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Database ⇄ AI

Neo4j to Pinecone integration — real-time, two-way sync

Keep Neo4j and Pinecone in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Neo4j and Pinecone

Sync the records in Neo4j into Pinecone and land its embeddings, classifications, and generated fields back on the same rows, in real time and without a pipeline to maintain.

AI systems do not hold customers or invoices the way business apps do. What they hold is derived from your data: the vectors and metadata in a vector store, or the classifications, extracted fields, and generated text a model produces over records it was given. Neo4j is where those source records actually live. The bridge between the two is the row itself, since an item in Pinecone and the record in Neo4j it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Indexes & Constraints, Databases, Users & Roles, Nodes in Neo4j with Index statistics, Indexes, Vectors (records), Namespaces in Pinecone in real time. Rows created or changed in Neo4j flow into Pinecone so inference and embedding run on current data, and the scores, labels, and generated fields Pinecone produces flow back onto the matching rows in Neo4j, mapped field by field. A change on either side appears on the other within seconds, with no extraction job or webhook plumbing to keep alive.

Because matching is by a stable identifier, every row in Neo4j stays tied to its AI-side counterpart in Pinecone. Retrieval, enrichment, and generated content always resolve back to the record they came from, so there are no orphaned vectors and no labels describing a version of a row that no longer exists.

Common use cases

  • 01 Back up a namespace by exporting its vector ids, values, and metadata to object storage or a database on a schedule using the list and fetch operations.
  • 02 Write embeddings and their metadata into a Pinecone index from a Postgres or warehouse table so a semantic-search or RAG feature always queries fresh vectors.
  • 03 Write computed relationship scores (fraud, influence, similarity) back to operational systems.
  • 04 Keep a customer-360 graph continuously updated from ERP, CRM, and support sources.

Common sync patterns

Keep derived data fresh as sources change

When a row in Neo4j is updated or removed, its counterpart in Pinecone is updated or removed too, so nothing in Pinecone describes a record that has since changed or gone.

Backfill once, then stay in step

Load your existing rows from Neo4j into Pinecone to build the index or enrichment set, then let ongoing changes sync automatically instead of re-running the whole job.

One record, one identifier

Each item in Pinecone carries the key of the row in Neo4j it came from, so results resolve back to the exact record with nothing orphaned or duplicated.

What you can sync between Neo4j and Pinecone

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Neo4j objects Pinecone objects How this pairing syncs
Relationships Typed, directed edges that carry the connections syncs exist to model. Indexes Serverless or pod-based containers holding vectors of a fixed dimension and distance metric (cosine, dotproduct, euclidean); managed on the control plane (api.pinecone.io) via create, list, describe, configure, and delete. describe_index returns the per-index data-plane host. Relationships is specific to Neo4j and Indexes to Pinecone — each maps to any object or custom field on the other side.
Properties Key-value attributes on both nodes and relationships, mapped from source fields. Vectors (records) The core data: an id (up to 512 chars), a dense values array, optional sparse_values, and JSON metadata (up to 40 KB filterable per record). Full CRUD on the data plane via upsert, update, fetch, query, and delete, so write is supported here. Properties is specific to Neo4j and Vectors (records) to Pinecone — each maps to any object or custom field on the other side.
Labels Node type markers used to map source tables or objects onto the graph. Namespaces Partitions inside an index; every read and write targets one namespace and vectors across namespaces are isolated. Enumerated with list_namespaces and sized per namespace via describe_index_stats. Labels is specific to Neo4j and Namespaces to Pinecone — each maps to any object or custom field on the other side.
Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast. Collections Immutable snapshots of a pod-based index that store its data but not its definition; created, listed, and deleted on the control plane and used to recreate a pod-based index. Serverless indexes use Backups instead. Indexes & Constraints is specific to Neo4j and Collections to Pinecone — each maps to any object or custom field on the other side.
Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs. Backups Point-in-time snapshots of a serverless index; created, listed, and restored into a new index on the control plane for recovery or cloning. Read as a recovery-asset inventory. Databases is specific to Neo4j and Backups to Pinecone — each maps to any object or custom field on the other side.
Users & Roles Security principals controlling what an integration credential can query or modify. Index statistics Describe_index_stats returns total and per-namespace vector counts, the index dimension, and index fullness; read to size a sync and to detect drift between Pinecone and the source of truth. Users & Roles is specific to Neo4j and Index statistics to Pinecone — each maps to any object or custom field on the other side.

How changes propagate between Neo4j and Pinecone

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Neo4j Pinecone Sub-second propagation

DetectionChanges in Neo4j are captured at the source via change data capture — no polling loop against its API. Neo4j Change Data Capture on Enterprise and Aura streams graph changes.

DeliveryEach detected change is written to Pinecone through its API, with automatic retries and rate-limit backoff.

Pinecone Neo4j Interval-based propagation

DetectionStacksync polls Pinecone for changes on an incremental schedule, reading only records changed since the previous pass. No webhooks and no native change-data-capture feed.

DeliveryEach detected change is written to Neo4j through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Pinecone: Data-plane operations are capped per second: query, upsert, update, and delete at 100 req/s per namespace, fetch at 100 req/s per index, and list at 200 req/s per index; breaches return HTTP 429. Hard request limits also apply: an upsert is max 2 MB or 1000 records, metadata max 40 KB per record, and query top_k up to 10,000 with a 4 MB result cap.
What ships with Neo4j ⇄ Pinecone

Connect Neo4j and Pinecone for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Neo4j–Pinecone connection.

Real-time

Two-way sync

Changes in Neo4j or Pinecone instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Neo4j or Pinecone data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Neo4j or Pinecone record.

Observability

Monitoring

Track your Neo4j ⇄ Pinecone sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Neo4j and Pinecone.

How the Neo4j and Pinecone connectors work

Neo4j

Integration surface
Bolt binary protocol with Cypher via official drivers, plus an HTTP query API
Authentication
Username/password (basic auth); enterprise deployments add SSO options
Change detection
Neo4j Change Data Capture on Enterprise and Aura streams graph changes; otherwise Cypher polling on timestamp properties
Capabilities
read · write · CDC

Pinecone

Integration surface
Two HTTP APIs: a control plane at api.pinecone.io (manage indexes, collections, backups, and, via the Admin API, projects and API keys) and a per-index data plane at the host returned by describe_index (upsert, query, fetch, update, delete, list). A gRPC data-plane transport is available through the official SDKs.
Authentication
API key in the Api-Key request header, scoped to one project; every request also sends an X-Pinecone-Api-Version header (date-based, e.g. 2025-10). The organization Admin API instead uses OAuth2 client-credentials (service accounts) via login.pinecone.io/oauth/token, passing a Bearer token to api.pinecone.io/admin (Enterprise).
Change detection
No webhooks and no native change-data-capture feed. Vectors carry no server-side update timestamp, so Stacksync detects changes by re-reading - paginating vector ids with the list operation (serverless indexes) and fetching by id, or by re-upserting from the source of truth. describe_index_stats bounds a resync with per-namespace counts.
Capabilities
read · write
Rate limits
Data-plane operations are capped per second: query, upsert, update, and delete at 100 req/s per namespace, fetch at 100 req/s per index, and list at 200 req/s per index; breaches return HTTP 429. Hard request limits also apply: an upsert is max 2 MB or 1000 records, metadata max 40 KB per record, and query top_k up to 10,000 with a 4 MB result cap.
How it works

How to connect Neo4j to Pinecone — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Neo4j and Pinecone with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Neo4j connected
    Pinecone connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Neo4j and Pinecone objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Neo4j ⇄ Pinecone
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Neo4j Pinecone
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Neo4j and Pinecone integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 490 integrations available for Neo4j and Pinecone.

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