-built Rust engine and serverless architecture, optimized for production AI, not just vector storage.
- Ship faster with predictable cost and scale: Go from prototype to production in days, not months. Fully managed serverless architecture with decoupled storage and compute and no infrastructure to manage. Scales from thousands to billions of vectors with On-Demand or Dedicated Read Nodes and a 99.9% uptime SLA.
- Enterprise-ready with a rich ecosystem: SOC 2 Type II and HIPAA certified with security enforced at the data layer. 50+ integrations with the most popular AI and data tools, including deep support across the AWS ecosystem.
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Buyer guide
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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 the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months | Overage cost |
|---|---|---|---|
Commit | Total Commitment Value | $100,000.00 |
Dimensions summary
The "Commit" dimension on AWS Marketplace represents an annual financial commitment to Pinecone's vector database service, offering volume-based discounts compared to pay-as-you-go pricing. While the specific discount structure requires discussion with Pinecone's sales team, the commitment covers the same core pricing components: serverless compute usage (measured in Read, Write, and Store Units), and data transfer costs. Customers should contact Pinecone directly to determine their optimal commitment level based on expected usage patterns before proceeding with the annual subscription through AWS Marketplace.
Top-of-mind questions for buyers like you
What is the annual commitment option on AWS Marketplace, and how does it differ from pay-as-you-go?
The annual commitment option allows customers to receive volume-based discounts by committing to a predetermined spending level for one year. The exact discount structure is customized based on expected usage patterns and requires discussion with Pinecone's sales team before purchasing through AWS Marketplace.
How is billing calculated under the annual commitment plan?
Usage is still measured and billed monthly based on actual consumption of serverless compute (Read, Write, and Storage Units), and data transfer. The annual commitment establishes a minimum spending threshold that provides access to discounted rates compared to standard pay-as-you-go pricing.
What should I do before purchasing the annual commitment plan on AWS Marketplace?
You should contact Pinecone's sales team through their website to discuss your expected usage patterns and receive a customized quote with volume-based discounts. This consultation will help determine the appropriate commitment level and ensure you understand the potential cost savings compared to pay-as-you-go pricing.
Vendor refund policy
Please contact us at support@pinecone.io
Custom pricing options
Request a private offer to receive a custom quote.
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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
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Support
Vendor support
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. support@pinecone.io support@pinecone.io
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.
Updated weekly
By Pinecone
By Qdrant
By Weaviate
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
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AI generated from product descriptions
Hybrid Search Capabilities
Combines semantic and keyword search with integrated reranking and real-time indexing for accurate retrieval across billion-vector datasets
Low-Latency Performance
Delivers search latency of 20-100ms on billion-vector datasets built on a purpose-built Rust engine and serverless architecture
Scalable Architecture
Supports elastic scaling from thousands to billions of vectors through On-Demand usage-based scaling and Dedicated Read Nodes for provisioned read capacity
Security and Compliance
SOC 2 Type II and HIPAA certified with security enforced at the data layer
Ecosystem Integration
Provides 50+ integrations with popular AI and data tools including deep support for Amazon Bedrock, SageMaker, and major AI frameworks
Vector Search Engine
High-performance vector search engine for storing, searching, and managing vector embeddings with production-ready service capabilities
Advanced Filtering Support
Extended filtering capabilities on additional metadata fields that can be stored as payload along with vector embeddings
Flexible Storage Options
Multiple storage configuration options to support various deployment and scalability requirements
API Interface
Convenient API for storing, searching, and managing vectors with payload support
Unstructured Data Processing
Support for neural network encoders and embeddings to enable matching, searching, and recommendation applications on unstructured data
Vector Similarity Search
End-to-end vector database supporting vector similarity search, hybrid search, and advanced filtered search capabilities.
Multimodal Data Support
Out-of-the-box support for multimodal media types including text, images, and other data formats.
Structured Filtering
Ability to seamlessly combine vector search with structured filtering for refined query results.
Cloud-Native Architecture
Fault-tolerant cloud-native database architecture with low-latency performance characteristics.
Multi-Language Client Support
Accessible through a variety of client-side programming languages for flexible integration.
Standard contract
No
No
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Customer reviews
Internet
Fast, Scalable Managed Vector Database for Production-Ready Semantic Search
Reviewed on Jul 29, 2026
Review provided by G2
What do you like best about the product?
It offers a fully managed vector database that makes it easy to build AI-powered semantic search and retrieval applications in a simple, scalable way. With fast query performance, high availability, automatic scaling, and a straightforward API, developers can deploy production-ready RAG (Retrieval-Augmented Generation) and recommendation systems without having to worry about infrastructure management.
What do you dislike about the product?
The platform is generally easy to use, but managing large-scale indexes can become expensive as data volumes grow. Some of the more advanced filtering and indexing configurations also require a deeper understanding of vector search concepts. More built-in monitoring and debugging tools would make it easier to optimise and troubleshoot as usage scales.
What problems is the product solving and how is that benefiting you?
Pinecone enables efficient storage and retrieval of vector embeddings, which makes semantic search, recommendation engines, and AI assistants more accurate and responsive. It removes much of the complexity involved in managing vector database infrastructure, helps reduce development time, and lets teams build scalable AI applications that deliver faster, more relevant search results.
Gissell P.
Pinecone Scales Our AI Chatbot Knowledge Base with a Consistent Experience
Reviewed on Jul 28, 2026
Review provided by G2
What do you like best about the product?
We use Pinecone for our custom AI chatbot, and it has helped us deliver a broader, more scalable range of information through the bot. Overall, it’s made it easier for us to support a larger knowledge base and provide a more consistent chatbot experience.
What do you dislike about the product?
We’re planning to release our chatbot in the fall, and we’re a bit nervous about the linear pricing scale. If usage ends up being high, we don’t want to be forced to rebuild or rework things just because it’s being used a lot.
What problems is the product solving and how is that benefiting you?
Compared to other platforms on the market, Pinecone fits what we were looking for. We have a large database, and this app can store it and match it to the right keywords.
Jayanth C.
PineCone Supercharges RAG with Easy API Integration and Better LLM Context
Reviewed on Jul 27, 2026
Review provided by G2
What do you like best about the product?
PineCone is very useful for my cloud-native vector database. It’s helpful in RAG projects, and it supports different plans with subscription options. It really improves the LLM during the process of retrieval-augmented generation. We can integrate it simply by using API keys and the provided tools.It increase my performance by giving context to the LLM. Simple registration steps to onboarding into the platform
What do you dislike about the product?
It covers almost all the necessary things you’d want in the context of an LLM. However, I’m still not sure about the security aspect. Also, there’s less control over low-level index configurations and algorithms compared to open-source tools.
What problems is the product solving and how is that benefiting you?
It’s been really helpful for me in my projects, especially for RAGs and agent context storage and retrieval using an index.
Marketing and Advertising
Pinecone Makes GTM Automations Easy with Powerful Retrieval and a Modern UI
Reviewed on Jul 14, 2026
Review provided by G2
What do you like best about the product?
Pinecone is one of the best vector databases I’ve used for GTM automations. It works as a serverless database, and the keyword-based retrieval is what I like most. The API also lets me connect it with n8n, which helps me build AI projects with the right context. On top of that, the UI feels modern and is easy to use.
What do you dislike about the product?
We’ve run into a few issues around self-hosting, since the platform doesn’t allow it. For larger projects, the cost is also quite high, which reduces our ROI. On top of that, support from their team can be a bit slow.
What problems is the product solving and how is that benefiting you?
We use Pipecone to build AI agents inside n8n, and it helps us search for and retrieve documents or data for our AI models. This improves the performance of our AI automations and makes it easier to find the information we need.
Information Technology and Services
PineCone Makes Local RAG Prototyping Fast and Effortless
Reviewed on Jul 07, 2026
Review provided by G2
What do you like best about the product?
I use PineCone for quick prototyping on my local machine. It’s an easy way to get started with a RAG pipeline. It’s serverless, and I can create multiple namespaces while using the free tier.
What do you dislike about the product?
The pricing is a little confusing. It’s hard to convince clients because the cost calculation feels overly complex. I also wish it offered self-hosting, due to privacy and data sovereignty concerns.
What problems is the product solving and how is that benefiting you?
I use it to implement RAG pipelines, and I can’t use a standard RDBMS to store embeddings for my AI applications.