Full-Text Search is Now Generally Available In Pinecone Database - Read the announcement
Select Experience:
Trusted Knowledge Infrastructure

Get enterprise AI out of pilot and into production

What an organization knows is its advantage. Pinecone makes AI accurate enough to trust, governed enough to approve, and predictable enough to budget.

The problem

Four ways enterprise AI stalls

Every one of these traces back to the same thing: what the system knows about your business.

The pilot never gets approved to launch

Over half of AI projects stall before production. Across hundreds of teams running agents, reliability outranked model capability as the top blocker.

Costs overrun the budget

Goldman Sachs projects token consumption to grow 24x by 2030. As AI usage expands, costs can rise faster than expected, making budgets harder to predict.

A wrong answer reaches the customer

AI can quote an outdated policy as if it still applies. A customer acts on the answer, leaving your team to resolve the dispute and rebuild their trust.

Security and legal never sign off

Security and legal need to trace AI answers to approved sources. Without that evidence, projects get stuck in review instead of reaching production.

The solution

The trusted AI knowledge platform

The knowledge layer is the only asset your organization owns. Turn your enterprise's knowledge into a ready-to-use layer for business users, agents, and customers.

Database

Semantic and full-text search with metadata filtering in one managed database, built for enterprise workloads.

Explore the database

Nexus

The knowledge engine. Compiles your data into governed, domain-specific knowledge and serves it to agents in one call.

Explore Nexus

Marketplace

Production-ready knowledge apps across sales, insurance, real estate, legal, HR, and support.

Explore Marketplace
The benefits

More accurate AI. Lower costs at scale.

Give AI the knowledge it needs to answer accurately, with less infrastructure to manage and controls that support enterprise security requirements.

More accurate

Over 90% task completion with up to 94% higher accuracy.

Agents complete the task. Search and recommendations return results people act on.

Faster

Up to 30x faster answers, on sub-50ms retrieval at billions-scale.

Concept to production in days rather than months. Your engineers spend the time on the product instead of the plumbing.

Lower cost

Up to 90% lower cost than agentic RAG on the same workload.

Costs stop scaling with every query. Finance can forecast the year instead of revising it each quarter.

Trusted

Security enforced at the data layer, where retrieval happens.

Keep data in your own cloud with zero-access BYOC, granular access controls, and support for enterprise compliance.

Trusted in production

Top enterprises run Pinecone in production

Performance

Built for regulated, production-scale workloads

99.95%Uptime SLAEnterprise tier
24k+Active organizationsRunning in production
1M+DevelopersWorldwide
Governance & compliance

Answers you can defend in an audit

Accuracy and access are enforced at the data layer, where retrieval happens.

Answers that cite their sources

Retrieval returns the records an answer was built from, so a reviewer can trace what the model saw. Pinecone Nexus returns field-level citations with confidence tiers.

See Nexus

An audit trail your auditors accept

Audit logs record user, service account, and API activity across your Pinecone resources, with Amazon S3 as a destination.

Read the security docs

Access control at the data layer

RBAC, SSO with SAML 2.0, SCIM, and service accounts. Permissions are enforced where the data is retrieved, so unauthorized access is prevented rather than detected afterward.

Read the security docs

No model lock-in

Pinecone supports embeddings from any model or provider. Change models, or move between hosted and your own, without rebuilding the knowledge layer underneath.

Explore the platform

Security and compliance your team can trust

The security and operational controls behind 10,000+ paying companies.

SOC 2 Type II

Annual audits. Controls for security, availability, and confidentiality.

HIPAA

BAA available on request for covered entities and business associates.

GDPR

Data processing agreements, EU data residency, and right-to-erasure support.

ISO 27001

Internationally recognized information security management standard.

Deployment

Deployment that fits what's already running

From instant on-demand indexes to a privately managed region inside your own cloud account, adapting to the infrastructure and compliance posture already in place.

Evaluate

What to take back to your team

Three things you can put in front of security, procurement, and engineering before the first call.

Security and compliance packet

Certifications, subprocessors, penetration test summaries, and the answers your security review is going to ask for.

Architecture review

Walk your workload through with a solutions architect. You leave with a deployment model, a scaling path, and a cost envelope.

Cost model

Size your own workload against On-Demand and Dedicated Read Node pricing before you talk to anyone.

FAQs

Questions your team will ask

Where do we start: the database or the knowledge engine?

The database, in most cases. Search, recommendations, and RAG run directly on it, and it is the retrieval foundation Nexus is built on. Add Nexus when you are putting agents into production and hitting accuracy, cost, or governance walls that better retrieval alone does not solve.

What does retrieval cost once we are at scale?

On-Demand pricing suits traffic that moves, since you pay only for the reads, writes, and storage you consume. For steady, high-volume workloads, Dedicated Read Nodes charge a fixed hourly price per node: three anonymized production workloads cut their retrieval bill by 77 to 97 percent moving from On-Demand to Dedicated Read Nodes. Size your own workload before you talk to anyone.

Can we run Pinecone inside our own cloud?

Yes. Bring Your Own Cloud runs the database inside your own AWS, Azure, or Google Cloud account with a zero-access operating model: no SSH, VPN, or inbound network access, and vectors and queries never leave your VPC. Nexus deploys the same way.

We already have a vector database. Why do we need a knowledge engine?

The vector database is the foundation, and Nexus is the engine above it. Search returns the top matching text stripped of the relationships that connect it, so an agent reconstructs on every request how a policy connects to a record. Public Preview customers compiled 3.5 million source chunks into roughly 26,000 pieces of structured, queryable knowledge, which is the work that stops happening at query time.

Can't we just wait for a better model?

On τ-Knowledge, both GPT-5.2 and GPT-5.5 already had enough reasoning capability. A larger model does not fix a bill that scales with retrieval, or a completion rate capped by the context an agent can reach.

Our ontology team owns this. Isn't that the same thing?

Central ontologies are authored up front by a team that does not do the work, and they decay from the day they ship. A Nexus Manifest is written by the subject matter expert, scoped to one job rather than the whole company, and re-curated in the same loop that surfaces conflicting sources for that expert to adjudicate.

What happens if we want to leave?

The knowledge layer Nexus compiles is yours, and you can download it as an archive. Because you also choose the models and can run in your own cloud, there is no second migration waiting behind the first.

See it on your own workload

Knowledge infrastructure your security team can approve and your CFO can forecast.

Subscribe to Pinecone