Building a Scalable, Event-Driven Image Generation Notification System

Introduction

AI-powered image generation platforms are transforming the way we create visual content, from concept art to product mockups. However, beneath the surface of artistic creation lies a complex backend architecture that ensures tasks are processed reliably, users are updated in real time, and the system remains resilient under heavy load.

One key challenge in such a platform is notifying users and systems when image generation tasks are completed. This blog post walks through the design of a generic, cloud-agnostic publish-subscribe (Pub/Sub) system that handles image generation completion events and distributes them to various consumers, such as user interfaces, analytics pipelines, and notification services.

Whether you're building your own image generation SaaS product or adding generation workflows to a larger system, this architecture is designed to scale, fail gracefully, and keep all stakeholders informed in real time.

✅ System Goals

Before diving into the architecture, let’s clarify the core goals:

• Scalability: Handle thousands of generation jobs per second without bottlenecks.

• Real-Time Feedback: Notify users or systems the moment a job is complete.

• Loose Coupling: Decouple components to isolate failures and enable independent scaling.

• Fault Tolerance: Provide strong retry mechanisms and fallback paths for failures.

• Extensibility: Support new notification channels (e.g., webhooks, mobile push) with minimal effort.

🧱 High-Level Architecture

The architecture consists of two major pipelines:

1. Image Generation Pipeline – Handles job intake and generation.

2. Notification Delivery Pipeline – Distributes completion updates to consumers.

📌 System Architecture Diagram

1. The Producer Pipeline (Image Generation Flow)

When a user initiates an image generation request, the system ingests it and passes it through a series of modular components:

🔹 API Layer → Request Queue

• The frontend or an API sends a generation request to a message queue.

• This queue acts as a buffer, decoupling request ingestion from image generation.

• Benefits:

  • Handles load spikes without losing data.

  • Supports retries, message ordering (if needed), and backpressure handling.

🔹 Image Generation Worker

• A worker service consumes messages from the request queue.

• It performs the image generation task using a backend engine (e.g., diffusion models, GANs).

• Once completed, it emits a completion event to the notification system.

🔁 Error Handling & Dead Letter Queues (DLQs)

• If the generation task fails, the system retries it a fixed number of times.

• Persistent failures are routed to a DLQ where they can be reviewed or reprocessed.

• This ensures that transient issues (e.g., GPU timeouts) don’t cause data loss or manual intervention.

2. The Consumer Pipeline (Notification Flow)

Once a generation job is completed, the notification pipeline takes over:

🔹 Event Broadcast (Pub/Sub)

• The event is broadcast using a publish-subscribe mechanism.

• All interested subscribers (e.g., UI, email service, analytics engine) receive a copy of the completion event.

• Advantages:

  • Fully decouples producers and consumers.

  • Makes it easy to add new subscribers (e.g., Discord bots, customer webhooks) without changing upstream logic.

🔹 Notification Queue & Dispatcher

• A second queue buffers notifications for downstream delivery.

• A dispatcher service polls this queue and sends updates via:

  • Email (transactional or marketing)

  • SMS or mobile push

  • WebSockets (for real-time frontend updates)

  • Webhooks (e.g., Zapier, Discord, internal tools)

📌 Event Flow Diagram

📦 Message Format and Schema

All communication in this architecture happens via well-defined, versioned JSON messages.

Generation Request Message

{   
"generationRequestId": "string",
"userId": "string",
"prompt": "string" 
}

Generation Completion Event

{
"generationRequestId": "string",
"status": "string",
"imagesUrls":[
    { "url": "string" },
    { "url": "string" }   
]
}

These schemas ensure service interoperability and make the system easier to debug and extend.

🔁 Error Handling Strategy

Failures in distributed systems are inevitable. This architecture includes multiple layers of protection:

1. Retry Mechanisms

• Tasks that fail due to temporary issues are retried automatically.

• Visibility timeouts prevent duplicate processing.

• Exponential backoff avoids overload from rapid-fire retries.

2. Dead Letter Queues (DLQ)

• Messages that fail repeatedly are routed to DLQs.

• These can be analyzed manually or handled by separate diagnostic tools.

3. Idempotency

• To avoid duplicate notifications or image generations, use unique identifiers (e.g., generationRequestId) for each task.

4. Monitoring and Alerts

• Metrics and logs are collected to track:

  • Queue depths

  • Error rates

  • DLQ entries

• Alerting systems notify engineers when thresholds are exceeded.

⚙️ Scaling Considerations

The architecture is inherently scalable due to its decoupled, event-driven nature.

• Horizontal Scaling: Workers and dispatchers can scale based on queue depth or load.

• Elastic Infrastructure: Serverless or containerized functions can spin up based on demand.

• Provisioning Control: Use concurrency limits and backoff to manage resource costs under high loads.

🔒 Security Best Practices

Security is woven into every layer of the system:

• Access Control: Use role-based access for components. Each part should only access what it needs.

• Encryption:

  • In transit: Secure all API and inter-service communication using TLS.

  • At rest: Encrypt queues and logs to protect sensitive data.

• Webhook Signing: Outgoing webhook notifications should be cryptographically signed to prevent spoofing.

🧩 Extending the Architecture

The architecture is modular and adaptable:

• Add new consumers (e.g., analytics, CRM) by subscribing them to the event bus.

• Support new delivery channels (e.g., mobile push, in-app banners).

• Integrate schema registries to manage evolving message formats.

• Introduce circuit breakers for external systems to prevent cascading failures.

🆚 Why This Design Works (Trade-offs Considered)

Conclusion

Building a modern, reliable image generation system isn’t just about powerful AI models. It requires a thoughtfully designed backend that can scale elastically, fail safely, and deliver real-time updates to users and services.

By leveraging standard patterns—queues, workers, pub/sub, and retries—you can design a robust architecture that performs under pressure and adapts as your platform grows.

Whether you're building a startup product or a large-scale image generation engine, this architecture lays the groundwork for operational excellence and an outstanding user experience.

✉️ Subscribe for more builds like this

📬 Join hundreds of devs mastering software engineering by building, breaking, and shipping real software.