Sold by: Pinecone
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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.
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Overview
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Pinecone Demo
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Getting started on AWS
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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.
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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
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.
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Vendor refund policy
Please contact support@pinecone.io
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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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By Pinecone
By Qdrant
By Weaviate
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
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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
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.
LOKESH G.
Pinecone Makes Scalable Semantic Search and RAG Simple
Reviewed on Aug 08, 2026
Review provided by G2
What do you like best about the product?
I like Pinecone for its ease of use, fast vector search, and straightforward integration with AI applications. It makes it simple to build scalable semantic search and RAG workflows without needing to manage the underlying vector database infrastructure yourself.
What do you dislike about the product?
Pricing can get expensive as usage and data scale up, and some of the more advanced features take time to understand and configure properly. I’d also appreciate more flexibility and control when it comes to infrastructure and deployment options.
What problems is the product solving and how is that benefiting you?
Pinecone helps address the challenge of efficiently storing, indexing, and retrieving high-dimensional vector data for AI applications. It makes semantic search and RAG workflows faster and easier to scale, reducing infrastructure management effort while improving the relevance and overall response quality of AI applications.