Certified AWS Architects · AI/ML · DevOpsYour AWS Bill Goes Up 15% Every Quarter. Nobody Knows Why.
Over-provisioned EC2s, idle NAT Gateways, S3 in the wrong tier, Reserved Instances nobody tracks. We audit, fix, and manage your AWS — plus deploy AI on Bedrock and SageMaker. Average client saves $4,200/month.
Quick answer · Updated June 2026
Why your AWS bill grows faster than your revenue.
We've audited 40+ AWS accounts in the last year. The pattern: bills grow ~15% per quarter even when traffic is flat. Six causes account for almost all of it — and none are about AWS being expensive. AWS is flexible enough to silently grow your spend when nobody owns the cost surface.
A $30k/month AWS bill that hasn't been touched in 12 months typically has $9k–$13k of recoverable spend. Most of it is no-code-change config and IAM. The 48-hour read-only audit identifies the top wins by dollar value — you decide what to action.
Get a free 48-hour AWS auditNew services launched, old ones never shut down
Every product launch spins up dev / staging / prod for a new feature. The dev environment lives forever. Two years in: 14 environments, 4 are still used. The other 10 keep billing. Nobody owns the kill decision.
Auto-scaling that only scales up
ASG configured for peak Black Friday traffic. Scale-up triggers fire daily. Scale-down cooldowns are 60 minutes (default) or disabled entirely. Result: you pay peak capacity 24/7 for 95% of the year.
Data transfer (egress) compounding quietly
Microservices chatting cross-AZ at $0.01/GB each way, NAT Gateway routing 2 TB/month at $0.045/GB, CloudFront missing in front of public APIs. Egress climbs 8–12% per quarter on its own as traffic grows. Most teams don't have a dashboard for it.
Engineers can't see cost in their workflow
AWS Cost Explorer lives in finance's tab nobody opens. Engineers can't see what their PR will cost. We wire up Infracost in CI so every PR shows the $/month delta — you stop the leak at code review, not invoice day.
S3 lifecycle policies are aspirational
Bucket created in 2019. Lifecycle policy: “move to IA after 30 days” — was never enabled. 800 TB of logs sit in Standard at $0.023/GB. The math: $18,400/month vs. $1,000/month in Glacier Deep Archive. We move them with versioning intact.
GPU and AI spend has no governance
Data scientist spins up a p4d.24xlarge for an experiment. $32/hour. Forgets to terminate. The bill arrives 28 days later. AWS Budgets alert was set to $5,000 — the GPU instance blew through $21,000 before anyone noticed. We deploy hard guardrails (Service Control Policies + auto-shutdown Lambdas).
AWS that saves money. And makes money.
Amazon Bedrock & Gen AI
Deploy generative AI using Claude, Llama, Titan — without building your own GPU cluster. RAG pipelines, knowledge bases, AI agents. Production-ready, not a POC that dies on a Jupyter notebook.
AWS Cost Optimization
Your bill goes up 15% every quarter and nobody knows why. We find every over-provisioned EC2, every idle NAT Gateway, every S3 bucket in the wrong storage class. Average savings: $4,200/month.
Cloud Migration
On-prem to AWS. Or fixing the mess from your last migration. Lift-and-shift, re-platform, or re-architect — depends on ROI, not what sounds impressive in a deck.
SageMaker & ML Ops
Custom ML models on SageMaker — demand forecasting, anomaly detection, recommendation engines. Model training, deployment, and monitoring. Not a research project, a production pipeline.
DevOps & CI/CD
CodePipeline, Terraform, Docker, EKS. Automated deployments, infrastructure-as-code, blue-green releases. Your team ships daily, not quarterly.
Security & Compliance
GuardDuty, Security Hub, WAF, CloudTrail. SOC2, HIPAA, PCI-DSS — not checkbox compliance. IAM policies that don't give everyone admin. Actual security posture.
Containerization & EKS
ECS, EKS, Fargate. Containerize your monolith, set up service mesh, implement auto-scaling. 60% infra cost reduction vs. always-on EC2 instances.
Serverless & Lambda
Lambda, API Gateway, DynamoDB, Step Functions. Pay-per-execution architecture for event-driven workloads. Zero idle cost. Infinite scale.
Every AWS service. Certified architects.
From AWS accounts we manage. Not marketing slides.
Avg Monthly Savings
Rightsizing, Reserved Instances, Savings Plans, Spot fleets, and nuking zombie resources
Avg Cost Reduction
Across 40+ AWS environments we've audited in the last 12 months
Uptime Achieved
Multi-AZ architecture with auto-scaling, health checks, and automated failover
Avg Migration
Structured, phased, zero-unplanned-downtime migration — not a 6-month consulting project
Faster Deployments
From manual deployments to CI/CD: CodePipeline + Terraform + Docker
Managed Services
Monitoring, patching, backups, cost reports, quarterly architecture reviews
Audit to managed. The audit is free.
AWS Audit & Cost Analysis
We scan your AWS account — every EC2 instance, every S3 bucket, every idle resource. You get a report showing exactly where you're bleeding money and what's misconfigured. Free.
Architecture & Action Plan
Blueprint your target architecture — multi-AZ, auto-scaling, security hardening. Fixed-scope proposal with timeline and pricing. Not a 40-page PDF that says "it depends."
Build & Migrate
Infrastructure-as-code deployment. Phased migration with parallel running and rollback plan. CI/CD pipelines, container orchestration, monitoring — all production-ready.
Optimize & Manage
Post-launch optimization: performance tuning, cost monitoring, security hardening. Then ongoing managed services — monthly reports, quarterly reviews, 24/7 alerting.
Pick the right AWS AI stack. Not whichever one trends on LinkedIn.
Three legitimate ways to run AI on AWS, three very different cost and ops profiles. We pick per workload based on token volume, latency targets, data sensitivity, and team capability. Below is the same decision matrix we use in week-one architecture reviews.
Amazon Bedrock
Use when: foundation models are good enough
- Access to Claude (Anthropic), Llama (Meta), Titan (Amazon), Mistral, Cohere via one API
- Pay per 1K input/output tokens — no idle cost, no GPU management
- Knowledge Bases for RAG (Bedrock embeds, S3 + OpenSearch retrieval, prompt augmentation)
- Bedrock Agents for tool-use / function-calling workflows
- Guardrails for content filtering, PII redaction, prompt-injection defence
- Best for: chatbots, content generation, summarisation, RAG, agentic workflows
- Skip when: you need a fine-tuned model on proprietary data with strict latency SLAs
Amazon SageMaker
Use when: you're building or fine-tuning your own models
- Full ML lifecycle: data labelling (Ground Truth), training, hyperparameter tuning, deployment, monitoring
- Custom training on your proprietary dataset — your competitive moat
- Inference endpoints with auto-scaling (real-time, serverless, async, batch)
- Model Registry, Pipelines for MLOps, Feature Store for re-use across teams
- Inferentia / Trainium instance support — 40–60% cheaper than NVIDIA equivalents
- Best for: classical ML (forecasting, classification, anomaly detection) and custom LLMs
- Skip when: an off-the-shelf model via Bedrock already does the job
Self-hosted on EC2 / EKS
Use when: you have GPU expertise and predictable scale
- Full control over inference stack — vLLM, TensorRT-LLM, llama.cpp, custom kernels
- P5 / P4d / G6 instances with EFA networking for multi-GPU training
- Spot Fleet + Karpenter for cost-aware GPU autoscaling
- Best for: open-source models at scale, fine-tuning runs, research workloads
- Best for: scenarios where Bedrock's token pricing is more expensive than reserved GPU hours
- Skip when: your team doesn't have a senior ML/infra engineer on call
“Our AWS bill was $32K/month with half the instances sitting idle. Braincuber audited everything, redesigned our architecture, set up auto-scaling, and migrated us in 6 weeks. Now we run 40% faster at $20K/month. And they deployed a Bedrock-powered chatbot that handles 60% of our customer queries.”
Vikram Nair
CTO, Pureplay Retail
AWS questions. Real answers.
Every month you wait, AWS charges you $4K+ more than it should.
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