Real-time
Two-way sync
Changes in Pinecone or Scaleway Postgres instantly reflect in both systems. No stale data, no manual imports.
AI ⇄ Database
Pinecone to Scaleway Postgres integration — real-time, two-way sync
Keep Pinecone and Scaleway Postgres 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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Migrated from MuleSoft
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Pinecone and Scaleway Postgres
Sync the records in Scaleway Postgres 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. Scaleway Postgres 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 Scaleway Postgres it describes are two halves of the same thing, and they drift the moment one is updated without the other.
Stacksync syncs Sequences, Columns, Tables, Views in Scaleway Postgres with Namespaces, Collections, Backups, Index statistics in Pinecone in real time. Rows created or changed in Scaleway Postgres 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 Scaleway Postgres, 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 Scaleway Postgres 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 Migrate or clone vectors between Pinecone indexes, projects, or namespaces - or from another vector store into Pinecone - using batched upserts.
- 02 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.
- 03 Replicate the application database into a warehouse for analytics without writing custom pipelines
- 04 Back internal tools with the same Postgres instance while Stacksync keeps it consistent with external systems
Common sync patterns
Backfill once, then stay in step
Load your existing rows from Scaleway Postgres 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 Scaleway Postgres 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 Scaleway Postgres 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 Scaleway Postgres
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 | Scaleway Postgres objects | How this pairing 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. | Columns Postgres-native types, including JSONB and arrays, are mapped to fields in the paired system. | Backups is specific to Pinecone and Columns to Scaleway Postgres — each maps to any object or custom field on the other side. | |
| 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. | Tables Primary sync unit; each table maps to an object or table on the other side of the sync. | Index statistics is specific to Pinecone and Tables to Scaleway Postgres — 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. | Views Read-only sources for shaping data before it leaves the database. | Indexes is specific to Pinecone and Views to Scaleway Postgres — 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. | Materialized views Precomputed result sets that can be read on a schedule for downstream syncs. | Vectors (records) is specific to Pinecone and Materialized views to Scaleway Postgres — 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. | Schemas Namespace tables so multiple applications or environments can be synced selectively. | Namespaces is specific to Pinecone and Schemas to Scaleway Postgres — 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. | Sequences Generate primary keys; sync tooling must respect them when writing rows. | Collections is specific to Pinecone and Sequences to Scaleway Postgres — each maps to any object or custom field on the other side. |
How changes propagate between Pinecone and Scaleway Postgres
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 Scaleway Postgres 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 Scaleway Postgres as a row-level write, with types converted between the two schemas.
Scaleway Postgres Pinecone Sub-second propagation
DetectionChanges in Scaleway Postgres are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication where the managed instance permits it.
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.
- Scaleway Postgres: Throughput is bounded by instance size and connection limits rather than API quotas.
What ships with Pinecone ⇄ Scaleway Postgres
Connect Pinecone and Scaleway Postgres for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Pinecone–Scaleway Postgres connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Pinecone or Scaleway Postgres 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 Scaleway Postgres record.
Observability
Monitoring
Track your Pinecone ⇄ Scaleway Postgres 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 Scaleway Postgres.
How the Pinecone and Scaleway Postgres 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.
Scaleway Postgres
- Integration surface
- SQL wire protocol (PostgreSQL)
- Authentication
- Database credentials (username/password over TLS)
- Change detection
- Log-based CDC via PostgreSQL logical replication where the managed instance permits it; otherwise timestamp or query-based polling
- Capabilities
- read · write · CDC
- Rate limits
- Throughput is bounded by instance size and connection limits rather than API quotas
How it works
How to connect Pinecone to Scaleway Postgres — 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.
- 01
Connect your apps
Authenticate Pinecone and Scaleway Postgres with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Pinecone
connected
Scaleway Postgres
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Pinecone and Scaleway Postgres 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.
objects · Pinecone ⇄ Scaleway PostgresCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Pinecone Scaleway PostgresCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Pinecone and Scaleway Postgres integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Pinecone and Scaleway Postgres: authenticate both systems, choose the objects to sync (such as Pinecone's Backups and Index statistics), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes — Stacksync ships production-grade connectors for both Pinecone and Scaleway Postgres. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Pinecone: 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. On Scaleway Postgres: Log-based CDC via PostgreSQL logical replication where the managed instance permits it; otherwise timestamp or query-based polling. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Pinecone side: Namespaces, Collections, Backups, Index statistics, plus custom fields where Pinecone exposes them. On the Scaleway Postgres side: Sequences, Columns, Tables, Views. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for Pinecone and Scaleway Postgres: Backfill once, then stay in step; One record, one identifier; Run the AI on current data. Load your existing rows from Scaleway Postgres into Pinecone to build the index or enrichment set, then let ongoing changes sync automatically instead of re-running the whole job.
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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 416 integrations available for Pinecone and Scaleway Postgres.
Popular · 7 of 416
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Pinecone ⇄ Render Postgres -
Pinecone ⇄ PostgreSQL -
Pinecone ⇄ Amazon RDS -
Pinecone ⇄ Supabase -
Scaleway Postgres ⇄ Openai -
Scaleway Postgres ⇄ Azure OpenAI -
Scaleway Postgres ⇄ Autopilot
No integrations match .
