Pinecone
Pinecone is a privately held company founded in 2019 that develops a fully managed, serverless vector database and adjacent AI infrastructure (Integrated Inference, Pinecone Assistant, Pinecone Nexus knowledge engine) for AI/ML engineering teams building agents, RAG applications, and enterprise search across SaaS, financial services, healthcare, and legal verticals.
- Company typePrivate
- Founded2019
- HeadquartersNew York, United States
- Headcount101–250
- GTM typeB2B
- OfferingSoftware
What Pinecone does
Pinecone Systems, Inc. is a privately held, venture-backed company founded in 2019 that develops a fully managed, serverless vector database built on object storage using an LSM tree-based slab architecture. Vectors and metadata are persisted in cloud storage (e.g., Amazon S3) while stateless query executors cache slabs on local SSDs and process queries via scatter-gather, decoupling compute and storage. Pinecone's proprietary indexing algorithms — Ananas (FJLT-based, used for small slabs up to ~10K records) and PQFS (asymmetric distance computation product quantization, used for medium slabs up to ~100K records) — are dynamically selected per slab and upgraded transparently during asynchronous compaction, without re-ingestion. Metadata fields are indexed via roaring bitmaps and adaptive pre/mid-scan filtering such that selective filters accelerate rather than slow queries.
The platform has expanded from a single vector database into a broader AI infrastructure suite. Around the core database, Pinecone offers Dedicated Read Nodes (reserved-capacity, fixed per-node pricing), Pinecone Inference (hosted embedding and reranking models including pinecone-sparse-english-v0 and pinecone-rerank-v0), Pinecone Assistant (a managed RAG service backed by hosted LLMs with cited responses and an OpenAI-compatible interface), and Pinecone Nexus with the KnowQL declarative query language (a knowledge engine for AI agents that compiles cited knowledge artifacts with governance, RBAC, and PII tagging). The company distributes through self-serve signup, cloud marketplaces (AWS, Google Cloud, Microsoft), Bring-Your-Own-Cloud deployment, and direct enterprise sales. Pricing is freemium-to-enterprise (Starter free, Builder $20/month flat, Standard from $50/month minimum, Enterprise from $500/month minimum, plus per-million-unit consumption of read/write units and inference tokens) with committed-use contracts available. Pinecone serves AI/ML engineering teams building agents and RAG applications, enterprise software/SaaS companies embedding AI-powered search, and regulated-industry customers (financial services, healthcare, legal) requiring SOC 2 Type II, HIPAA, GDPR, and ISO 27001 compliance plus BYOC deployment.
Pinecone firmographics
Firmographics- Name
- Pinecone
- Legal name
- Pinecone Systems, Inc.
- Website
- https://pinecone.io
- Company type
- Private
- Founded year
- 2019
- Operating status
- Operating
- Headcount range
- 101–250 employees
- Short description
- Pinecone is a privately held company founded in 2019 that develops a fully managed, serverless vector database and adjacent AI infrastructure (Integrated Inference, Pinecone Assistant, Pinecone Nexus knowledge engine) for AI/ML engineering teams building agents, RAG applications, and enterprise search across SaaS, financial services, healthcare, and legal verticals.
- Ownership category
- akta.pro rank
Pinecone industry classification
Industry- Product category
- Vector Database / AI Infrastructure
- NAICS
- Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (5182), Custom Computer Programming Services (541511), Computer Systems Design and Related Services (5415)
- SIC
- Services-Computer Programming, Data Processing, Etc. (7370), Services-Prepackaged Software (7372)
- akta.pro primary industry
- Data Platform (Unified Data & Analytics) Suites (HDAEABAD)
- akta.pro secondary industries
- Enterprise AI Data & Knowledge Platforms (Vector Databases, Knowledge Graphs) (HDAEANAH), Data Warehouse/Lakehouse Performance Optimization & Cost Management (HDAEABAL), Log Management & Analytics (HDABAJAC)
Keywords
Where Pinecone is headquartered
LocationHeadquarters
- HQ city
- New York
- HQ country
- United States
- HQ region
- North America
Offices1 record
Markets served
Pinecone business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Personnel, Technology or R&D, Infrastructure, Marketing or Sales, Operations
Revenue model
- Database Subscription and Usage: Free Starter tier; flat $20/month Builder tier; Standard at $50/month minimum usage (pay-as-you-go after) with 3-week trial including $300 credits; Enterprise at $500/month minimum usage. Pay-as-you-go for Database On-Demand, Inference, and Assistant. Storage priced at $0.33/GB/month on Standard/Enterprise.
- Write Units Consumption: Pay-per-use consumption-based pricing for write operations (upsert, update, delete) on Standard ($4–$4.50 per million write units) and Enterprise ($6–$6.75 per million write units), varying by cloud and region.
- Read Units Consumption: Pay-per-use consumption-based pricing for read operations (query, fetch, list) on Standard ($16–$18 per million read units) and Enterprise ($24–$27 per million read units), varying by cloud and region.
- Inference and Reranking API: Usage-based pricing for hosted embedding models (llama-text-embed-v2 at $0.16/M tokens, multilingual-e5-large at $0.08/M tokens, pinecone-sparse-english-v0 at $0.08/M tokens) and reranking models ($2 per 1k requests for bge-reranker-v2-m3, pinecone-rerank-v0, cohere-rerank-v3.5).
- Freemium / Free Tier: Free Starter plan with up to 2GB storage, 2M write units/month, 1M read units/month, 5M embedding tokens, 500 rerank requests, 5 serverless indexes, and 100 namespaces. Designed as land-and-expand funnel to paid tiers.
- Cloud Marketplace and BYOC Billing: Subscription via AWS Marketplace, Google Cloud Marketplace, and Microsoft Marketplace — billed through customer's cloud commit. Bring-Your-Own-Cloud (BYOC) deployment runs Pinecone in customer VPC with zero-access operations, available via Pulumi GitHub repo.
- Committed Use Contracts: Annual/multi-year committed spend contracts offering larger usage discounts and enhanced support for high-volume customers.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Freemium | Monthly | Starter — Free tier for trying out and small applications |
| Subscription | Monthly | Builder — $20/month flat for solo developers and small teams |
| Hybrid | Pay-as-you-go | Standard — $50/month minimum usage, popular for production applications |
| Hybrid | Multi-year contract | Enterprise — $500/month minimum usage for mission-critical production |
| Usage-based | Pay-as-you-go | Inference API consumption pricing |
Go-to-market motion3 records
Distribution channels5 records
Marketing channels8 records
Pinecone product offering
Product offeringCore offering
Pinecone operates a fully managed, serverless vector database built on object storage (e.g., Amazon S3) with a Log-Structured Merge tree-based slab system, supporting dense, sparse, and full-text indexes with integrated embedding and reranking inference. The platform delivers consistent low-latency queries at billion-vector scale, automatic indexing and compaction, adaptive metadata filtering, multi-cloud availability (AWS, GCP, Azure), Bring-Your-Own-Cloud deployment, and SOC 2 Type II / HIPAA / GDPR / ISO 27001 compliance for enterprise AI workloads.
Product overview
Pinecone offers a unified, fully managed vector database platform for production AI workloads rather than a single standalone product. The core platform is the Pinecone Vector Database — a serverless, object-storage-based database using an LSM slab architecture with automatic per-slab algorithm selection (Ananas, PQFS, IVF) and bitmap-based metadata filtering. Around this core, Pinecone offers several integrated modules and add-ons: Dedicated Read Nodes (DRN) for reserved-capacity high-throughput retrieval; Pinecone Inference for hosted embedding (llama-text-embed-v2, multilingual-e5-large, pinecone-sparse-english-v0) and reranking models (bge-reranker-v2-m3, pinecone-rerank-v0, cohere-rerank-3.5); Pinecone Assistant as a managed RAG-as-a-service with hosted LLMs (gpt-4o, gpt-4.1, gpt-5, o4-mini), citations, evaluation, multimodal context, and a dedicated MCP server; and Pinecone Nexus with KnowQL as a knowledge engine and declarative query language for agentic AI. The platform is complemented by a Marketplace of 90+ pre-built applications, a Python/Node/Java/Go/.NET/Rust SDK family, a CLI (`pc`), Bring-Your-Own-Cloud deployment, and an MCP server plus Claude Code, Cursor, Gemini CLI, and Agent Skills plugins for AI-agent integration.
Differentiator
Problem solved
Functional benefit
Brands
- Pinecone Nexus: Knowledge engine for AI agents that structures, contextualizes, and composes specialized knowledge contexts, with built-in governance, RBAC permissions scoping, PII tagging, and token consumption management.
- Pinecone Assistant
- KnowQL
- Pinecone Dedicated Read Nodes (DRN)
- Pinecone Marketplace
Products and services
- Pinecone Vector Database
Quantifiable outcome
- 70-95% reduction in token consumption per agent with Pinecone (Jenova case study)
- +4 more outcomes
Companies that use Pinecone
Customer profileNamed customers7 records
Segments4 records
Ideal customer profiles4 records
Pinecone technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration14 records
AI capability10 records
Feature9 records
Pinecone partnerships and signals
Strategic signalPartnerships
13 partnerships are on record, tiered flagship, core and minor.
- MicrosoftflagshipAnnounced Pinecone Nexus integration with Microsoft OneLake at Microsoft Build 2026, enabling AI agents to query enterprise data through structured knowledge artifacts. Delivers 95%+ reduction in frontier LLM token usage, 30x faster task execution, and 90%+ completion rates. Pinecone also supports Pinecone Nexus on Microsoft Fabric and participated in Microsoft for Startups.
- TealiumcoreAdded Pinecone to its AI Partner Ecosystem alongside LangChain at Tealium's Digital Velocity conferences. Bi-directional connector enables enterprises to ground RAG pipelines and LLM agents in real-time, consented customer data using vector retrieval.
- Moores Lab AIminorRam Sriharsha (former Pinecone machine learning and big data leader) joined Moores Lab AI's Advisory Board, indicating executive-level talent flow between Pinecone and the semiconductor AI solutions provider.
- CommvaultcoreStrategic partnership announced December 2025 to deliver enterprise-grade cyber resilience for AI vector databases via Commvault Cloud, supporting AWS, Azure, and Google Cloud. Adds immutable backups, point-in-time recovery, and extended retention without impacting query latency. Targeted for GA in H1 2026.
- Amazon Web Services (AWS)flagshipPrimary cloud infrastructure partner — Pinecone runs on AWS regions including us-east-1, us-west-2, eu-west-1 (Ireland), eu-central-1 (Frankfurt), and ap-southeast-1 (Singapore). Available via AWS Marketplace for SaaS subscription billing. Also supports AWS PrivateLink for BYOC deployment.
- Google Cloud Platform (GCP)corePinecone is available on Google Cloud Marketplace as a SaaS subscription and supports GCP Private Service Connect for BYOC deployment. Listed in Pinecone's multi-cloud marketplace distribution.
- Microsoft AzurecorePinecone is available on Microsoft Marketplace as a SaaS subscription and supports Azure Private Link for BYOC deployment. Azure region support included in Standard and Enterprise plans.
- AnyscalecoreListed as part of Pinecone's expanded partner ecosystem covering AI infrastructure integrations.
- LangChainflagshipListed in Pinecone's partner ecosystem. LangChain MCP client documentation specifically demonstrates Pinecone Assistant integration for multi-agent research workflows, and LangChain provides framework-level integration for building RAG applications with Pinecone.
- MistralcoreListed as part of Pinecone's expanded partner ecosystem covering AI infrastructure integrations.
- ClouderacoreListed as part of Pinecone's expanded partner ecosystem covering AI infrastructure integrations for enterprise data platforms.
- CoreWeavecoreCoreWeave publishes technical deployment guidance for running Pinecone on CoreWeave's cloud infrastructure for production-ready RAG with agentic AI. Typical latencies under 10ms via high-performance cloud interconnects.
- Anthropic (Claude Code)coreOfficial Pinecone plugin for Anthropic's Claude Code IDE provides 8 built-in skills, MCP server integration, and slash commands (/pinecone:quickstart, /pinecone:query, /pinecone:assistant, etc.) for direct agentic IDE workflows.
Scale indicators13 records
Recent moves8 records
Expansion highlights6 records
Pinecone competitors and assessment
Company assessmentDirect peers
- Weaviate: Weaviate offers an open-source and managed vector database with hybrid search, modules for embeddings, and enterprise features. It is a direct competitor to Pinecone in serving AI engineers building RAG and semantic search at scale, with overlapping target customers and similar pricing models.
- Qdrant: Qdrant is an open-source vector database written in Rust, with a managed cloud offering. It competes head-to-head with Pinecone in AI/ML and semantic search workloads, emphasizing high-performance ANN search, metadata filtering, and a self-host or managed deployment model.
- Zilliz (Milvus): Zilliz is the commercial entity behind the open-source Milvus vector database, offering a fully managed cloud service. It targets the same AI/ML engineer and enterprise customer base as Pinecone, with similar billion-vector scale ambitions and managed-service pricing.
- Chroma: Chroma is an open-source embedding database widely used in Python-based AI and RAG applications. It overlaps directly with Pinecone in the developer-first vector database category and is frequently evaluated alongside Pinecone for AI prototyping and production workloads.
Broad incumbents
- MongoDB Atlas Vector Search: MongoDB has integrated vector search into its Atlas cloud database, allowing customers to add vector similarity to existing document data. It is a broad incumbent in operational databases with vector capabilities layered on top, competing with Pinecone for workloads where customers want to consolidate on a single database vendor.
- Elastic (Elasticsearch / Elasticsearch Vector Search): Elastic has built vector search and semantic search into its core Elasticsearch platform with ELSER and dense_vector fields. It is a broad incumbent in search and observability that competes with Pinecone for hybrid search and RAG use cases, particularly with customers already using the Elastic Stack.
- Redis (RedisVL / Vector Search): Redis offers vector similarity search as part of its in-memory data platform, with the RedisVL library targeting AI applications. It is a broad incumbent in caching and real-time data that competes with Pinecone for low-latency vector retrieval, especially in customer-facing AI applications.
- Google Cloud Vertex AI Vector Search: Google Cloud's Vertex AI Vector Search (formerly Matching Engine) is a fully managed vector database inside the Google Cloud AI platform. It competes with Pinecone for GCP customers who want to keep vector workloads within their existing Google Cloud contract, particularly for enterprise AI and RAG deployments.
- AWS OpenSearch / Amazon Bedrock Knowledge Bases: AWS provides vector search via OpenSearch Service and managed RAG via Bedrock Knowledge Bases. It competes with Pinecone for AWS-native customers by bundling vector retrieval into existing cloud spend, posing a major threat to Pinecone's enterprise expansion on AWS.
- Azure AI Search: Azure AI Search (formerly Cognitive Search) provides integrated vector and hybrid search inside the Microsoft Azure ecosystem. It competes with Pinecone for Microsoft-anchored enterprise AI workloads and is increasingly paired with Azure OpenAI services for RAG, complementing the Microsoft OneLake integration Pinecone announced in 2026.
Market position
Strengths5 records
Weaknesses5 records
Competitive moat6 records
Key risks7 records
Key highlights7 records
Customer concentration
Pinecone social profiles
Digital presencePinecone compliance and trust
Trust signalCompliance4 records
Pinecone financial estimates
Financial estimateRevenue estimate
Valuation estimate
Pinecone leadership team
Management profileNumber of profiles
Profiles9 records
Pinecone funding detail
Funding detailFunding overview
Funding rounds4 records
Investors5 records
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Pinecone M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Pinecone
What does Pinecone do?
Pinecone operates a fully managed, serverless vector database built on object storage (e.g., Amazon S3) with a Log-Structured Merge tree-based slab system, supporting dense, sparse, and full-text indexes with integrated embedding and reranking inference. The platform delivers consistent low-latency queries at billion-vector scale, automatic indexing and compaction, adaptive metadata filtering, multi-cloud availability (AWS, GCP, Azure), Bring-Your-Own-Cloud deployment, and SOC 2 Type II / HIPAA / GDPR / ISO 27001 compliance for enterprise AI workloads.
Is Pinecone a public or private company?
Pinecone is a private company. It is classified as venture growth investor backed and is currently operating.
When was Pinecone founded?
Pinecone was founded in 2019. It employs 101 to 250 people.
Where is Pinecone based?
Pinecone is headquartered in New York, United States, in the North America region.
How does Pinecone make money?
Seven revenue lines are on record. Database Subscription and Usage is the primary driver. The others are write Units Consumption, read Units Consumption, inference and Reranking API, freemium / Free Tier, cloud Marketplace and BYOC Billing and committed Use Contracts.
Who are Pinecone's main competitors?
Direct peers on record are Weaviate, Qdrant, Zilliz (Milvus) and Chroma. Broad incumbents are MongoDB Atlas Vector Search, Elastic (Elasticsearch / Elasticsearch Vector Search), Redis (RedisVL / Vector Search), Google Cloud Vertex AI Vector Search, AWS OpenSearch / Amazon Bedrock Knowledge Bases and Azure AI Search.
Does Pinecone have an API?
Yes. Pinecone offers a public REST API and a gRPC API for managing serverless and pod-based vector indexes, including operations for index creation, configuration, upsert, query, fetch, delete, update, and list. Authentication is via API key (PINECONE_API_KEY). The API has versioning (e.g., 2025-10 stable, 2026-01.alpha for the documents API), supports rate limits at namespace and index level (e.g., 100 QPS query/upsert/update/delete per namespace, 50 MB/s upsert size, 2000 query read units per second per index). Available on AWS Marketplace, Google Cloud Marketplace, and Microsoft Marketplace. Official SDKs are provided for Python, Node.js, Java, Go, .NET, and Rust. A CLI tool (pc) is also offered for terminal-based management. Developer documentation is at docs.pinecone.io/reference/api/introduction.
What industry is Pinecone in?
Pinecone's product category is Vector Database / AI Infrastructure. Its primary akta.pro industry code is HDAEABAD, Data Platform (Unified Data & Analytics) Suites, with a secondary code of HDAEANAH, Enterprise AI Data & Knowledge Platforms (Vector Databases, Knowledge Graphs). Its NAICS code is 5182 and its SIC code is 7370.