AI/ML Cost Overview
LLM Model Impact Analyzer
AI Optimization Database & Guardrails
AI Token Optimization
A curated database of AI infrastructure optimization tactics per hyperscaler, guardrails that support cost avoidance and governance, and practices that promote proactive AI cost management.
Optimization Tactics
18
Across compute, inference & data
Cost Guardrails
10
Preventive & detective controls
Hyperscalers Covered
3
AWS (primary), Azure, GCP
Potential Savings
40-90%
Depending on tactic applied
AI Infrastructure Optimization Tactics — AWS
| Category | Tactic | AWS Service / Feature | Est. Savings |
|---|---|---|---|
| Compute | Use Spot Instances for fault-tolerant training with checkpointing | SageMaker Managed Spot Training | up to 90% |
| Compute | Right-size training instances; use Graviton/Inferentia/Trainium where supported | SageMaker + AWS Inferentia2 / Trainium | 30-50% |
| Inference | Scale endpoints to zero for bursty/low traffic workloads | SageMaker Serverless Inference | up to 90% |
| Inference | Consolidate multiple models onto shared hosting | SageMaker Multi-Model Endpoints | 40-70% |
| Inference | Auto-scale endpoint instance count on invocation metrics | Application Auto Scaling | 30-60% |
| Inference | Batch offline predictions instead of keeping live endpoints | SageMaker Batch Transform | 50-80% |
| GenAI | Match model to task complexity (Haiku/Titan for simple, Sonnet for complex) | Amazon Bedrock model selection | 60-80% per request |
| GenAI | Enable prompt caching for repeated context | Bedrock Prompt Caching | 20-40% tokens |
| GenAI | Use Provisioned Throughput for sustained high-volume inference | Bedrock Provisioned Throughput | 30-50% at scale |
| GenAI | Batch non-urgent GenAI jobs for discounted throughput | Bedrock Batch Inference | ~50% |
| Dev | Auto-stop idle notebooks with lifecycle configs | SageMaker Notebook Lifecycle Config | $50-200/mo each |
| Commitment | Commit to steady ML compute for a discount | SageMaker Savings Plans | up to 64% |
| Data | Tier training data and artifacts to cheaper storage | S3 Intelligent-Tiering / Glacier | 40-70% storage |
| Data | Compress and dedupe feature/training datasets | S3 + Parquet/columnar formats | 20-50% storage |
Cross-Hyperscaler Tactic Equivalents
| Tactic | AWS | Azure | GCP |
|---|---|---|---|
| Discounted interruptible training compute | EC2/SageMaker Spot | Azure Spot VMs / Low-Priority | Preemptible / Spot VMs |
| Purpose-built AI accelerators | Inferentia2 / Trainium | ND-series GPUs | Cloud TPUs |
| Scale-to-zero inference | SageMaker Serverless Inference | Azure ML Managed Online (scale to 0) | Vertex AI / Cloud Run |
| Managed GenAI model catalog | Amazon Bedrock | Azure OpenAI / AI Foundry | Vertex AI Model Garden |
| Commitment-based discounts | SageMaker Savings Plans | Reserved / Savings Plans | Committed Use Discounts |
| Batch inference discount | Bedrock Batch Inference | Azure OpenAI Batch API | Vertex AI Batch Prediction |
AI Guardrails — Cost Avoidance & Governance
| Type | Guardrail | Implementation | Prevents |
|---|---|---|---|
| Preventive | Restrict which Bedrock models/regions can be invoked | IAM policy + SCP on bedrock:InvokeModel | Use of unapproved, expensive models |
| Preventive | Cap max training/endpoint instance types & counts | SCP / IAM condition keys on instance type | Runaway GPU spend |
| Preventive | Require cost-allocation tags on all AI resources at creation | Tag Policies + SCP deny on missing tags | Untracked, unattributable AI spend |
| Preventive | Set per-model token/throughput quotas | Bedrock quotas + application rate limiting | Token blowout from loops/abuse |
| Detective | Dedicated AI/ML budget with 50/80/100% alerts | AWS Budgets with service filter | Silent budget overruns |
| Detective | Anomaly detection on AI/ML service spend | AWS Cost Anomaly Detection | Undetected cost spikes |
| Detective | Log every model invocation with token counts | Bedrock Model Invocation Logging | Blind spots in usage attribution |
| Detective | Alert on idle endpoints (zero invocations, N days) | CloudWatch alarm on Invocations metric | Paying for unused endpoints |
| Responsive | Auto-stop idle notebooks & endpoints | EventBridge + Lambda automation | Idle resource waste |
| Responsive | Content/output guardrails to limit response length | Bedrock Guardrails + max_tokens caps | Excess output-token cost |
Promoting Proactive AI Cost Management
Forecast Before You BuildPlan
Use the LLM Model Impact Analyzer to project annual cost at expected volume before writing production code.
Start Small, Escalate on NeedRight-size
Default to the cheapest model that meets quality bars; escalate to premium models only when evals prove it necessary.
Tag EverythingAttribute
Enforce Environment / Owner / Project / CostCenter tags so every dollar of AI spend maps to a team and use case.
Review WeeklyInspect
Add AI/ML spend to the weekly FinOps review; investigate any week-over-week spike above threshold.
Set Budgets & AlertsGuard
Every AI workload gets a budget with 50/80/100% alerts before it reaches production.
Automate CleanupAutomate
Auto-stop idle notebooks and endpoints; make waste removal the default, not a manual chore.
Proactive Culture Maturity Model
| Level | Behavior | Signal |
|---|---|---|
| 1 — Reactive | Costs reviewed only after the bill arrives | Surprise overruns, no tagging |
| 2 — Aware | Dashboards exist; spend is visible but not owned | Visibility without accountability |
| 3 — Managed | Budgets, alerts, and tags enforced per workload | Guardrails in place, alerts acted on |
| 4 — Proactive | Cost forecast and model eval done before launch | Optimization built into design |
| 5 — Optimized | Continuous automated optimization & cleanup | Waste auto-removed, spend predictable |