Skip to content
Database ⇄ AI

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

Keep AWS Aurora PostgreSQL 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.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

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

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect AWS Aurora PostgreSQL and Pinecone

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

Stacksync syncs Primary keys and constraints, Views and materialized views, Foreign keys, Replication slots and publications in AWS Aurora PostgreSQL with Namespaces, Collections, Backups, Index statistics in Pinecone in real time. Rows created or changed in AWS Aurora 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 AWS Aurora 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 AWS Aurora 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 Expose ERP records such as customers, orders, and invoices as Postgres tables the engineering team can query and update with plain SQL.
  • 04 Capture row-level changes with logical replication and propagate them to SaaS tools without batch jobs.

Common sync patterns

Write results back onto the record

Scores, labels, extracted fields, or generated text produced in Pinecone land on the matching row in AWS Aurora PostgreSQL, next to the source data your applications already query.

Keep derived data fresh as sources change

When a row in AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL into Pinecone to build the index or enrichment set, then let ongoing changes sync automatically instead of re-running the whole job.

What you can sync between AWS Aurora PostgreSQL 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.

AWS Aurora PostgreSQL objects Pinecone objects How this pairing syncs
Tables The core sync unit; rows are matched across systems by primary key. 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 is specific to AWS Aurora PostgreSQL and Indexes to Pinecone — each maps to any object or custom field on the other side.
Rows Inserted, updated, and deleted in both directions during bi-directional syncs. 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. Rows is specific to AWS Aurora PostgreSQL and Vectors (records) to Pinecone — each maps to any object or custom field on the other side.
Columns Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. 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. Columns is specific to AWS Aurora PostgreSQL and Namespaces to Pinecone — each maps to any object or custom field on the other side.
Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. 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. Primary keys and constraints is specific to AWS Aurora PostgreSQL and Collections to Pinecone — each maps to any object or custom field on the other side.
Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. 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. Views and materialized views is specific to AWS Aurora PostgreSQL and Backups to Pinecone — each maps to any object or custom field on the other side.
Foreign keys Relationship metadata that syncs can translate into object references elsewhere. 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. Foreign keys is specific to AWS Aurora PostgreSQL and Index statistics to Pinecone — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora PostgreSQL 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.

AWS Aurora PostgreSQL Pinecone Sub-second propagation

DetectionChanges in AWS Aurora PostgreSQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback.

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

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

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 AWS Aurora PostgreSQL ⇄ Pinecone

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

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

Observability

Monitoring

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

Trading partners

EDI

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

How the AWS Aurora PostgreSQL and Pinecone connectors work

AWS Aurora PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
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 AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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
    AWS Aurora PostgreSQL connected
    Pinecone connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora PostgreSQL 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 · AWS Aurora PostgreSQL ⇄ 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
    AWS Aurora PostgreSQL Pinecone
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

AWS Aurora PostgreSQL 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.

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 425 integrations available for AWS Aurora PostgreSQL and Pinecone.

Popular · 7 of 425
All AWS Aurora PostgreSQL integrations All Pinecone integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own