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.
4.4
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
This listing uses a single pay-as-you-go dimension called the Pinecone Billing Unit. You are metered on actual usage rather than a fixed subscription price. The Billing Unit shown here does not reflect the real cost or how usage is measured. Instead, your charges add up across the resources you consume, such as storage, write and read activity, data import, backup, and inference. Costs scale with how much you use each month. There are no upfront quantities to configure; you pay for what your workload consumes.
Top-of-mind questions for buyers
Your bill combines several usage metrics. These include storage per gigabyte, write units, read units, data import from object storage, backup storage, and restore. Inference usage such as embedding tokens, reranking requests, and assistant tokens also adds up. Each resource meters independently based on what your workload consumes each month.
It depends on your workload pattern. Read and write activity dominates for high-query search or recommendation systems. Storage charges outweigh query charges for large data footprints with low query rates. Inference tokens add up for embedding and reranking workloads. All these charges apply at once and combine on one invoice.
Storage charges continue for the data you keep, based on gigabytes stored per month. Read and write charges only accrue when you run queries or update data. Import, backup, and inference charges apply only when those actions occur. Idle workloads still incur storage cost but avoid query-based charges.
www.pinecone.io+1
Helpful?
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
Farhan A.
Pinecone Makes Vector Search Setup Effortless
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
What I like most about Pinecone is how easy it is to set up and use for vector search. It makes adding semantic search and AI-powered features to applications straightforward, without having to manage the underlying infrastructure myself. Pinecone integrates well with the rest of my stack, especially with AI frameworks and backend services. The APIs and SDKs make it straightforward to connect with my application and use it for storing and retrieving embeddings without much additional setup.
What do you dislike about the product?
The main thing I dislike about Pinecone is that it can become expensive as usage and data scale. For smaller prototypes, the free tier is useful, but the pricing can be harder to justify once you start handling larger amounts of data.
What problems is the product solving and how is that benefiting you?
Pinecone solves the complexity of storing and searching vector embeddings. It makes it easy to add semantic search and retrieval to AI applications without having to build and manage the vector database infrastructure myself for RAG. This lets me prototype and ship AI features much faster.
Parshav S.
Pinecone Makes Vector Search Easier to Manage
Reviewed on Aug 24, 2026
Review provided by G2
What do you like best about the product?
with pinecone what i feel is that it makes vector search more easier to manage compared with something like FAISS
What do you dislike about the product?
I feel like cost can be concern as one scales up, as for POC one can try with Pinecone but have to decide what payment tier to go and how to get the best payment option
What problems is the product solving and how is that benefiting you?
it mainly solves the problem of storing and searching the vector databased without managing infra and indexing option is there also
Juhi P.
Efficient Vector Search, But Scaling Costs
Reviewed on Aug 22, 2026
Review provided by G2
What do you like best about the product?
I mainly use Pinecone for vector search and retrieval in AI applications. It's useful for storing and searching embeddings, so we can quickly find relevant information and feed it into our AI workflows. I like that Pinecone takes a lot of the complexity out of vector search. Once it is set up, it is pretty straightforward to work with, and the search is fast enough that it works well in real-time AI applications. I also like that I don't have to worry too much about managing the infrastructure myself. The initial setup was pretty easy. Getting a basic index up and running did not take much time, and the documentation was helpful enough to get through the first setup. I would give Pinecone an 8 out of 10. It is reliable, easy enough to work with, and really useful if you're building AI apps that need fast semantic search or RAG.
What do you dislike about the product?
One thing I would improve is the cost, especially as usage and the amount of stored data start growing. It can also take a little time to figure out the right indexing and configuration for a specific use case. The basic experience is pretty smooth, but I would like more straightforward controls and visibility into performance and costs.
What problems is the product solving and how is that benefiting you?
I use Pinecone for vector search in AI apps, storing and searching embeddings efficiently, and it saves us from building search infrastructure. It makes large info searchable by meaning, easing semantic search and RAG solutions, while handling vast data well and maintaining response speed.
sophieraiin R.
Reliable and developer-friendly vector database for production AI applications
Reviewed on Aug 22, 2026
Review provided by G2
What do you like best about the product?
As a software engineer, I use Pinecone to store and search vector embeddings for AI powered applications. The setup is relatively straightforward, and the API is easy to integrate into an existing backend. I especially appreciate the speed and relevance of similarity searches, which makes it useful for retrieval augmented generation, semantic search, recommendation features, and document retrieval.
What do you dislike about the product?
The pricing can become difficult to predict as data volume, indexing activity, and query traffic increase. It is important to monitor usage carefully, especially for production applications with unpredictable workloads. Some advanced configuration and troubleshooting scenarios can also require a deeper understanding of vector search concepts and Pinecone’s architecture.
What problems is the product solving and how is that benefiting you?
Pinecone solves the problem of efficiently storing and searching high dimensional vector data. Instead of building and maintaining a custom similarity search system, I can use it to retrieve relevant documents or content based on meaning rather than exact keyword matches.this has helped me develop AI features more quickly and improve the relevance of search and retrieval results.
Caneel M.
Simple and reliable for working with vector search
Reviewed on Aug 22, 2026
Review provided by G2
What do you like best about the product?
What I like most is how straightforward it is to manage vector data. The dashboard makes it easy to see indexes, usage, and the overall project status without having to dig through a lot of settings. I also like having the database and assistant features available in the same place.
What do you dislike about the product?
The initial setup can take a little time if you are new to vector databases. Some of the concepts around indexes and configuration are not immediately obvious, so a bit more guidance for first-time users would be helpful.
What problems is the product solving and how is that benefiting you?
It makes it easier to store and search vector embeddings without having to build and manage the whole infrastructure myself. This is especially useful when working on AI features where I need to retrieve relevant information quickly