Pinecone Feature Trails

Feature trailActive

Bring Your Own Cloud (BYOC)

Pinecone serverless is now generally available on AWS, GCP, and Azure with a Bring Your Own Cloud (BYOC) offering. This allows customers to deploy a privately managed Pinecone region within their own cloud account, ensuring data sovereignty and control. The BYOC offering includes a zero-access operating model, self-serve deployment, and support for AWS, GCP, and Azure. It complements the existing serverless offering by providing enhanced security and compliance for enterprises and regulated industries.

4 posts · 2023–2026

Timeline

20232026

Feature trailActive

Vector Database Technology Evaluation

Pinecone continues to evolve its AI search capabilities by unifying dense and sparse retrieval with advanced reranking. This includes introducing sparse-only vector index types, a new sparse embedding model (pinecone-sparse-english-v0) that outperforms traditional methods like BM25, and integrating state-of-the-art rerankers (cohere-rerank-3.5, pinecone-rerank-v0). These enhancements aim to deliver up to 48% better performance by capturing both semantic understanding and precise keyword matches. The company has also developed VQ-bench, a composable framework for evaluating vector quantization methods, enabling fair and reproducible benchmarking of various compression techniques and primitives, with contributions to primitives, quantizers, and datasets.

32 posts · 2021–2026

Timeline

20212026

Feature trailActive

Full Text Search

Pinecone now supports full text search by integrating the Tantivy library, enabling advanced text functionality alongside vectors and metadata. This includes full Lucene-syntax queries, multi-field schemas, BM25 scoring, tokenization in 18 languages, stemming and stop-word removal, phrase matching search, and new filters based on text match for vector search. The system leverages Tantivy's Rust implementation and maps cleanly onto Pinecone's slab architecture. Document ordering is informed by vector similarity and BM25 scores, allowing for hybrid search. This complements existing capabilities by providing exact-match precision for identifiers and phrases, addressing limitations of purely semantic search.

9 posts · 2021–2026

Timeline

20212026

Feature trailActive

Managed Knowledge Layer for AI Applications

Pinecone Assistant is a fully managed service for building AI assistants over private data. This post details the development of a new benchmarking scheme, the 'Answer Alignment Score', to measure the quality of AI assistant answers. This metric aims to improve upon existing unsupervised metrics by using a supervised protocol that extracts atomic facts from ground truth answers and classifies the generated answer's entailment, contradiction, or neutrality towards each fact. The post presents results comparing Pinecone Assistant against OpenAI assistants on three benchmark datasets (FinanceBench, Open Australian Legal, NQ-HARD), showing Pinecone Assistant's outperformance. Future work includes automating benchmark results generation for arbitrary datasets.

21 posts · 2023–2026

Timeline

20232026

Feature trailActive

Agent-Friendly API Design

Pinecone Assistant is now generally available, offering optimized interfaces with new chat and context APIs, custom instructions for tailoring assistant behavior, expanded input/output formats (JSON, .md, .docx), and region control (EU/US). The Context API provides structured context for agentic workflows, and the Chat API delivers grounded responses with citations. Custom instructions allow for fine-tuning assistant roles, and metadata filters enhance data management. The service is built on Pinecone's managed vector database, ensuring data security and privacy.

2 posts · 2025–2026

Timeline

20252026

Feature trailActive

API Versioning

Pinecone has introduced a date-based API versioning schema managed via a custom header, allowing for incremental changes and a clear, predictable, and manageable API lifecycle. Stable API versions are supported for a minimum of 12 months. The Global Control Plane API has been launched, using a global URL for all control plane operations, supported within Python, Node, and Java clients, and also via Terraform and Pulumi. This update also includes broader availability with a new serverless region.

5 posts · 2024–2026

Timeline

20242026

Feature trailActive

AI Application Performance Measurement

Pinecone has evolved its serverless architecture to optimize for agentic workloads, characterized by millions of namespaces with small, sporadically accessed vector sets. This involves architectural innovations in the write path with adaptive indexing based on log-structured merge trees and in the query path with on-demand data fetching from blob storage and adaptive caching. The architecture also sees improvements for traditional search workloads with enhanced disk-based metadata filtering and a new benchmarking pipeline that automates the process of measuring performance across various configurations for specific customer workloads, enabling precise cost and performance recommendations.

15 posts · 2023–2026

Timeline

20232026

Feature trailActive

Test Data Generation for Vector Databases

Pinecone has introduced a comprehensive workflow for generating and managing test data for vector databases. This includes utilities for creating realistic datasets from real text, generating embeddings locally or with hosted models, preparing metadata in a format compatible with Pinecone's bulk import (Parquet files with specific schema requirements), and partitioning data for distributed processing. The workflow supports iterative development and testing of vector search capabilities at various scales. This post adds a new method for bulk data ingestion directly from object storage, supporting Parquet files and offering significant cost and efficiency improvements over traditional upsert methods for initial data loading and large-scale migrations.

1 post · 2024–2026

Timeline

20242026

Feature trailActive

Observability Platform

Pinecone has enhanced its observability capabilities by introducing an open-source monitoring stack, `pinecone-monitoring`, built on Prometheus and Grafana. This stack provides pre-built dashboards and alerting for both Pinecone SaaS and BYOC deployments, offering deeper insights into index performance, operational data, and BYOC infrastructure health. It enables proactive operations, root cause analysis, workload change detection, cost visibility, capacity planning, multi-project visibility, and now includes enhanced metrics for request frequency, duration, read/write unit consumption, and latency via Datadog integration and new Prometheus endpoints.

4 posts · 2023–2026

Timeline

20232026

Feature trailActive

Dedicated Read Nodes

Pinecone Dedicated Read Nodes (DRN) are now generally available, offering reserved capacity for queries with predictable performance and cost. This complements the On-Demand service for bursty workloads. DRN provides lower, predictable costs, consistent low-latency and high throughput, and scales for large workloads. It supports billion-vector semantic search, high-QPS recommendation systems, mission-critical AI services, and large enterprise/multitenant platforms. The system scales via replicas for QPS and shards for storage, eliminating rate limits. GA adds configurable performance vs. recall per query, metrics exporting, a web console, and multi-namespace support (early access).

5 posts · 2022–2026

Timeline

20222026

Feature trailActive

AI Service Load Balancing

Pinecone Assistant routes requests across embeddings, rerankers, and LLMs using a service-aware load balancer based on the 'power of two choices' algorithm. This provides adaptive routing without a global coordinator, automatic failover during upstream incidents, and reduced latency. Different scoring policies are used for different service types (embeddings/rerankers on latency+reliability, LLMs on reliability+load) to optimize performance and availability.

1 post · 2026

Timeline

Feature trailActive

Object Lifecycle Management

Pinecone has implemented 'Janitor', a robust system for managing object lifecycle and safely deleting billions of objects at scale. This system addresses the challenges of immutable storage by identifying and removing stale and orphaned objects through distinct 'Normal', 'Orphan', and 'Customer Deletion' modes. Janitor employs a core protocol of 'Identify -> Verify -> Execute' to ensure safe and auditable deletion, with advanced property-based testing using a mock clock to validate complex, time-dependent behaviors.

1 post · 2023–2026

Timeline

20232026

Feature trailActive

AI Application Performance Measurement

Pinecone's serverless architecture has been further evolved to optimize for diverse AI workloads, including recommender systems, large-scale semantic search, and agentic systems. Key innovations include a log-structured indexing scheme for improved freshness and cost-effectiveness, predictable caching mechanisms, and disk-based metadata filtering for high-cardinality use cases. These advancements aim to provide predictable performance, high reliability, and cost efficiency for production AI applications.

2 posts · 2024–2026

Timeline

20242026

Feature trailActive

Vector Indexing Architecture

Pinecone's vector database architecture is purpose-built for vector search, moving beyond single-algorithm approaches. It employs a per-slab adaptive indexing strategy (Ananas, PQFS, and IVF) on a serverless infrastructure, designed to handle the memory, compute, and scale requirements of real-time AI applications. This approach contrasts with 'bolt-on' solutions that integrate algorithms like HNSW into existing architectures, which Pinecone argues are unable to meet production demands due to memory, compute, and scale limitations. New features include vertical scaling for zero-downtime capacity increases, collections for centralized data storage and experimentation, and p2 pods offering up to 10x better performance for high-traffic applications with a new graph-based index. Query performance on p1 and s1 pods has also been improved by approximately 50%. The Starter plan now includes s1 pods, offering 5x greater capacity.

6 posts · 2022–2026

Timeline

20222026

Feature trailActive

Metadata-Based Data Operations

Pinecone has introduced new bulk data operations that allow users to update, delete, and fetch records directly using metadata filters. This enhances data management scalability and efficiency by enabling targeted operations on large datasets without needing to collect individual record IDs. The operations leverage existing filter syntax, simplifying integration and common data management tasks like bulk updates, data purges, and selective fetches. Pinecone 2.0 introduces metadata filtering for these operations.

2 posts · 2021–2025

Timeline

20212026

Feature trailActive

Build vs. Buy for AI Infrastructure

This post discusses the strategic decision-making process for AI infrastructure, specifically focusing on the trade-offs between building custom solutions (like vector databases) and purchasing managed services. It highlights the hidden costs of building, including infrastructure management, search optimization, reliability, and talent acquisition, and argues that for many companies, buying a managed service leads to faster value creation and allows engineering teams to focus on core business innovation.

1 post · 2025

Timeline

20252026

Feature trailCooling

Integrated Inference for Embeddings

Pinecone Inference provides integrated embedding generation and reranking capabilities, allowing users to generate vector embeddings and perform reranking directly within the platform. This simplifies embedding pipelines by enabling automatic embedding and reranking generation as part of the upsert or query process. The reranking feature supports specific models like bge-reranker-v2-m3 and cohere-rerank-3.5, with plans for more models. This integration aims to reduce infrastructure management.

8 posts · 2024–2025

Timeline

20242026

Feature trailCooling

Local Development Emulator

Pinecone Local, a self-hosted, in-memory emulator of the vector database, is now available in public preview for all users. This allows developers to prototype, test, and develop AI applications locally, integrating workflow testing into CI/CD pipelines without incurring usage costs or resource drain. It supports all SDKs and control/data plane endpoints, requiring no API key for local use. The emulator is distributed via a Docker image (`pinecone-local`) and is not intended for production use.

1 post · 2024

Timeline

20242026

Feature trailCooling

Customer-Managed Encryption Keys (CMEK) and Role-Based Access Control (RBAC)

Pinecone has introduced Customer-Managed Encryption Keys (CMEK) for enhanced data security and tenant isolation, allowing customers to manage their own encryption keys. This feature is enabled at the project level and impacts all indexes within that project. The system uses hierarchical encryption with Key Encryption Keys (KEKs) and Data Encryption Keys (DEKs) to maintain performance and security. Additionally, Pinecone has expanded Role-Based Access Control (RBAC) with API key roles, providing granular permissions for both control plane and data plane operations. CMEK is currently available on AWS, with Azure and GCP support planned. Private Endpoints for AWS PrivateLink are also generally available.

1 post · 2024

Timeline

20242026

Feature trailQuiet since 2024

Private Endpoints

Pinecone serverless now offers Private Endpoints for secure, private connectivity from a user's VPC to Pinecone via AWS PrivateLink, reducing exposure risks and securing data traffic over the AWS network. Support for GCP and Azure is planned.

1 post · 2024

Timeline

20242026

Feature trailQuiet since 2023

Production Architecture Reference

Pinecone has released an open-source AWS Reference Architecture, defined via Pulumi, to help users deploy production-grade, high-scale AI applications leveraging Pinecone's vector database. This architecture demonstrates best practices for AWS and Pinecone, including distributed systems, queue-based work distribution, network and security separation, ECS microservices, and autoscaling. It has been tested with large data batches and provides a quick start guide and technical walkthrough for deployment.

2 posts · 2023

Timeline

20232026

Feature trailQuiet since 2023

Python Packaging and Dependency Management

Pinecone has adopted Poetry for its Python client, simplifying dependency management, local development, and integration with AI libraries like LangChain. This move aims to improve the developer experience for contributors and users alike, while maintaining backward compatibility for existing users.

1 post · 2023

Timeline

20232026

Feature trailQuiet since 2023

Free Tier Architecture Efficiency

Pinecone has introduced a new, more efficient architecture for its free tier, running in a new free region called `gcp-starter`. This change enables instant sign-ups, allows new users to keep their free indexes indefinitely, and significantly reduces index provisioning time to seconds. This new architecture also simplifies index creation for new users by removing features like namespaces, collections, and delete by metadata from free indexes. The `gcp-starter` region replaces `us-west1-gcp-free`.

5 posts · 2023

Timeline

20232026

Feature trailQuiet since 2022

Rust Vector Database Rewrite

Pinecone has rewritten its core vector database engine from Python and C++ to Rust to improve performance, memory management, and developer experience. This involved identifying and addressing performance bottlenecks through continuous benchmarking and building an observability layer using OpenTelemetry and OpenMetrics. Performance optimizations focused on leveraging Rust's low-level instruction sets, memory layout control, and efficient async task execution with Tokio and Rayon.

1 post · 2022

Timeline

20222026