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.
4.4
Overview
Video
Pinecone Demo
Video
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.
Details
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Trust Center
Access real-time vendor security and compliance information through their Trust Center powered by Drata or Vanta. Review certifications and security standards before purchase.
Buyer guide
Gain valuable insights from real users who purchased this product, powered by PeerSpot.
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
This listing uses a single pricing dimension: a Total Commitment Value billed as an annual contract. You agree to a set spending amount upfront for the term. Your actual usage of the vector database draws down against that commitment. Usage covers activity like storage, write and read operations, and related services, billed by the units defined for each. Larger commitment amounts unlock associated discounts and support benefits. Because pricing is committed rather than pay-as-you-go per unit, you get predictable annual spend while retaining flexibility in how you consume the underlying services.
Top-of-mind questions for buyers
Your commitment covers vector database activity billed by defined units. This includes storage per gigabyte, write and read units, backup and restore per gigabyte, and object storage imports. Inference services like embedding and reranking, plus assistant token usage, also draw down. All metered usage counts against the committed amount.
It depends on your workload. Storage charges per gigabyte per month dominate for large, low-traffic datasets. Write and read units drive cost for high-throughput or high-query applications. Inference and assistant token usage add on top. All these charges accrue simultaneously and draw from the same committed amount.
The commitment is a set spending amount you agree to upfront. Once your metered usage draws down the full committed value, additional usage is billed separately. To adjust your committed spending, you contact the vendor directly, since annual commitments are arranged with their team.
www.pinecone.io+1
Helpful?
Vendor refund policy
Please contact us at 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
Vendor resources
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
Mixed reviews
Negative reviews
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
No
Customer reviews
Rehan A.
Pinecone Makes Semantic Search Straightforward and Infrastructure-Free
Reviewed on Aug 17, 2026
Review provided by G2
What do you like best about the product?
I use Pinecone for storing and searching vector embeddings in AI projects. I like that it makes semantic search straightforward and handles vector retrieval without requiring me to manage the database infrastructure myself.
What do you dislike about the product?
There is a learning curve when working with embeddings and indexing for the first time. Costs can also increase as the amount of stored data and query volume grows.
What problems is the product solving and how is that benefiting you?
Pinecone helps me add semantic search and retrieval to AI applications without building a vector database from scratch. It saves development time and makes it easier to retrieve relevant information for RAG-based workflows.
Mayank J.
Efficient Semantic Search with Easy Integration
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
I really like Pinecone for its combination of straightforward UI, strong performance, and ease of integration. The interface is easy to use when managing and searching vector data, and the performance remains reliable even as the volume of candidate profiles and job requirements grows. Faster response times are especially valuable for real-time candidate matching, where recruiters need results quickly. Pinecone's simple UI makes it easy to manage, while its fast performance helps us quickly search and match candidates at scale. The easy integrations with AI workflows also save development time and make the overall staffing process more efficient. The biggest benefit is how easily Pinecone integrates with our existing AI and search workflows, allowing us to connect resume data, candidate profiles, skills, and job requirements to build semantic candidate matching.
What do you dislike about the product?
One area that could be improved is making the UI even more intuitive for users who aren't deeply technical. More detailed monitoring and troubleshooting tools would also be more helpful. From a staffing perspective, additional integrations and easier management of large datasets could make it even more convenient as our AI workflows scale. I'd suggest a more intuitive dashboard with clearer navigation, simpler terminology, and more visual insights into indexes, usage, and performance. Guided setup, helpful tooltips, and built-in examples would make it easier for non-technical staffing users to understand and manage Pinecone without relying heavily on developers.
What problems is the product solving and how is that benefiting you?
I use Pinecone to quickly find the right candidates from large volumes of resumes in the US Staffing industry. It enhances semantic matching, reduces reliance on keywords, and speeds up submissions by connecting profiles and requirements efficiently.
srishti g.
Effortless Semantic Search with Intuitive UI
Reviewed on Aug 13, 2026
Review provided by G2
What do you like best about the product?
I use Pinecone as a vector database for storing and searching embeddings in AI applications, and it makes semantic search and retrieval fast and scalable, which is especially useful for building RAG-based systems and AI-powered search experiences. I appreciate Pinecone's combination of strong performance and a clean, intuitive UI/UX. The dashboard makes it easy to monitor indexes, manage data, and understand what's happening without unnecessary complexity. The APIs are straightforward, setup is smooth, and the overall experience feels polished and developer-friendly. The dashboard gives me a clear overview of indexes, usage, and performance, making it easier to monitor everything in one place. Integrating vector search into AI applications is quick and flexible with Pinecone. It reduces development overhead and feels reliable and easy to manage. Moving to Pinecone from a basic vector search setup gave us better scalability, performance, and a smoother developer experience. Also, the initial setup was quite easy, straightforward, and pretty good.
What do you dislike about the product?
I think one area for improvement is making the dashboard more intuitive for new users. Some advanced features and settings could be easier to discover and understand. I feel that more detailed documentation, clearer usage insights, and simpler configuration options would enhance the overall experience. Better onboarding would make Pinecone easier for new users, with a guided setup, clearer explanations, and practical examples. The dashboard could offer more actionable insights into performance, costs, and index health instead of requiring me to dig through different sections. Improved search, filtering, and clearer configuration options would make day-to-day management faster and more intuitive.
What problems is the product solving and how is that benefiting you?
I use Pinecone for storing and searching embeddings, solving the challenge of managing large vector data. It makes semantic search fast, reduces infrastructure complexity, and scales with data growth.
Nirmal K.
Pinecone’s Hands-Off Serverless Scaling for Billions of Embeddings
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
Unlike many open-source vector databases that require you to provision and manage your own Kubernetes clusters, Pinecone is completely hands-off. Its serverless architecture automatically scales up to handle billions of embeddings and scales down to zero when not in use, removing all infrastructure management overhead.
What do you dislike about the product?
You cannot self-host it on your own bare-metal servers or run a local version for offline development, which is a dealbreaker for teams with strict data-residency requirements.
What problems is the product solving and how is that benefiting you?
Pinecone serves as the backbone for many AI applications because it features plug-and-play integrations with top-tier orchestration frameworks (like LangChain and LlamaIndex) and LLM providers (like OpenAI, Cohere, and Anthropic).
Andrew T.
Amazing Fully Hosted Vector Database with No Setup Hassles
Reviewed on Aug 11, 2026
Review provided by G2
What do you like best about the product?
Vector database is amazing. It saves a lot of trouble for people who are just getting into this type of database. No hassles and no need to set up the environment locally with a fully hosted database.
What do you dislike about the product?
Need to have some more compatibility for traditional databases or non-relational databases. I didn't find features that would allow you to have both vector and conventional databases, as some providers do.
What problems is the product solving and how is that benefiting you?
I'm using it to set up this Retrieval-Augmented Generation with LangChain and Ollama. So far it's been working fine.