AI API Gateway
Batch Processing
Complete Guide

Batch processing represents a paradigm shift from traditional real-time AI API interactions. Instead of making individual requests, applications submit batches of requests that are processed collectively, offering significant efficiency gains.

The fundamental challenge with AI APIs lies in their token-based pricing and rate limiting. Traditional approaches waste capacity during idle periods and struggle with rate limit management. Batch processing addresses these issues by aggregating requests, optimizing token usage, and implementing intelligent queuing.

Successful batch processing implementations follow several architectural patterns. The most effective approach depends on your specific requirements for latency, throughput, and fault tolerance.

Worker Queue Architecture

This pattern uses a central message queue (RabbitMQ, Apache Kafka, AWS SQS) with worker processes that consume batches of requests. Workers manage rate limits and provider-specific constraints while processing requests in parallel.

class BatchWorker:
    def __init__(self, provider, max_workers=10):
        self.provider = provider
        self.executor = ThreadPoolExecutor(max_workers)
        self.rate_limiter = TokenBucketRateLimiter(
            tokens_per_minute=provider.rate_limit
        )
    
    async def process_batch(self, requests):
        # Process requests with rate limiting
        tasks = []
        for request in requests:
            if self.rate_limiter.acquire():
                task = self.executor.submit(
                    self._process_request, request
                )
                tasks.append(task)
        
        results = await asyncio.gather(*tasks)
        return self._aggregate_results(results)

Fan-out Architecture

In this pattern, a single coordinator distributes requests across multiple specialized workers, each handling different AI providers or request types. This allows for optimal resource utilization and provider-specific optimizations.

Implementing batch processing requires careful consideration of error handling, retry logic, and result aggregation. Here's a comprehensive approach to building production-ready batch processing.

# Batch processing orchestration
class BatchOrchestrator:
    def __init__(self, batch_size=100, max_retries=3):
        self.batch_size = batch_size
        self.max_retries = max_retries
        self.request_queue = PriorityQueue()
        self.result_store = RedisStore()
    
    async def submit_batch(self, requests):
        # Validate and prioritize requests
        validated = await self._validate_requests(requests)
        prioritized = self._prioritize_by_token_cost(validated)
        
        # Process in optimal batch sizes
        batches = self._create_batches(prioritized)
        results = []
        
        for batch in batches:
            batch_result = await self._process_with_retry(batch)
            results.extend(batch_result)
            
        return self._format_response(results)
    
    async def _process_with_retry(self, batch, retry_count=0):
        try:
            return await self.gateway.process(batch)
        except RateLimitError as e:
            if retry_count < self.max_retries:
                await asyncio.sleep(e.retry_after)
                return await self._process_with_retry(batch, retry_count + 1)
            else:
                raise BatchProcessingError("Max retries exceeded")

Beyond basic batch processing, several advanced techniques can dramatically improve performance and cost-efficiency.

Token-aware Batching

Group requests by token count to maximize provider rate limits. Modern AI API gateways can estimate token usage before sending requests, allowing for optimal batch composition.

Provider-specific Optimization

Different AI providers have unique characteristics. OpenAI excels at parallel processing, while Anthropic offers better cost efficiency for certain batch sizes. Intelligent routing can leverage these differences.