Real-time
Real-time sync
Changes in Azure OpenAI or Scaleway Postgres instantly reflect in both systems. No stale data, no manual imports.
AI ⇄ Database
Azure OpenAI to Scaleway Postgres integration — real-time data sync
Keep Azure OpenAI 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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Why teams connect Azure OpenAI and Scaleway Postgres
Sync the records in Scaleway Postgres into Azure OpenAI and land its embeddings, classifications, and generated fields back on the same rows, in real time and without a pipeline to maintain.
Azure OpenAI is a read-only source: Stacksync reads its data in real time and delivers it into Scaleway Postgres, so Scaleway Postgres always reflects the current state of Azure OpenAI — without exports, scripts, or schedulers.
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 Azure OpenAI 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 Schemas, Sequences, Columns, Tables in Scaleway Postgres with Vector stores, Deployments, Models, Fine-tuning jobs in Azure OpenAI in real time. Rows created or changed in Scaleway Postgres flow into Azure OpenAI so inference and embedding run on current data, and the scores, labels, and generated fields Azure OpenAI 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 Azure OpenAI. 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 Pull per-deployment TPM/RPM usage into a warehouse for FinOps chargeback and quota-exhaustion alerting.
- 02 Mirror Assistants and Vector stores configuration into a database as an auditable inventory of retrieval assets and their linked files.
- 03 Run a two-way sync between Scaleway Postgres and SaaS tools so edits made in either system converge on the same rows
- 04 Replicate the application database into a warehouse for analytics without writing custom pipelines
Common sync patterns
Keep derived data fresh as sources change
When a row in Scaleway Postgres is updated or removed, its counterpart in Azure OpenAI is updated or removed too, so nothing in Azure OpenAI describes a record that has since changed or gone.
Backfill once, then stay in step
Load your existing rows from Scaleway Postgres into Azure OpenAI 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 Azure OpenAI 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.
What you can sync between Azure OpenAI 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.
| Azure OpenAI objects | Scaleway Postgres objects | How this pairing syncs | |
|---|---|---|---|
| Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. | Schemas Namespace tables so multiple applications or environments can be synced selectively. | Assistants is specific to Azure OpenAI and Schemas to Scaleway Postgres — each maps to any object or custom field on the other side. | |
| Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. | Sequences Generate primary keys; sync tooling must respect them when writing rows. | Vector stores is specific to Azure OpenAI and Sequences to Scaleway Postgres — each maps to any object or custom field on the other side. | |
| Deployments Named model deployments (model, version, SKU, assigned TPM capacity) read as a control-plane inventory via Azure Resource Manager; read-only in sync. | Columns Postgres-native types, including JSONB and arrays, are mapped to fields in the paired system. | Deployments is specific to Azure OpenAI and Columns to Scaleway Postgres — each maps to any object or custom field on the other side. | |
| Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. | Tables Primary sync unit; each table maps to an object or table on the other side of the sync. | Models is specific to Azure OpenAI and Tables to Scaleway Postgres — each maps to any object or custom field on the other side. | |
| Fine-tuning jobs Training jobs with status, base model, hyperparameters, and result files; status is polled from queued through succeeded or failed. | Views Read-only sources for shaping data before it leaves the database. | Fine-tuning jobs is specific to Azure OpenAI and Views to Scaleway Postgres — each maps to any object or custom field on the other side. | |
| Files Uploaded training, validation, and batch-input files plus generated output files; listed and read by ID, not written back in sync. | Materialized views Precomputed result sets that can be read on a schedule for downstream syncs. | Files is specific to Azure OpenAI and Materialized views to Scaleway Postgres — each maps to any object or custom field on the other side. |
How changes propagate between Azure OpenAI 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.
Azure OpenAI Scaleway Postgres Interval-based propagation
DetectionStacksync polls Azure OpenAI for changes on an incremental schedule, reading only records changed since the previous pass. Polling: list endpoints plus GET on job IDs for status.
DeliveryEach detected change is applied to Scaleway Postgres as a row-level write, with types converted between the two schemas.
Scaleway Postgres Azure OpenAI 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.
DeliveryAzure OpenAI does not accept inbound record writes, so this direction carries requests rather than records: Azure OpenAI's output flows back as field updates on the originating Scaleway Postgres records.
Rate-limit considerations
- Azure OpenAI: Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.
- Scaleway Postgres: Throughput is bounded by instance size and connection limits rather than API quotas.
What ships with Azure OpenAI ⇄ Scaleway Postgres
Connect Azure OpenAI and Scaleway Postgres for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Azure OpenAI–Scaleway Postgres connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Azure OpenAI 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 Azure OpenAI or Scaleway Postgres record.
Observability
Monitoring
Track your Azure OpenAI ⇄ 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 Azure OpenAI and Scaleway Postgres.
How the Azure OpenAI and Scaleway Postgres connectors work
Azure OpenAI
- Integration surface
- REST data-plane (inference + authoring) and Azure Resource Manager control-plane
- Authentication
- API key in the api-key header, or a Microsoft Entra ID bearer token / managed identity
- Change detection
- Polling: list endpoints plus GET on job IDs for status; no webhooks or change feed. Fine-tuning and batch jobs expose queued/running/succeeded states.
- Capabilities
- read
- Rate limits
- Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.
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 Azure OpenAI 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 Azure OpenAI 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.
Azure OpenAI
connected
Scaleway Postgres
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Azure OpenAI 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 · Azure OpenAI ⇄ 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.
Azure OpenAI Scaleway PostgresCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Azure OpenAI and Scaleway Postgres integration FAQ
-
Yes. Stacksync provides a managed, real-time integration between Azure OpenAI and Scaleway Postgres — Azure OpenAI is a read-only source, so data flows from it into the other system: authenticate both systems, choose the objects to sync, map fields visually, and changes propagate in milliseconds — no code required.
-
Yes — Stacksync ships production-grade connectors for both Azure OpenAI 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 Azure OpenAI: Polling: list endpoints plus GET on job IDs for status; no webhooks or change feed. Fine-tuning and batch jobs expose queued/running/succeeded states. 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.
-
On the Azure OpenAI side: Vector stores, Deployments, Models, Fine-tuning jobs, plus custom fields where Azure OpenAI exposes them. On the Scaleway Postgres side: Schemas, Sequences, Columns, Tables. Stacksync auto-detects both schemas and converts types between the two systems.
-
Azure OpenAI is a read-only source, so this integration runs one-way: Stacksync reads from Azure OpenAI in real time and delivers into Scaleway Postgres. Field mapping and monitoring work the same as for two-way pairs.
-
Common patterns for Azure OpenAI and Scaleway Postgres: Keep derived data fresh as sources change; Backfill once, then stay in step; One record, one identifier. When a row in Scaleway Postgres is updated or removed, its counterpart in Azure OpenAI is updated or removed too, so nothing in Azure OpenAI describes a record that has since changed or gone.
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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ISO 27001
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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 Azure OpenAI and Scaleway Postgres.
Popular · 7 of 416
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Azure OpenAI ⇄ CockroachDB -
Azure OpenAI ⇄ Google AlloyDB -
Azure OpenAI ⇄ Dynamo DB -
Azure OpenAI ⇄ IBM Db2 -
Scaleway Postgres ⇄ Pinecone -
Scaleway Postgres ⇄ Openai -
Scaleway Postgres ⇄ Autopilot
No integrations match .
