I run inference APIs for models of different sizes — from tiny classification services to multi-GPU transformer endpoints — and one problem always comes up: how do I keep latency predictable without blowing the budget? Autoscaling is the obvious answer, but naïve autoscaling that only looks at CPU or request rate often leads to oscillation, over-provisioning, or surprise bills. In this guide I’ll walk you through a practical, cost-aware approach to autoscaling ML inference endpoints that balances latency SLOs, throughput needs and cloud spending.
What I mean by “cost-aware autoscaling”
When I say cost-aware, I mean autoscaling that explicitly trades off performance and dollars. Instead of blindly spinning up resources to chase a spike in QPS, a cost-aware system:
knows your latency SLOs and maximum acceptable tail latency,estimates the cost-per-inference for each resource type (CPU, GPU, spot vs on-demand),chooses scaling actions that meet SLOs at minimal incremental cost, andHigh-level architecture I use
My typical setup has four layers:
ingress/load balancer (API gateway or Envoy),a lightweight front-end service that does routing, authentication and prediction batching when helpful,a pool of inference workers (Kubernetes pods, serverless containers, or cloud model endpoints), anda monitoring and autoscaling controller that uses custom metrics and a cost model to make decisions.The key is that autoscaling decisions are driven by both performance metrics (latency, queue length, GPU utilization) and a running cost estimate for each potential scaling action.
Metrics I rely on
I prioritize these observability signals:
P95/P99 latency (inference end-to-end),queue length or in-flight request count per worker,per-inference time for cold vs warm containers,resource utilization (CPU, GPU SM utilization, memory),cost per hour and cost per inference estimates for each instance type,spot interruption rate if using spot VMs.If you use Kubernetes, record custom metrics (Kubernetes Custom Metrics API or Prometheus adapter) for queue length and cost-per-second so the autoscaler can consume them.
Autoscaling strategies I use in practice
Depending on scale and SLA, I mix these strategies:
Reactive scaling based on queue length — scale replicas when the combined queue length exceeds target capacity. This is straightforward and works well for steady growth.Latency-aware scaling — advance scaling when P95 latency approaches the SLO, using predictive smoothing to avoid thrashing.Cost-first scaling — prefer cheaper resources (smaller instances, spot VMs, serverless) up to a point and fall back to expensive resources only when latency would otherwise breach the SLO.Warm pool + scale-up concurrency — keep a small warm pool of ready workers to absorb small spikes and avoid cold start tails.Horizontal with mixed instance types — run a mix (e.g., CPU cheap nodes + GPU burst nodes) and route heavy requests to GPU nodes only when necessary.Implementing cost-aware scaling on Kubernetes
For K8s I often combine the Horizontal Pod Autoscaler (HPA) with a custom controller or KEDA. The pattern:
Expose custom metrics: queue_length_per_pod and estimated_cost_per_pod.Create HPA that targets a composite metric, e.g. desired_replicas = max(required_by_latency, required_by_queue) but constrained by a cost ceiling computed by the controller.Use a cost-aware admission controller that prevents scaling beyond budget and instead triggers alternatives (e.g., enable batching, reject low-priority requests).I use Prometheus + custom-exporter for metrics, then the Kubernetes Metrics API to feed HPA or KEDA for event-driven scaling (e.g., based on queue). If you need GPU autoscaling, use node pools: keep a small on-demand GPU node pool for critical low-latency work and a larger spot GPU pool for background or lower-priority requests.
Serverless / cloud-managed endpoints
Managed services (AWS SageMaker Endpoint, GCP Vertex AI, Azure ML) simplify the infra, but cost control still matters:
Use multi-config endpoints where small instances serve most requests and large instances are provisioned only during high load.Prefer instance types priced for inference (e.g., AWS inf1, GCP A2 with accelerators) and model-quantize to reduce instance size.For unpredictable traffic, serverless inference (AWS Lambda for tiny models, Cloud Run) reduces idle costs — but measure cold starts and runtime cost-per-inference.Practical knobs that reduce cost without sacrificing SLOs
Over the years I’ve found the following tactics to be most effective:
Batching — small batch sizes drastically improve throughput for GPU-backed models. Adaptive batching that grows with queue length is ideal.Quantization and model distillation — smaller models reduce CPU/GPU cycles and cost-per-inference.Warm pools — maintain N ready containers; tune N to expected spike size.Pre-warming during predictable spikes (cron or scheduled scale-up for business hours),Use spot/discounted instances for non-critical or best-effort traffic, with automatic fallback to on-demand when spot is interrupted or insufficient.Rate limiting + priority queues — protect SLOs for high-value traffic while allowing low-priority workloads to be delayed.Estimating cost — a small model I use
| Resource | Hourly $ | Throughput (inf/sec) | Cost per inference ($) |
|---|
| Small CPU pod | 0.10 | 5 | 0.02 |
| Large CPU pod | 0.50 | 40 | 0.0125 |
| GPU pod (spot) | 1.20 | 200 | 0.006 |
I keep this running as dynamic inputs in my autoscaler so it can evaluate "add one GPU pod reduces expected tail latency by X at incremental cost Y — approve if cost per inference remains below threshold or if SLO is at risk."
Testing and validation
Don’t trust assumptions — load test with realistic traffic (burstiness, tail latencies) and simulate failures (spot interruptions, pod evictions). I test three scenarios:
steady load at 50% capacity,sudden spike 5x for 1–5 minutes,failure of cheap pool forcing fallback to expensive nodes.Record cost, P95/P99 latency and error rate. Iterate on warm pool size, batch windows and scale thresholds.
Operational playbook I keep handy
monitor billing alerts for unexpected spend,have runbook to reduce capacity quickly (scale-to-zero or scale-to-min),automated fallback routes (send requests to simpler model or cache),regularly review model performance vs cost (quantize when beneficial).Setting up cost-aware autoscaling is more than a one-time config: it’s an ongoing process of measuring, modeling and adapting. But with the right metrics, a simple cost model and a few operational controls (batching, warm pools, spot fallback), you can keep latency tight and your cloud bill sensible — which is the sweet spot we’re all aiming for.
