AI API Proxy Pub/Sub Pattern
Implement publish-subscribe messaging for AI services. Decouple producers from consumers, build real-time event pipelines, and enable fan-out architectures for scalable AI operations.
Pub/Sub Benefits
Advantages of publish-subscribe for AI systems.
Loose Coupling
Publishers and subscribers operate independently. Add new subscribers without modifying publishers or existing consumers.
Fan-Out Broadcasting
One message reaches multiple subscribers simultaneously. Perfect for analytics, logging, and notifications.
Asynchronous Processing
Publishers continue immediately after publishing. Subscribers process at their own pace without blocking.
Independent Scaling
Scale subscribers independently based on processing needs. Different consumers can have different throughput requirements.
Fault Isolation
Subscriber failures don't affect publishers or other subscribers. Each consumer handles its own errors.
Flexible Routing
Route events based on topics, patterns, or content. Implement complex event flows with simple subscriptions.
Pub/Sub Topics
Common topics for AI API proxy systems.
ai.requests
Published when a new AI request is received through the gateway. Contains request details and metadata.
- Rate Limiter Service
- Request Logger
- Cost Tracker
- Analytics Pipeline
ai.completions
Published when an AI request completes successfully. Contains response data and performance metrics.
- Response Cache
- Billing Service
- Quality Monitor
- WebSocket Gateway
ai.errors
Published when an AI request fails. Contains error details for monitoring and retry processing.
- Error Tracker (Sentry)
- Alert System
- Dead Letter Queue
- Root Cause Analysis
ai.metrics
Periodic metrics about API usage, performance, and resource consumption for monitoring.
- Time-Series Database
- Dashboard Service
- Auto-Scaler
- Cost Reporter
Implementation Guide
Build a pub/sub enabled AI gateway.
class PubSubGateway: """Pub/Sub enabled AI API gateway""" def __init__(self, broker_client): self.broker = broker_client self.handlers = {} async def publish( self, topic: str, event: dict, headers: dict = None ): """Publish event to topic""" await self.broker.publish( topic=topic, message=json.dumps(event), headers=headers or {} ) async def subscribe( self, topic: str, subscriber_name: str, handler: callable ): """Subscribe to topic with handler""" self.handlers[subscriber_name] = handler await self.broker.subscribe( topic=topic, subscription=subscriber_name, callback=self._create_callback(subscriber_name) ) def _create_callback(self, name): async def callback(message): try: event = json.loads(message.data) await self.handlers[name](event) await message.ack() except Exception as e: await message.nack() logger.error(f"Handler {name} failed: {e}") return callback # Usage example async def setup_gateway(): gateway = PubSubGateway(GooglePubSubClient()) # Subscribe to completion events async def handle_completion(event): # Cache the response await cache.set( event['request_id'], event['response'] ) await gateway.subscribe( topic='ai.completions', subscriber_name='cache-service', handler=handle_completion ) # Subscribe to error events async def handle_error(event): # Send alert await alert_service.send( f"AI Error: {event['error']}" ) await gateway.subscribe( topic='ai.errors', subscriber_name='alert-service', handler=handle_error ) return gateway # Publisher example async def process_request(gateway, request): # Publish request event await gateway.publish( topic='ai.requests', event={ 'request_id': request.id, 'model': request.model, 'timestamp': datetime.utcnow().isoformat() } ) # Process AI request result = await ai_client.complete(request) # Publish completion event await gateway.publish( topic='ai.completions', event={ 'request_id': request.id, 'response': result, 'latency_ms': result.latency } )