Running AI workloads in production is fundamentally different from running traditional web applications. Compute-heavy jobs, unpredictable traffic patterns, large data volumes, and extensive observability requirements can cause cloud costs to grow rapidly — often faster than user adoption.
This case study describes how we helped a client optimize their AWS architecture for a production AI platform , reducing costs significantly while preserving performance, scalability, and reliability.
The Business Problem: Rising AWS Costs Without Matching Growth
The client operates a production AI-based platform on AWS, serving customers through APIs while running background AI processing jobs. Over time, their AWS monthly spend was increasing steadily, but:
- User growth was flat
- Request volume was stable
- No major new features had been launched
Despite a technically sound architecture, cloud costs were becoming a concern for leadership. The goal was not to cut costs at the expense of reliability or slow down the product, but to establish cost-efficient, sustainable operations for AI workloads.
High-Level Overview of the AI Platform
At a high level, the system consisted of:
- Public APIs running on AWS compute services
- Asynchronous AI jobs for data processing and model inference
- Managed databases for transactional and analytical workloads
- Object storage for AI artifacts and intermediate data
- Extensive logging and metrics for observability and debugging
The platform was designed for scalability and correctness — but cost efficiency had not been revisited since the early growth phase.
Step 1: Cost Analysis and Visibility
The first phase focused on understanding where money was actually being spent.
Actions Taken
- Enabled detailed AWS Cost Explorer and Cost and Usage Reports (CUR)
- Tagged resources by:
- Service
- Environment (prod, staging)
- Workload type (API, AI jobs, storage, observability)
- Analyzed costs across:
- EC2, ECS/EKS
- RDS and object storage
- CloudWatch Logs and metrics
- Data transfer between services and regions
Key Findings
- Compute resources were over-provisioned for peak traffic that rarely occurred
- AI background jobs were running on on-demand instances despite being fault-tolerant
- Logging volume had grown linearly with data size, not with actual debugging needs
- Cross-AZ and cross-service data transfer costs were non-trivial
This phase established a baseline and ensured optimization decisions were data-driven , not assumption-based.
Step 2: Compute Optimization for APIs and AI Jobs
API Layer
- Right-sized EC2/ECS workloads based on real CPU and memory utilization , not instance defaults
- Tuned Auto Scaling policies to scale on realistic metrics rather than conservative thresholds
- Reduced idle capacity during off-peak hours without affecting latency SLAs
Trade-off: Slightly slower scale-up time during traffic spikes, mitigated by warm capacity buffers.
Background AI Jobs
- Migrated suitable workloads to Spot Instances using:
- Managed node groups / capacity providers
- Job retry logic and checkpointing
- Split AI workloads into:
- Latency-sensitive (on-demand)
- Throughput-oriented (Spot)
Trade-off: Occasional Spot interruptions, handled at the application level with retries and idempotency.
Step 3: Database and Storage Optimization
Databases
- Reviewed RDS instance classes and storage performance
- Reduced over-provisioned IOPS and instance sizes where utilization was consistently low
- Introduced read replicas only where read scaling was actually required
- Implemented data lifecycle policies for historical data
Object Storage
- Applied S3 lifecycle rules :
- Hot data in Standard
- Infrequently accessed artifacts moved to IA
- Long-term archives moved to Glacier
- Removed unused or duplicate AI artifacts accumulated during experimentation
Trade-off: Slightly higher retrieval latency for archived data, acceptable for non-production paths.
Step 4: Logging and Monitoring Cost Reduction
Logging was one of the fastest-growing cost centers.
Improvements Made
- Reduced log verbosity for production workloads
- Introduced sampling for high-volume API logs
- Set retention policies in CloudWatch Logs instead of keeping logs indefinitely
- Exported critical logs to S3 for low-cost long-term retention
- Reviewed custom metrics and removed unused ones
Outcome: Observability quality remained intact while log storage and ingestion costs dropped substantially.
Step 5: Network Configuration Optimization and Traffic Path Reduction
As the platform scaled, we identified that a meaningful portion of AWS spend was tied to network data transfer rather than raw compute. While the architecture was functionally correct, traffic paths were not always cost- or latency-efficient.
Improvements Implemented
- Reviewed service-to-service traffic flows and eliminated unnecessary cross-AZ communication by aligning compute and dependent services within the same Availability Zones where fault tolerance allowed.
- Optimized VPC routing and security group design to ensure direct traffic paths and avoid unintended hops through NAT gateways or intermediate services.
- Reduced reliance on NAT Gateways by introducing VPC endpoints (Interface and Gateway endpoints) for AWS services such as S3, CloudWatch, and ECR, significantly lowering outbound data transfer costs.
- Ensured load balancers, backend services, and databases were regionally and zonally aligned , minimizing cross-zone data transfer charges.
- Introduced caching at appropriate layers to avoid repeated network calls for frequently accessed data and AI artifacts.
Engineering Trade-offs
- Tighter AZ affinity required careful evaluation of failure scenarios and was balanced with selective multi-AZ redundancy for critical paths.
- Additional upfront design effort was needed to map traffic flows, but it resulted in simpler and more predictable network behavior.
Outcome
These changes reduced network data transfer costs while also improving request latency and hop efficiency , leading to faster service-to-service communication and a more predictable networking model under load.
Step 6: Governance and Cost Controls
To prevent cost creep from returning:
- Implemented AWS Budgets and alerts
- Enforced resource tagging via IaC
- Added cost checks to infrastructure review processes
- Established periodic cost review cycles alongside performance reviews
This ensured optimisation became an ongoing practice , not a one-time effort.
Results: Measurable Improvement Without Compromise
Without disclosing exact numbers, the outcomes were clear:
- Noticeable reduction in overall AWS spend
- Improved compute efficiency for AI workloads
- Better alignment between traffic patterns and infrastructure scaling
- Stable production performance with no reliability regressions
- Increased confidence in cost predictability as the platform scales
Most importantly, the platform became cost-efficient by design , not by constant manual intervention.
Conclusion: Sustainable Cost Control for AI Workloads
AI platforms amplify both value and inefficiency in cloud environments. Over-provisioning, excessive logging, and conservative architecture choices can quietly inflate costs if left unchecked.
This case study demonstrates that meaningful AWS cost optimization:
- Does not require compromising performance or reliability
- Relies on engineering discipline, not shortcuts
- Works best when embedded into architecture and governance
Key Takeaway
Sustainable cloud cost optimization for AI products comes from understanding workload behavior, making intentional trade-offs, and continuously aligning infrastructure with real usage — not peak assumptions.
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