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AI ⇄ Database

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

Keep Pinecone and PostgreSQL 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 Pinecone and PostgreSQL

Sync the records in PostgreSQL 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. PostgreSQL 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 PostgreSQL it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Sequences, Custom Types and Enums, Tables, Views in PostgreSQL with Collections, Backups, Index statistics, Indexes in Pinecone in real time. Rows created or changed in PostgreSQL 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 PostgreSQL, 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 PostgreSQL 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 Keep a Pinecone index aligned with a source of truth (a product catalog, knowledge base, or CRM) so new, changed, and deleted records upsert and delete the matching vectors.
  • 02 Two-way sync vector metadata between Pinecone and an operational database so filters and tags stay aligned on both sides.
  • 03 Let an application write to its own database and have those rows appear as records in business systems in near real time
  • 04 Consolidate data from several microservice databases into one operational Postgres store

Common sync patterns

Backfill once, then stay in step

Load your existing rows from PostgreSQL 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 PostgreSQL it came from, so results resolve back to the exact record with nothing orphaned or duplicated.

Run the AI on current data

Rows created or changed in PostgreSQL flow into Pinecone as they happen, so embeddings, classifications, and prompts run on the latest records instead of a nightly snapshot.

What you can sync between Pinecone and PostgreSQL

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.

Pinecone objects PostgreSQL objects How this pairing syncs
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. Custom Types and Enums Constrain synced values to a fixed set, mirroring picklist fields. Index statistics is specific to Pinecone and Custom Types and Enums to PostgreSQL — each maps to any object or custom field on the other side.
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. Tables The primary sync target; rows map one-to-one to records in connected SaaS systems. Indexes is specific to Pinecone and Tables to PostgreSQL — each maps to any object or custom field on the other side.
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. Views Read-side projections used to expose joined or filtered data to a sync. Vectors (records) is specific to Pinecone and Views to PostgreSQL — each maps to any object or custom field on the other side.
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. Materialized Views Precomputed result sets synced outward on a refresh schedule. Namespaces is specific to Pinecone and Materialized Views to PostgreSQL — each maps to any object or custom field on the other side.
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. Schemas Namespaces that scope which tables a sync reads and writes. Collections is specific to Pinecone and Schemas to PostgreSQL — each maps to any object or custom field on the other side.
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. Columns Field-level mapping targets; types are mapped to the connected system's field types. Backups is specific to Pinecone and Columns to PostgreSQL — each maps to any object or custom field on the other side.

How changes propagate between Pinecone and PostgreSQL

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.

Pinecone PostgreSQL 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 applied to PostgreSQL as a row-level write, with types converted between the two schemas.

PostgreSQL Pinecone Sub-second propagation

DetectionChanges in PostgreSQL are captured at the source via change data capture — no polling loop against its API. Logical replication (wal_level = logical) for change data capture via the "Postgres" connector.

DeliveryEach detected change is written to Pinecone 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.
  • PostgreSQL: No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput.
What ships with Pinecone ⇄ PostgreSQL

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Pinecone or PostgreSQL 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 Pinecone or PostgreSQL record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Pinecone and PostgreSQL connectors work

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.

PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL frontend/backend protocol)
Authentication
Database credentials (connection string or parameters), with optional SSL root certificate upload and optional SSH tunnel (SSH user + host); a least-privilege DB user
Change detection
Logical replication (wal_level = logical) for change data capture via the "Postgres" connector; database triggers (TRIGGER grant + stacksync_logging schema) via the trigger-based "Postgres Heroku" connector where
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput
PostgreSQL setup guide
How it works

How to connect Pinecone to PostgreSQL — 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 Pinecone and PostgreSQL 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
    Pinecone connected
    PostgreSQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Pinecone and PostgreSQL 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 · Pinecone ⇄ PostgreSQL
    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
    Pinecone PostgreSQL
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Pinecone and PostgreSQL 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.

Learn more about security
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

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 518 integrations available for Pinecone and PostgreSQL.

Popular · 8 of 518
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