Async Inference

In async inference, the client submits a request and immediately receives a job identifier or request ID, not the prediction result. The system places the request in a queue for processing. The client must later poll a separate status endpoint or provide a webhook callback URL to retrieve the completed results. This pattern is essential for models with high latency, such as large language models (LLMs) generating long text, complex vision models, or batch processing jobs that exceed typical API timeout limits.

The core infrastructure component is a durable job queue (e.g., using Redis, RabbitMQ, or cloud-native queues like Amazon SQS or Google Cloud Tasks). This queue decouples the request ingress from the actual inference workers. Key queue functions include:

• Load Leveling: Smooths traffic spikes by buffering requests.
• Retry Logic: Manages transient failures by re-queuing jobs.
• Priority Queues: Allows urgent requests to jump the line.
• Dead-Letter Queues: Captures and isolates jobs that repeatedly fail for analysis. The queue enables scalability and reliability, as inference workers can be scaled independently based on queue depth.

Completed inference results are not held in memory on the worker but are persisted to a result store, typically a fast, ephemeral datastore like Redis or an object store like Amazon S3. The result is keyed by the original job ID. This architecture allows:

• Result Durability: Predictions survive worker crashes or restarts.
• Delayed Fetching: Clients can retrieve results hours or days later.
• Multi-Client Access: Different systems can fetch the result if they have the job ID. The system must implement result expiration policies (TTL) to automatically clean up old, unretrieved results and manage storage costs.

Async inference optimizes infrastructure utilization and cost, particularly for bursty or variable workloads.

• Elastic Scaling: Inference workers (e.g., GPU instances) can be scaled to zero when the queue is empty and spun up dynamically based on queue depth, avoiding the cost of idle, expensive hardware.
• Batch Optimization: Workers can pull multiple jobs from the queue and process them in a dynamic batch on a single GPU, maximizing hardware utilization and throughput, even if requests arrived at different times.
• Spot Instance Friendly: The decoupled nature makes it ideal for using preemptible/spot cloud instances for workers, as job progress is checkpointed and can be resumed by another worker.

Async inference is not suitable for real-time user interactions but is ideal for backend processing tasks.

• Document Processing: Summarizing, translating, or extracting data from uploaded PDFs or videos.
• Content Generation: Creating long-form articles, marketing copy, or code snippets.
• Large Batch Predictions: Running inference on millions of records uploaded overnight.
• Complex Multimodal Pipelines: Processing that involves chaining multiple models (e.g., image captioning followed by sentiment analysis).
• Scientific Simulations: Running AI-driven simulations that take minutes to complete. Frameworks like Celery, Dramatiq, or cloud services like Google Cloud AI Platform Prediction with async support implement this pattern.

Understanding the trade-offs between async and online (synchronous) inference is crucial for system design.

Online Inference (Synchronous):

• Pattern: Request → Immediate Prediction → Response.
• Latency: Typically <1 second (p99).
• Use Case: Interactive applications (chatbots, real-time recommendations).
• Cost: Requires always-on endpoints, potentially underutilized.

Async Inference:

• Pattern: Request → Job ID → (Polling/Webhook) → Result.
• Latency: Seconds to hours.
• Use Case: Background jobs, batch processing, long-running tasks.
• Cost: Enables efficient, on-demand scaling; can use cheaper compute.

Hybrid systems often employ both, routing requests based on expected latency or client requirements.

Online inference (or real-time inference) is the synchronous counterpart to async inference. The client sends a request and blocks, waiting for the model's prediction to be returned with low latency (typically < 1 second). This pattern is essential for user-facing applications like chatbots, fraud detection, and recommendation engines where immediate feedback is required.

• Key Contrast: Unlike async inference, the client connection remains open until the response is delivered.
• Use Case: Interactive applications requiring sub-second responses.
• Trade-off: Requires provisioning for peak load to maintain latency SLAs, which can be less resource-efficient for variable or computationally heavy workloads.

Batch inference is an asynchronous processing pattern for generating predictions on large, accumulated datasets. Jobs are typically scheduled (e.g., nightly) or triggered by the arrival of new data, prioritizing high throughput and cost efficiency over low latency.

• Typical Workflow: Data is collected into files (e.g., Parquet, CSV), a job is submitted to a cluster, and results are written to a destination store.

• Common Use Cases: Generating personalized email recommendations, scoring large customer segments, offline model evaluation on new data, and creating training data for other models.

• Infrastructure: Often runs on managed services like AWS Batch, Kubernetes Jobs, or Spark clusters, decoupling compute from the serving API.

The job queue and polling mechanism is the core infrastructure enabling async inference. When a client submits a request, the system:

1. Enqueues a job with a unique identifier (Job ID) into a durable message queue (e.g., Apache Kafka, RabbitMQ, AWS SQS).
2. Returns the Job ID to the client immediately.
3. Worker processes consume jobs from the queue, load the model, execute inference, and store results in a database or cache.
4. The client polls a separate results endpoint with the Job ID until the prediction is ready.

This pattern decouples the request submission from execution, providing resilience and scalability.

In inference serving, throughput and latency are fundamental, often opposing, performance metrics.

• Throughput: Measures the system's capacity, defined as the number of successful inference requests processed per unit of time (e.g., Requests Per Second - RPS). Async inference systems are typically optimized for high throughput by using large batch sizes and efficient queue processing.

• Latency: Measures responsiveness, defined as the time delay between submitting a request and receiving the complete response. For async inference, this is broken into queue latency (time job waits) and processing latency (time model takes).

Optimizing one often impacts the other; async inference favors high throughput, accepting higher end-to-end latency.

Dynamic batching is a critical optimization technique used within async inference workers to improve hardware utilization (especially on GPUs). Instead of processing requests one-by-one, the inference server collects multiple pending requests from the queue and groups them into a single batch.

• How it works: A batcher waits for a short time window or until a target batch size is reached, then executes the batch through the model in parallel.
• Benefit: Dramatically increases throughput and cost-effectiveness, as GPU matrix operations are highly efficient on larger batches.
• Consideration: Introduces a trade-off between throughput and latency; longer wait times for batching increase individual request latency.

An inference endpoint is the network-accessible API (commonly REST or gRPC) that exposes a deployed model for predictions. In an async inference architecture, this is typically split into two distinct endpoints:

1. Submission Endpoint: Accepts the inference request, validates it, creates a job, and returns a Job ID.
2. Results Endpoint: Accepts a Job ID and returns the status (e.g., PENDING, SUCCESS, FAILED) and the prediction result if complete.

This endpoint abstraction provides a clean contract for clients, hiding the complexity of the underlying queue, workers, and storage systems.