LLM API Gateway Message Queue
Implement reliable message queues for LLM API workloads. Handle async processing, manage backpressure, and build fault-tolerant AI systems with enterprise-grade message brokers.
Queue Benefits
Why message queues are essential for AI APIs.
Load Leveling
Smooth out traffic spikes by buffering requests. Process at optimal rate regardless of incoming request volume.
Automatic Retries
Failed messages are automatically retried with configurable backoff policies. Never lose a request due to transient failures.
Backpressure Handling
Queue depth monitoring enables proactive scaling. Reject requests gracefully when system is at capacity.
Durable Storage
Messages persist across restarts and failures. Zero data loss even during broker outages.
Horizontal Scaling
Add more consumers to increase throughput. Scale workers independently from API endpoints.
Priority Queuing
Process high-priority requests first. Different SLA tiers for different customer segments.
Message Queue Providers
Choose the right broker for your AI workloads.
- Advanced routing rules
- Message acknowledgment
- Dead letter exchanges
- Priority queues
- Event sourcing
- Stream processing
- High throughput
- Message replay
- Fully managed
- Unlimited queues
- FIFO support
- Lambda integration
- Sub-millisecond latency
- Consumer groups
- Persistence options
- Simple API
- Global scaling
- Exactly-once delivery
- Schema registry
- BigQuery integration
- Topic subscriptions
- Scheduled delivery
- Dead letter queue
- Geo-disaster recovery
Implementation Example
Build a queue-based LLM gateway.
class QueueBasedGateway: """Message queue based LLM API gateway""" def __init__(self, queue_client, result_store): self.queue = queue_client self.results = result_store async def enqueue_request( self, request: LLMRequest ) -> str: """Enqueue LLM request for async processing""" # Generate request ID request_id = str(uuid.uuid4()) # Create queue message message = { 'id': request_id, 'prompt': request.prompt, 'model': request.model, 'params': request.params, 'priority': request.priority, 'timestamp': datetime.utcnow().isoformat() } # Determine queue based on priority queue_name = 'llm.high' if request.priority == 'high' else 'llm.normal' # Publish to queue await self.queue.publish( queue=queue_name, message=json.dumps(message), headers={ 'model': request.model, 'priority': request.priority } ) return request_id async def get_result( self, request_id: str ) -> Optional[dict]: """Check for completion result""" return await self.results.get(request_id) class QueueWorker: """Worker that processes LLM requests from queue""" def __init__(self, queue_client, llm_client, result_store): self.queue = queue_client self.llm = llm_client self.results = result_store self.running = True async def start(self, queues: List[str]): """Start consuming from queues""" for queue_name in queues: asyncio.create_task( self.consume_queue(queue_name) ) async def consume_queue(self, queue_name: str): """Consume messages from a queue""" while self.running: try: # Fetch message message = await self.queue.fetch( queue=queue_name, timeout=5 ) if not message: continue # Process request request = json.loads(message.body) result = await self.llm.complete( prompt=request['prompt'], model=request['model'], **request['params'] ) # Store result await self.results.set( key=request['id'], value={ 'status': 'completed', 'response': result, 'completed_at': datetime.utcnow().isoformat() }, ttl=3600 ) # Acknowledge message await message.ack() except Exception as e: logger.error(f"Error processing: {e}") await message.nack(requeue=True)