Build Knowledgeable AI on AWS
Build accurate, production-grade AI applications on AWS with Pinecone’s fully managed, serverless vector database. Pinecone runs natively on AWS and integrates seamlessly with Amazon Bedrock and Amazon SageMaker to power search, RAG, agents, and recommendation systems at scale.
Why Pinecone on AWS
Pinecone and AWS turn unstructured data into real-time knowledge.
Most enterprise data is unstructured, and traditional systems weren’t built for semantic retrieval at scale. Production AI requires infrastructure purpose-built for accuracy, speed, and cost efficiency.
Built natively on AWS, Pinecone is the vector database foundation for production-grade AI applications.
Pinecone delivers:
- Accuracy at scale: 20–100ms retrieval across 100M–billion+ vector datasets
- Cost efficiency aligned to workload: Serverless, usage-based pricing—no idle infrastructure
- Real-time freshness: New data searchable in seconds
- Enterprise-grade reliability and security: 99.9% uptime SLA, encryption, private endpoints, audit logs
Featured whitepapers
Dive deep into advanced topics with our latest whitepapers.
Building remarkable multimodal search applications with Pinecone and AWS
Explore how to integrate diverse data types for richer, more contextual analysis in search applications.
Optimizing and accelerating data classification with Pinecone and AWS
Discover how vector databases can enhance efficiency and scalability in AI-driven classification tasks.
Build AI Agents with Amazon Bedrock and Pinecone
Want to see it in action? Follow this hands-on AWS workshop to build a production-ready AI agent using Amazon Bedrock and Pinecone.
Explore the AWS WorkshopBuilt for Developers. Ready for Production.
From rapid prototyping to enterprise deployment:
- Create your first index in minutes
- Hybrid search (dense + sparse) with metadata filtering
- Automatic scaling from thousands to billions of vectors
- No capacity planning or cluster tuning
More than 9,000 customers ship AI applications faster with Pinecone.
Trusted by Leading Organizations on AWS
“The ability to scale quickly, without re-architecting or running into cost or performance cliffs, has been huge for us. Pinecone just works, which lets us grow without hesitation.”
Sarosh Khan
Head of AI
Since deploying Pinecone, Vanguard has boosted accuracy by 12% with hybrid retrieval, reduced call times and overhead, and enhanced compliance.
“Pinecone gave us the flexibility and performance we needed to move from basic search to something much more knowledgeable—retrieval that understands context, adapts to our data, and scales with our growth.”
Max Tano and Harrison Linowes
Founding Engineers
“Our choice to work with Pinecone wasn’t just based on technology; it was rooted in their commitment to our success. They listened, understood, and delivered beyond our expectations.”
Jacob Eckel
VP, R&D Division Manager
"We trust Pinecone to provide the foundational infrastructure we rely on for accurate, production-grade vector retrieval at scale."
Alden Do Rosario
CEO
1up achieves 10x faster response generation for RFPs and compliance questionnaires with Pinecone
DISCO Revolutionizes Legal Technology with Pinecone
“Pinecone was incredibly easy to use, allowing us to quickly achieve success. We chose it to fulfill the promises we made to our clients with the products we were building.”
Philipp Grothaus
CTO
InpharmD Redefines Evidence-Based Healthcare with Pinecone
“Pinecone has transformed our customer service operations, enabling us to achieve unprecedented levels of efficiency and customer satisfaction”
Manish Pandya
SVP of Digital Transformation
We evaluated our semantic search system’s recall performance both with and without the Pinecone reranker and saw substantial improvements. Using a reranker has from that point onwards been a no-brainer. By centralizing our vector database interactions to one system, it will be easier for other teams to adopt this technology too.
Viktor Karlsson
Software Engineer