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AI ⇄ Database

Azure OpenAI to Neo4j integration — real-time data sync

Keep Azure OpenAI and Neo4j 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 Neo4j

Sync the records in Neo4j 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 Neo4j, so Neo4j 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. Neo4j 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 Neo4j it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Databases, Users & Roles, Nodes, Relationships in Neo4j with Fine-tuning jobs, Files, Batch jobs, Usage and quota in Azure OpenAI in real time. Rows created or changed in Neo4j 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 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 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 Sync the Deployments inventory (model, version, TPM capacity) into Postgres so platform teams track every Azure OpenAI deployment across subscriptions in SQL.
  • 02 Land Fine-tuning jobs with their status, base model, and result Files in a warehouse to power MLOps dashboards without per-viewer API calls.
  • 03 Feed identity and access data into a graph for entitlement and blast-radius analysis.
  • 04 Write computed relationship scores (fraud, influence, similarity) back to operational systems.

Common sync patterns

Backfill once, then stay in step

Load your existing rows from Neo4j 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 Neo4j 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 Neo4j flow into Azure OpenAI as they happen, so embeddings, classifications, and prompts run on the latest records instead of a nightly snapshot.

What you can sync between Azure OpenAI and Neo4j

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 Neo4j objects How this pairing syncs
Usage and quota Per-deployment TPM/RPM consumption and remaining quota, read from usage endpoints and Azure Monitor for cost and throttling reporting. Relationships Typed, directed edges that carry the connections syncs exist to model. Usage and quota is specific to Azure OpenAI and Relationships to Neo4j — each maps to any object or custom field on the other side.
Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. Properties Key-value attributes on both nodes and relationships, mapped from source fields. Assistants is specific to Azure OpenAI and Properties to Neo4j — 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. Labels Node type markers used to map source tables or objects onto the graph. Vector stores is specific to Azure OpenAI and Labels to Neo4j — 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. Indexes & Constraints Uniqueness constraints and indexes that make MERGE-based upserts reliable and fast. Deployments is specific to Azure OpenAI and Indexes & Constraints to Neo4j — 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. Databases Named databases in a single instance that scope multi-tenant or multi-domain syncs. Models is specific to Azure OpenAI and Databases to Neo4j — 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. Users & Roles Security principals controlling what an integration credential can query or modify. Fine-tuning jobs is specific to Azure OpenAI and Users & Roles to Neo4j — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and Neo4j

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

Neo4j Azure OpenAI 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.

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 Neo4j 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.
What ships with Azure OpenAI ⇄ Neo4j

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Azure OpenAI or Neo4j 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 Neo4j record.

Observability

Monitoring

Track your Azure OpenAI ⇄ Neo4j 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 Neo4j.

How the Azure OpenAI and Neo4j 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.

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
How it works

How to connect Azure OpenAI to Neo4j — 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 Azure OpenAI and Neo4j 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
    Azure OpenAI connected
    Neo4j connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Azure OpenAI and Neo4j 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 · Azure OpenAI ⇄ Neo4j
    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
    Azure OpenAI Neo4j
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Azure OpenAI and Neo4j 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 Azure OpenAI and Neo4j.

Popular · 8 of 490
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