FinOps-Powered Cost Optimisation and Cloud Cost Visibility for AI Workloads
Client
PlotWeaver AI
Partner
Marlocks Technologies
Focus
FinOps
Overview
PlotWeaver AI is a fast-growing AI startup building generative AI products that depend on heavy inference workloads across Amazon Bedrock, Amazon SageMaker, and GPU-backed EC2 instances. As their user base scaled, cloud spend scaled faster than revenue — a familiar pattern for AI startups operating at the intersection of high-cost foundation models and rapid product iteration.
PlotWeaver engaged Marlocks Technologies to bring discipline, visibility, and structural cost reduction to their AWS environment without compromising the performance their product depends on.
Key Challenges
The shape of an AI startup's cloud bill makes it uniquely difficult to control.
Opaque Spend Drivers
Monthly AWS bills grew month-on-month with no clear understanding of which services, environments, or product features were driving the increase.
Untagged Resources
Compute and storage resources had been provisioned organically across multiple accounts with no consistent tagging, making cost allocation to product lines or customer cohorts impossible.
Over-Provisioned Inference
GPU instances ran continuously to serve unpredictable traffic, leaving capacity idle during off-peak hours and inflating cost per inference.
No Commitment Strategy
All consumption was on-demand — no Savings Plans, no Reserved Instances, no Spot adoption — leaving 20–40% of typical savings on the table.
Reactive Cost Management
No budgets, no anomaly detection, no alerts. Cost surprises only surfaced at month-end when the invoice arrived.
The Solution
Marlocks delivered a FinOps-first engagement designed to deliver immediate cost reduction while installing the practices, tooling, and governance for sustained cost discipline.
Cost & Usage Assessment — Deep analysis of AWS Cost and Usage Report (CUR) over a 90-day window using AWS Cost Explorer and a custom Amazon QuickSight dashboard, isolating top cost drivers by service, account, and workload.
Tagging & Cost Allocation Strategy — Standardised tagging schema (environment, product, owner, customer-segment) applied across all resources and enforced going forward via AWS Config and tag policies.
Right-Sizing & Compute Optimisation — Migration of inference workloads from over-provisioned GPU EC2 instances to Amazon Bedrock on-demand and SageMaker Serverless Inference, with Spot Instances adopted for non-production training and batch jobs.
Commitment-Based Discounts — Analysis of steady-state baseline consumption and purchase of a tailored Compute Savings Plan covering predictable workloads, with on-demand capacity reserved for elastic spikes.
Real-Time Visibility — AWS Budgets with multi-threshold alerts, AWS Cost Anomaly Detection across all linked accounts, and weekly FinOps dashboards delivered to PlotWeaver's leadership team.
Knowledge Transfer — Engineering team upskilled through a structured FinOps enablement programme so cost decisions become part of the daily engineering rhythm rather than a quarterly fire-drill.
Key Results
Total AWS Bill Reduction
34% in first 60 days
Sustained through subsequent months as new architecture absorbed user growth
Cost per Inference Reduction
48%
Via right-sizing, Bedrock adoption, and elimination of idle GPU capacity
Cost Attribution Coverage
100% of spend tagged
Attributable to product line, environment, and customer cohort
Anomaly Response Time
Under 48 hours
Reduced from 30+ days — surfacing within 24 hours via automated alerts
Scalability
3x users at 1.4x cost
Breaking the previous linear cost-to-usage relationship
Key Lessons Learned
Tagging discipline is the foundation of FinOps:
Without consistent tagging, no amount of analysis or commitment optimisation can deliver durable savings. Tagging must be designed early and enforced through automation, not policy alone.
Serverless and managed inference shift the cost curve:
Migrating suitable workloads to Bedrock and SageMaker Serverless Inference broke the linear relationship between user growth and infrastructure cost — the single highest-leverage change in the engagement.
FinOps is a cultural shift, not a one-time project:
The most lasting outcome was embedding cost awareness into engineering decision-making. Once developers can see the cost impact of their choices in real time, sustained savings follow without a consultant in the room.