The First Database for Vector-Based Applications
Pinecone is a managed database for working with vectors in production. Transform and index billions of high dimensional vectors. Search and rank in milliseconds. Work from anywhere, like a Jupyter notebook. Go to production in days. No maintenance needed, ever.
Customer Applications
Answers queries, like nearest-neighbor search, faster and more accurately than any open-source library without parameter tuning.
Provides blazing-fast indexing and efficient storage for high-dimensional vectors.
Ensures exceptional performance regardless of scale with dynamic load balancing, replication, name-spacing, sharding, and more.
Supports custom model- or container-based transformations and ranking.
Enables updating and querying vector indexes from anywhere, including Jupyter notebooks.
Provides programmatic creation and management of artifacts such as models, indexes, and services.
Provides hands-free resource allocation, observability, SLA guarantees, security, and more.
Supports critical loads with built-in high availability and 24/7 operational on-call.
1×
Real-time indexing speed compared to open-source libraries.
<999ms
Latency for vector retrieval for over a billion vectors.
0%
Practically perfect recall, precision, and relevance.
API-First Experience
Pinecone is a fully managed service, so there is no need to configure open-source software or set up infrastructure.
Customize
Configure and launch services with custom models, transformations, and rankers.
Load
Add or update data in batches or streams. Data is turned into vectors and the index is updated in real time.
Query
Query and get results. Find similar or top-ranking items in milliseconds.
Observe
Monitor and track deployments for operational health.
Compare
No more compromising between accuracy and speed, or building and maintaining complex infrastructure.
vs Do-It-Yourself
With Pinecone there is no need to build and manage complex infrastructure to run open-source nearest-neighbor algorithms such as Annoy, Faiss, and NMSLib in production.
vs Legacy Search
Sidestep legacy index and search solutions that require extensive workarounds to support high-dimensional vector search and model based transformations and ranking.
vs Black-Box Solutions
Pinecone replaces black-box personalization products by letting you build better inhouse solutions using your own data and models.
Editions
The Teams edition comes with cost effective pay-as-you-go pricing, quick and easy onboarding, and hands-free operations. Get started →
The Enterprise edition comes with additional operational control, tighter security and governance, guaranteed performance and resilience, and 24/7 on-call operational support. Request demo →
| Teams | Enterprise | |||
| Vector Index | ||||
| Real Time Indexing Items are searchable milliseconds after updates | Teams Enterprise | |||
| Rich Vector Queries Cosine Similarity, Max Dot Product, and Euclidean distance | Teams Enterprise | |||
| Autotuning Indexing Algorithm No algorithm selection or parameter tuning needed | Teams Enterprise | |||
| Namespacing Group IDs into namespaces and search only within specific namespaces | Teams Enterprise | |||
| Metadata Store Attach arbitrary metadata to vectors and retrieve with query results | Teams Enterprise | |||
| Fast Exact Search For extremely accuracy-sensitive applications | Teams Enterprise | |||
| Container Distribution | ||||
| Realtime Pipelines Specify arbitrary preprocessing, transformations, ranking, and postprocessing | Teams Enterprise | |||
| Data Sharding For reduced latency | Teams Enterprise | |||
| Replication For increased throughput and resilience | Teams Enterprise | |||
| Auto Node Recovery For high availability | Teams Enterprise | |||
| Replica Autoscaling For handling spiky query throughput | Teams Enterprise | |||
| Zero-Downtime Revisions Switch production traffic between services seamlessly. | Teams Enterprise | |||
| API - Clients | ||||
| Model Hub For ingress pipelines and ranking with support for TensorFlow, PyTorch, Scikit, or custom containers | Teams Enterprise | |||
| Python Client Intuitive interface available from everywhere including Jupyter notebooks | Teams Enterprise | |||
| HTTPs/grpc Client Access to lowest-level, highest-performance interfaces | Teams Enterprise | |||
| Java Client For easy integration in JVM-based systems | Teams Enterprise | |||
| Managed Operations | ||||
| Resource provisioning No need to manage hardware resources | Teams Enterprise | |||
| Observability Real time monitoring, dashboards, logging, and alerting | Teams Enterprise | |||
| Optional GCP Deployment | Teams Enterprise | |||
| Single Tenant Environment With VPC peering for further data and network isolation | Teams Enterprise | |||
| Geo-Replication Disaster recovery across availability zones | Teams Enterprise | |||
| Role-Based Access Control (RBAC) Admins can manage permissions per user and per project | Teams Enterprise | |||
| Operational Support 24/7 oncall support | Teams Enterprise | |||
| Get Started | Request Demo | Get Started Request Demo | ||