What you will learn
- How batching and caching cut inference cost
- How autoscaling differs for GPU and CPU models
- The build order from one endpoint to a multi-model service
- Why latency, cost, and accuracy trade against each other in serving
What this project is
Training gets the attention, but inference pays the ongoing bill. Every user request runs the model, and at scale that means GPUs, queues, and latency budgets that never sleep. A serving platform turns a model file into an endpoint with predictable latency, measured cost, and a way to scale when traffic jumps.
The central tension is latency versus utilisation. GPUs are expensive idle and slow when overloaded, so the platform must batch requests, cache repeated work, and scale before queues grow, not after users notice.
Core features
- Model endpoints with versioning, traffic splitting, and rollback to a previous version.
- Dynamic batching that groups concurrent requests without breaching latency budgets.
- GPU autoscaling based on queue depth and latency, not just CPU-style utilisation.
- Response caching for repeated prompts and embeddings where correctness allows it.
- Multi-model serving with per-model resource limits so one model cannot starve others.
- Usage metering and cost views per model, per customer, and per endpoint.
How it is built
- One endpoint first. A single model, batching, and latency measurement under real traffic patterns.
- Autoscaling second. Queue-based scaling and load testing before promising uptime.
- Multi-model third. Isolation, limits, and per-model metering for a shared service.
- Caching and optimisation fourth. Quantisation, caching, and batch tuning where they preserve quality.
- Operations last. Alerts, capacity planning, and cost controls for production scale.
Technology considerations
Serving is a systems problem: request queues, GPU scheduling, and network paths decide latency more than model code. Batching must respect per-request deadlines, or throughput gains arrive as user-visible delay. Quantisation and smaller model variants can cut cost sharply, but they change outputs, so quality checks must gate any optimisation. GPU hosting costs dominate; confirm with a quote for expected traffic and model sizes.
- A serving runtime with batching, queues, and deadline-aware scheduling.
- GPU autoscaling driven by queue depth and latency signals.
- Per-model resource limits and usage metering.
- Caching layers for prompts, embeddings, and repeated requests.
Challenges to plan for
- Cold starts. A scaled-to-zero model takes seconds to load, so warm pools or predictive scaling matter for latency-sensitive endpoints.
- Noisy neighbours. One heavy model can starve others on shared GPUs, so isolation and limits are essential.
- Optimisation drift. Quantised models behave differently, and unmeasured optimisation becomes a silent quality regression.
- Cost spikes. Traffic bursts on large models scale GPU bills fast, so caps and alerts must exist before the first spike.
A worked example
A photo-editing app adds an AI background-removal feature that spikes every evening when users finish work, and the team needs latency under two seconds without paying for idle GPUs all day. The serving platform runs the model behind an endpoint with dynamic batching, queue-based GPU autoscaling, and a warm minimum pool sized for evening peaks. Repeated requests for the same popular templates hit a response cache, cutting GPU work further.
During a holiday spike, traffic triples in an hour. Queue depth triggers scaling before latency breaches the budget, and per-model resource limits stop the background-removal model from starving the app's smaller upscaling model on shared hardware. Cost per thousand requests is tracked daily, and when a quantised model variant passes quality checks, it ships to a traffic slice first, cutting cost per request without a visible quality change. The team sleeps through the spike, which is the real test of a serving platform.
Who it is for, and its limits
This fits teams serving models to users at volume, or vendors offering inference as a service across many models.
It does not fit a demo with occasional traffic, where a single hosted endpoint is cheaper. It also does not improve model quality; it serves the model you have, faster and cheaper.
Frequently asked questions
What is dynamic batching?
Grouping concurrent requests into one model run to use the GPU efficiently, while keeping each request inside its latency budget.
Should inference scale to zero?
For rarely used models, yes, if cold-start delay is acceptable. For latency-sensitive endpoints, a warm minimum is worth the cost.
Does quantisation hurt quality?
It can, depending on model and task, so quantised versions need quality checks against the original before serving.
How is inference cost controlled?
Through caching, batching, autoscaling, per-customer caps, and metering that shows cost per model and endpoint.
Can one platform serve many model types?
Yes with per-model runtimes and resource limits, though very different models may need separate serving pools.
How do you handle traffic spikes without overpaying for idle GPUs?
With queue-based autoscaling, a warm minimum pool for predictable peaks, batching, and caching, so capacity follows demand instead of sitting idle or lagging behind.