Sold by: Pinecone
Deployed on AWS
Free Trial
AWS Free Tier
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
Video
Pinecone Demo
Video
Getting started on AWS
Video
Product video
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
Subscribing through AWS Marketplace automatically upgrades your Pinecone organization to the Standard plan, designed for production applications at any scale. - Monthly minimum: $50/month applied toward usage
- Pay-as-you-go pricing after the minimum is met
- Usage credits apply to Database, Inference, and Assistant usage
Full pricing details and calculator: https://www.pinecone.io/pricing
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
Free trial
Try this product free according to the free trial terms set by the vendor.
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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
Pinecone Billing Unit | $0.01 |
Dimensions summary
Pinecone Vector Database uses a single dimension called "Pinecone Billing Unit" which represents their consumption-based pricing model. Based on Pinecone's documentation, this billing unit aggregates costs across different usage metrics including read units (RUs), write units (WUs), and storage for serverless indexes. Additional costs may apply for operations like data imports, backups, and AI model inference services.
Top-of-mind questions for buyers like you
What is a Pinecone Billing Unit and how is it calculated?
A Pinecone Billing Unit represents the aggregated consumption across different usage metrics including read operations (RUs), write operations (WUs), and storage for serverless indexes.
Is there a minimum usage commitment for Pinecone?
Yes, Pinecone requires a minimum usage commitment of $50/month for Standard plans and $500/month for Enterprise plans, with customers being charged only for actual usage if it exceeds these minimums.
How does Pinecone charge for different types of operations?
Pinecone charges based on the type of operation - read units for queries and fetches, write units for data modifications, storage costs per GB, and additional charges for specialized services like embedding and reranking models.
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.
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 to deliver relevant results across different query types.
Low-Latency Vector Retrieval
Achieves 20-100ms search latency on billion-vector datasets with real-time indexing and purpose-built Rust engine architecture.
Scalable Infrastructure Options
Supports elastic On-Demand scaling for variable traffic and Dedicated Read Nodes for provisioned read capacity with 99.9% uptime SLA.
Security and Compliance Certifications
SOC 2 Type II and HIPAA certified with security enforced at the data layer for enterprise deployments.
AWS Ecosystem Integration
Deep integration with Amazon Bedrock, SageMaker, and 50+ popular AI frameworks and data platforms through a unified API.
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
Muhammed A.
Fast, Hands-Off Serverless Vector Search That Scales Effortlessly
Reviewed on Aug 01, 2026
Review provided by G2
What do you like best about the product?
The fully managed, serverless architecture is the biggest win for us — we could go from having embeddings to a working semantic search feature in production without provisioning a single server or tuning any indexing parameters ourselves. Query latency has been consistently fast even as our vector count has grown, which matters for a RAG feature where retrieval speed directly affects how snappy the whole response feels to the end user. Scaling has been genuinely hands-off; we haven't had to think about resharding or capacity planning as our data volume increased, which freed up real engineering time that would have otherwise gone into managing infrastructure. The metadata filtering alongside vector search has also been useful — being able to combine semantic similarity with structured filters in a single query simplified what would otherwise have needed a separate filtering step in our application logic.
What do you dislike about the product?
Cost becomes a real consideration as usage scales — the serverless pricing model based on read/write units and storage is easy to reason about early on, but it adds up faster than expected once query volume grows, and it's worth comparing against self-hosted alternatives if budget is tight. There's no self-hosted option if you need full infrastructure control or have strict data residency requirements beyond what the managed bring-your-own-cloud option offers. Documentation is generally solid, but we ran into a bit of friction with SDK version differences early on, since some older tutorials online reference a syntax that's since been deprecated.
What problems is the product solving and how is that benefiting you?
Pinecone let us ship a RAG-based feature in our product without building or operating our own vector search infrastructure, which would have been a significant engineering investment for a small team. The combination of low-latency retrieval and hands-off scaling means our semantic search feature performs reliably in production without us needing to actively monitor or tune the underlying database, letting us focus engineering time on the application logic instead.
Muhammad O.
Fast and Reliable Vector Database for AI Projects
Reviewed on Aug 01, 2026
Review provided by G2
What do you like best about the product?
I like how easy Pinecone makes it to work with vector databases for AI projects. Creating an index and getting up and running is straightforward, and the interface feels clean and intuitive to navigate. It also integrates smoothly with modern AI tools and has been reliable in my experience, even when I’m working with embeddings and semantic search.
What do you dislike about the product?
The platform definitely has a learning curve if you’re new to vector databases. Some of the more advanced configuration options and parts of the documentation can feel pretty technical at first, so it takes a bit of time to figure out the best setup for different AI use cases. I’d also like to see more beginner-friendly tutorials and practical examples to help new users get up to speed.
What problems is the product solving and how is that benefiting you?
Pinecone helps us manage and search vector data efficiently, which boosts the performance of AI applications such as semantic search and RAG-based assistants. It takes a lot of the complexity out of working with embeddings and speeds up information retrieval, so we can build more responsive AI features while spending less time managing infrastructure.
aziz atilla y.
fast and reliable vector database for semantic search
Reviewed on Jul 31, 2026
Review provided by G2
What do you like best about the product?
pinecone is super easy to integrate into next.js and node projects for vector search. the serverless index option works really well and latency is impressive even with large semantic search datasets. it handles indexing and retrieval smoothly without having to manage heavy vector db infrastructure myself.
What do you dislike about the product?
pricing can get a bit high once index volume grows, and free index limitations are slightly restrictive during initial prototyping. also, filtering by complex metadata inside the dashboard UI could be a bit more user friendly.
What problems is the product solving and how is that benefiting you?
it simplifies creating full-text and semantic search systems for AI driven directories and content engines. saves a ton of time on database setup and maintenance, letting me focus on frontend integration and overall search quality.
Jeni J.
Effortless Vector Management with Rapid Semantic Search
Reviewed on Jul 30, 2026
Review provided by G2
What do you like best about the product?
I use Pinecone as a managed vector database to power AI applications that rely on semantic search and Retrieval-Augmented Generation. I like that Pinecone removes the operational complexity of running a vector database while delivering fast, reliable semantic search at scale. Since it's fully managed, I don't have to spend time handling infrastructure, scaling, or performance tuning, which lets me focus on building AI features instead. I also appreciate its consistently low-latency retrieval, which is essential for responsive RAG applications and AI assistants. Overall, Pinecone significantly reduces maintenance overhead and speeds up development, allowing me to focus on building AI applications instead of managing database infrastructure .the UI was very clean
What do you dislike about the product?
Pinecone is an excellent managed vector database, but there are a few areas where it could improve. Pricing can become expensive as datasets and query volumes grow, so more predictable pricing and cost optimization tools would be helpful for production workloads. I'd also like to see richer built-in monitoring and query analytics to better understand retrieval performance, latency, and index usage without relying heavily on external observability tools.
What problems is the product solving and how is that benefiting you?
Pinecone solves the challenge of storing and retrieving information from large datasets efficiently. It provides low-latency, accurate semantic search, and removes the complexity of managing vector database infrastructure, allowing me to focus on building AI applications.
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.