How to Optimize SageMaker and Vertex AI Endpoint Costs

Last updated: June 2026

A managed inference endpoint is a hosted model behind an API that runs on a provisioned instance you pay for by the hour. On Amazon SageMaker and Google Vertex AI, that instance bills continuously while the endpoint is up, whether traffic is heavy, light, or zero. So the cost of a real-time endpoint is set less by how many inferences you serve and more by how much capacity you leave running idle. Optimizing endpoints means making provisioned capacity follow real demand: smaller instances, autoscaling, scale-to-zero for bursty traffic, shared hosting, and a commitment only on the part of the load that is genuinely always on.

This article is part of our AI, GPU and ML cluster. For the full picture start with the complete guide to AI and GPU cost optimization, the pillar this piece links up to. The instance-selection work here pairs closely with how to right-size GPU instances for model serving.

Why are SageMaker and Vertex AI endpoints so expensive?

Real-time endpoints are expensive because they bill for the underlying instance every hour it is provisioned, not per inference. A GPU endpoint kept running around the clock for a feature used a few hours a day spends most of its budget on idle accelerator time. The pattern repeats across teams: a model is deployed to its own always-on endpoint, traffic is intermittent, and nobody scales it down. Multiply that by dozens of models and the inference bill is mostly paid-for idle capacity. The fix is structural, not a per-request optimization. AWS documents the real-time, asynchronous, and serverless options in the SageMaker model deployment guide, and Google covers prediction modes in the Vertex AI predictions documentation.

Step 1: How do I measure cost per endpoint and per request?

Attribute spend to each endpoint and divide by its request volume before changing anything, because you cannot cut what you cannot see. Tag every endpoint with its owning team and feature, pull instance-hours and request counts from Cost Explorer or Cloud Billing export, and compute cost per thousand requests per endpoint. The endpoints with high cost per request, or high cost and near-zero requests, are your targets. This measurement is the See step and the baseline you will judge every later change against.

Step 2: How do I right-size the instance behind each endpoint?

Right-sizing means matching the instance type and accelerator to the model's real memory and latency needs instead of defaulting to the largest GPU. Profile the model's memory footprint and latency target, then load test the smallest instance and accelerator that meets the SLA at expected concurrency. Many models deployed on large GPUs run within latency on a smaller GPU, and quantized models often run on CPU. Keep the cheapest instance that holds the latency target under realistic load. This is the highest-value change after eliminating idle endpoints.

Step 3: How do I turn on autoscaling tied to real traffic?

Configure target-tracking autoscaling so instance count follows request load rather than running peak capacity all day. On SageMaker, attach an Application Auto Scaling policy keyed to invocations per instance; on Vertex AI, set minimum and maximum replica counts with a target utilization. Set the minimum to what your latency SLA needs during quiet periods and let the endpoint add replicas only when load rises. Autoscaling turns a flat peak-sized bill into one shaped like your actual traffic curve.

Step 4: When should I scale idle endpoints to zero?

Scale to zero whenever an endpoint serves bursty or low-volume traffic, because a scaled-to-zero endpoint stops billing when no requests arrive. SageMaker Serverless Inference and Vertex AI both support scaling that lets capacity drop to nothing during idle periods. The tradeoff is a cold-start delay on the first request after idle, so reserve scale-to-zero for workloads that tolerate that latency, internal tools, batch-style features, and intermittent APIs. For sustained high-throughput traffic, a right-sized always-on endpoint with a commitment is cheaper than serverless.

Step 5: How do I consolidate models onto shared endpoints?

Consolidation hosts multiple models on one endpoint so you stop paying for one underused instance per model. SageMaker multi-model and multi-container endpoints, and Vertex AI model co-hosting, let several models share one instance and its memory, loading models on demand. This removes the one-instance-per-model tax that dominates the bill when teams deploy many small or low-traffic models separately. Group models with compatible runtimes and complementary traffic so the shared instance stays busy without contention.

Step 6: When should I apply commitments to inference?

Apply commitments only after usage is right-sized and stable, so you discount a clean baseline rather than locking in waste. Once an endpoint or fleet shows a persistent floor of always-on capacity, cover that floor with SageMaker Savings Plans or Vertex AI committed use discounts and leave the variable peak on on-demand and autoscaling. Committing before right-sizing is the classic mistake: it buys a discount on capacity you should have eliminated. Reservations last, on a clean baseline, is the Cut sequence in our AI cost FinOps scope.

Frequently asked questions

Why are SageMaker and Vertex AI endpoints so expensive?

Real-time endpoints on SageMaker and Vertex AI bill for the underlying instance for every hour it is provisioned, whether or not requests arrive. A GPU endpoint left running around the clock for a feature used a few hours a day spends most of its budget on idle accelerator time. The expense is rarely the per-request compute; it is provisioned capacity that nobody scaled down.

Does serverless inference save money?

Serverless inference saves money for bursty or low-volume workloads because it bills only while a request is being processed and scales to zero when idle. It can cost more than a provisioned instance for sustained high-throughput traffic, where a right-sized always-on endpoint with a commitment is cheaper. Match the mode to the traffic pattern.

How do I right-size a GPU inference endpoint?

Profile the model's memory footprint and latency target, then pick the smallest instance and accelerator that meets the latency SLA at expected concurrency. Many models that default to a large GPU run fine on a smaller GPU or even CPU once quantized. Load test each candidate instance and keep the cheapest that holds latency.

Can I run several models on one endpoint?

Yes. SageMaker multi-model and multi-container endpoints, and Vertex AI model co-hosting, let several models share one instance and its memory. This removes the one-instance-per-model tax that dominates the bill when teams deploy many small or low-traffic models, each on its own endpoint.

The short version

Optimize SageMaker and Vertex AI endpoint costs by measuring cost per endpoint, right-sizing instances, autoscaling to traffic, scaling idle endpoints to zero, consolidating models, and committing only the stable baseline. The saving comes from eliminating idle provisioned capacity, and every instance change should be load tested against your latency SLA. When you want that endpoint optimization done and the saving proven, that is exactly what our FinOps implementation service delivers.

Written by Morten Andersen and reviewed by Fredrik Filipsson, applying the See, Cut, Lock, Run method. Independent and vendor neutral.