AWS Cost Optimisation for AI Workloads
AI workloads on AWS frequently cost more than expected. This guide covers the specific patterns that drive cost overruns — and how to fix them.
Published 10 February 2026
Right-Sizing Inference Compute
The most common cost mistake is over-provisioning GPU instances for inference. Many teams deploy on the same instance types they used for training, which is dramatically more compute than inference requires.
Start by profiling your actual inference workload: measure latency requirements, throughput needs, and memory usage. Many inference workloads run efficiently on CPU instances or smaller GPU instances like g5.xlarge rather than the p4d instances used for training.
Use auto-scaling aggressively for inference. AI workloads are often bursty — high demand during business hours, low demand overnight. Scale to zero when possible using serverless inference endpoints or container-based auto-scaling with aggressive scale-down policies.
Caching Strategies for LLM Calls
LLM API calls are expensive and often repetitive. Implementing semantic caching — where similar queries return cached results — can reduce LLM costs by 30–60% for many enterprise applications.
Use embedding-based similarity matching to identify queries that are semantically equivalent to previous ones. Set similarity thresholds appropriate to your use case: customer support can tolerate higher thresholds (more cache hits, slightly less precise), while financial analysis needs lower thresholds (fewer cache hits, higher precision).
Implement cache invalidation strategies tied to your data update cycles. When underlying data changes, invalidate cached responses that depended on that data. Time-based expiration works as a safety net but shouldn't be your primary invalidation strategy.
Reserved Capacity for Predictable Workloads
If you have predictable baseline AI workloads — batch processing jobs, scheduled inference pipelines, or consistent API traffic — reserved capacity can reduce costs by 40–70% compared to on-demand pricing.
Analyse your workload patterns over at least 30 days before committing to reservations. Identify the baseline (minimum consistent usage) and reserve that amount. Use on-demand or spot instances for the variable portion above the baseline.
For SageMaker endpoints, consider Savings Plans rather than reserved instances — they offer similar discounts with more flexibility to change instance types as your workload evolves.
Monitoring Token Spend by Feature
Without per-feature cost attribution, you can't optimise AI spending effectively. Implement tagging and logging that tracks token consumption by feature, user segment, and request type.
Build dashboards that show daily and weekly cost trends by feature. Identify which features are cost-efficient (high user value per token spent) and which are wasteful (high token spend with low engagement or satisfaction).
Set budget alerts at the feature level, not just the account level. A single poorly-optimised prompt or an unexpected traffic spike on one feature can consume your entire monthly AI budget in days.
Architectural Changes That Reduce Costs
The highest-impact cost reduction comes from architectural decisions, not infrastructure optimisation. Evaluate whether each AI feature needs a large model or whether a smaller, fine-tuned model would deliver equivalent results at 10x lower cost.
Implement model routing: use a cheap, fast model to classify incoming requests and route only complex ones to expensive models. For many applications, 70–80% of requests can be handled by smaller models, with only the remainder needing GPT-4-class capabilities.
Consider self-hosted open-source models for high-volume, predictable workloads. The upfront infrastructure investment pays off quickly when you're processing millions of requests monthly and the workload is well-understood.
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