The leading vector database for AI applications. Similarity search, embeddings and RAG systems with ultra-fast performance and scalability.
Pinecone offers a fully managed vector database for AI applications. With sub-second latency, automatic scaling and easy integration, it enables advanced similarity search and RAG applications.
Sub-second latency for billions of vectors
Automatic scaling based on workload
SOC 2 Type II zertifiziert mit VPC-Support
Pay-per-use ohne Infrastruktur-Management
Kombiniert Dense und Sparse Vektoren
Erweiterte Filterung mit Metadaten
Live Updates ohne Performance-Einbußen
Vector search for modern AI applications
Intelligente Suche über Text, Bilder und Audio
Retrieval Augmented Generation für LLMs
Personalisierte Empfehlungssysteme
The leading vector database
Everything you need to know about implementing vector databases and similarity search with Pinecone
Pinecone is a fully managed vector database designed specifically for machine learning applications requiring similarity search and semantic understanding. Unlike traditional databases that store and search exact matches, Pinecone enables searching by meaning and context through vector embeddings. This capability is essential for RAG (Retrieval-Augmented Generation) systems, recommendation engines, semantic search, and personalization features. Pinecone's purpose-built architecture ensures low-latency, high-accuracy similarity search at scale.
RAG combines large language models with external knowledge sources by using vector similarity search to find relevant context. Pinecone stores document embeddings and enables real-time retrieval of the most relevant information based on user queries. This allows AI applications to access up-to-date, domain-specific information without retraining models. RAG with Pinecone enables applications like intelligent document search, knowledge bases, customer support systems, and research assistants that can access vast amounts of specialized knowledge.
Pinecone excels in applications requiring semantic search and similarity matching. This includes e-commerce platforms with visual and semantic product search, content recommendation systems for media and publishing, knowledge management systems for enterprises, customer support platforms with intelligent document retrieval, personalization engines for user experiences, fraud detection systems with anomaly detection, and research tools for scientific and academic applications. Any application needing to understand meaning and context rather than exact text matches benefits from Pinecone.
Pinecone integration timeline varies by data volume and application complexity. Basic implementations with small datasets can be completed in 2-3 weeks. Enterprise deployments with large-scale data ingestion, custom embedding models, and sophisticated search workflows typically require 6-12 weeks. Complex applications involving multiple indexes, real-time updates, and advanced filtering may take 12-16 weeks. Our team provides comprehensive integration support including data pipeline setup, embedding optimization, index configuration, and performance tuning.
Pinecone pricing is based on the number of vectors stored and queries performed. Starter plans begin at $70/month for 5M vectors with additional charges for usage above included quotas. Enterprise deployments typically range from $500-5,000 monthly depending on scale and requirements. Integration costs typically range from $8,000-25,000 including data pipeline setup, embedding optimization, and production deployment. We help optimize costs through efficient indexing strategies, appropriate dimensionality selection, and smart caching to minimize query volumes while maintaining performance.
Discover the advantages of purpose-built vector search for AI applications
Unlike general-purpose databases with vector extensions, Pinecone is specifically designed for machine learning workloads. This specialized architecture enables superior performance, accuracy, and scalability for similarity search operations. Pinecone's optimized indexing algorithms and query processing ensure low-latency responses even with billions of vectors, making it ideal for real-time AI applications requiring instant semantic search results.
Pinecone handles all infrastructure management including scaling, replication, backup, and maintenance. This allows your team to focus on building AI features rather than managing database infrastructure. Automatic scaling ensures consistent performance as your data grows, while built-in redundancy and backup systems provide enterprise-grade reliability for mission-critical applications.
Pinecone supports sophisticated filtering capabilities allowing you to combine vector similarity search with traditional metadata filtering. This enables complex queries like "find similar products in a specific category and price range" or "retrieve relevant documents from the last 30 days." These advanced filtering capabilities make Pinecone suitable for complex business applications requiring both semantic understanding and precise constraints.
Pinecone supports real-time vector updates and deletions while maintaining search consistency and performance. This capability is crucial for applications with dynamic content like e-commerce catalogs, news feeds, or user-generated content. The platform ensures that new vectors are immediately searchable without requiring full reindexing, enabling applications that need to stay current with rapidly changing data.
Tell us what you need and get exact pricing + timeline in 24 hours
Launch your product quickly and start generating revenue
No surprises - clear pricing and timelines upfront
Transparent communication and guaranteed delivery
Built to grow with your business needs