Cloud & AI Cost Optimization Service | Codersarts CostControl

Last month's AWS bill was double what you projected. Your OpenAI API costs scaled faster than your revenue. Your Vercel usage fees appeared out of nowhere. Your team has no clear picture of what's actually driving the spend — just a growing invoice and a finance team asking questions you can't answer.

This is not a scaling problem. It is an optimization problem. And it is fixable.

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│  COSTCONTROL SYSTEM STATUS: OPERATIONAL
│  🟢 Infrastructure Audits: Open
│  💰 Avg Client Savings: 40–70% of current spend
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Codersarts CostControl is a cloud and AI infrastructure cost audit and optimization service. We analyse your entire stack — cloud compute, AI API usage, database infrastructure, third-party services, and deployment architecture — identify exactly where money is being wasted, and implement fixes that cut your monthly bill without touching your product's performance.

Who CostControl Is For

• SaaS founders whose AI API costs are growing faster than MRR

• Startups that scaled quickly and never optimised the infrastructure underneath

• Engineering teams under pressure from finance to reduce cloud spend without cutting features

• CTOs and tech leads who inherited infrastructure they didn't design and don't fully understand

• Enterprise teams running multiple AI workloads with no clear cost attribution per team or product

• Indie hackers and bootstrapped builders whose hosting and API costs are eating their profit margin

What CostControl Audits

1. AI & LLM API Costs

The fastest-growing line item on most tech stacks in 2026. AI API costs are almost always higher than they need to be — not because the product uses too much AI, but because the implementation is inefficient.

• Model selection waste: Using GPT-4o for tasks that GPT-4o-mini handles equally well at one-tenth the cost

• Token bloat: Oversized system prompts, redundant context windows, uncompressed document injection

• Missing caching: Re-running identical or near-identical prompts on every request instead of caching responses

• No streaming: Loading full completions before rendering, increasing perceived latency and compute cost simultaneously

• Unthrottled agent loops: Autonomous agents making API calls without budget guards or loop limits

• Wrong embedding model: Using expensive embedding models for retrieval tasks where smaller models perform identically

2. Cloud Compute & Infrastructure

• Overprovisioned instances: EC2, GCP Compute, or Azure VMs running at 10% CPU utilisation — you're paying for capacity you never use

• Idle resources: Dev and staging environments left running 24/7, load balancers with no traffic, unused Elastic IPs

• Wrong instance family: Memory-optimised instances running compute-heavy workloads, or vice versa

• No auto-scaling: Fixed capacity provisioned for peak load instead of scaling dynamically with actual demand

• Data transfer costs: Unnecessary cross-region traffic, uncompressed payloads, missing CDN coverage for static assets

• Unattached storage: EBS volumes, S3 buckets, and database snapshots accumulating with no deletion policy

3. Database Infrastructure

• Overprovisioned RDS/Cloud SQL: Database instances sized for a load that never materialised

• Missing connection pooling: Every application request opening a new database connection, driving up compute costs

• Unoptimised queries: Full table scans, missing indexes, N+1 query patterns generating unnecessary read load

• Redundant read replicas: Read replicas provisioned but not used, or used for workloads that don't require them

• Snapshot retention bloat: Daily snapshots retained for 90 days with no policy review

4. Serverless & Edge Costs

• Cold start mismanagement: Functions provisioned incorrectly causing repeated cold starts and inflated execution times

• Memory over-allocation: Lambda/Cloud Functions allocated 1GB of memory for tasks requiring 128MB

• Unthrottled invocations: Background jobs triggering serverless functions in loops without rate limiting

• Vercel/Netlify function overuse: Edge function invocations exploding due to unoptimised middleware

5. Third-Party Service Stack

• Duplicate tooling: Paying for Datadog, New Relic, and CloudWatch simultaneously for the same observability function

• Unused seats and plans: SaaS subscriptions on enterprise tiers with 3 active users

• Zapier/Make automation costs: Automation platforms running thousands of tasks that could be replaced by a simple cron job

• Overlapping storage: Files stored redundantly across S3, Cloudflare R2, and a third-party CDN

6. AI Agent & Pipeline Infrastructure

• Unguarded agent execution: Agentic workflows with no token budget limits running until they hit rate limits

• Vector database over-provisioning: Pinecone or Weaviate indexes sized for 10 million vectors when 100,000 are in use

• Embedding pipeline redundancy: Re-embedding documents on every ingestion run instead of caching existing vectors

• LLMOps tooling cost: Paying for hosted LangSmith or Helicone tiers when self-hosted alternatives are equivalent

What You Receive

Infrastructure Cost Audit Report

A complete breakdown of your current spend by category, service, and root cause. Every wasteful pattern identified, quantified in dollars, and ranked by savings potential. Not a generic best-practices checklist — a specific analysis of your actual stack.

Optimisation Implementation

We don't just identify the problems — we fix them. For each finding, we implement the optimisation directly: refactoring prompt structures, rightsizing instances, setting up caching layers, configuring auto-scaling policies, and writing the infrastructure-as-code changes needed to make reductions permanent.

Cost Attribution Dashboard

A monitoring setup that gives your team — and your finance stakeholders — real-time visibility into spend by service, environment, team, and product feature. Built on your existing observability stack or set up from scratch.

30-Day Savings Verification

We track your infrastructure costs for 30 days post-implementation and deliver a verified savings report showing exactly how much was reduced and where. Your finance team gets a documented ROI on the engagement.

How It Works

Step 1: Stack Access & Scoping (Day 1–2)

You grant read-only access to your cloud billing dashboards, AI API usage logs, and infrastructure configuration. We scope the audit based on your current monthly spend and identify the highest-priority areas to investigate first.

Step 2: Full Cost Audit (Day 3–7)

We run a systematic analysis across every layer of your stack. Every service, every environment, every API endpoint. Output: a prioritised findings report with estimated savings per item before we touch anything.

Step 3: Optimisation Approval

You review the findings and approve which optimisations to implement. Nothing changes in your infrastructure without explicit sign-off. High-risk changes (instance resizing, architecture modifications) are flagged separately with rollback plans.

Step 4: Implementation (Day 8–14)

We implement approved optimisations directly in your stack. Infrastructure changes are applied via pull requests to your IaC repository (Terraform, Pulumi, or direct console changes with documentation). Prompt and pipeline refactors are delivered as code changes with test coverage.

Step 5: Monitoring & Verification (Day 15–45)

We monitor the impact of every change for 30 days and deliver a verified savings report at the end of the window.

Typical Savings by Category

These are conservative estimates based on common patterns — actual savings depend on your specific stack and current inefficiencies.

CostControl vs. Doing It Internally

Frequently Asked Questions

Q: What access do you need to our infrastructure? A: Read-only access to your cloud billing dashboards (AWS Cost Explorer, GCP Billing, Azure Cost Management), AI API usage logs, and infrastructure configuration files. We never require write access until you explicitly approve specific changes in Step 3.

Q: Will optimisations affect our product's performance or uptime? A: No. Every optimisation is evaluated for performance impact before implementation. Changes that carry any risk are flagged separately, tested in a staging environment first, and implemented with a rollback plan in place.

Q: Our stack is spread across AWS, GCP, and multiple AI providers. Can you handle multi-cloud? A: Yes. Multi-cloud and multi-provider stacks are common — and they almost always have more waste than single-cloud setups because costs are harder to track across providers. We audit across every provider simultaneously.

Q: How quickly will we see savings? A: The fastest wins — model swaps, caching layers, idle resource shutdowns — show up on the next billing cycle. Infrastructure rightsizing and architecture changes take 2–4 weeks to fully reflect. The 30-day verification window captures the full picture.

Q: We're an enterprise team with strict security requirements. How do you handle data sensitivity? A: We work within your security constraints. Audit access is read-only and scoped to billing and configuration data — not application data or databases. We sign NDAs as standard and can operate within your VPN or private network environment.

Q: What if our engineers disagree with a recommended optimisation? A: Nothing is implemented without your team's approval. Every recommendation includes a technical rationale and a risk assessment. If your engineers have concerns, we discuss and adjust before proceeding. We work with your team, not around them.

Q: Is this a one-time service or ongoing? A: The core CostControl engagement is a one-time audit and implementation. We offer an optional monthly monitoring retainer for teams that want ongoing cost governance and alerting as the stack evolves.

Your Infrastructure Bill Is Higher Than It Needs to Be

Every month you delay an audit is another month of avoidable spend. For most stacks we review, the savings identified in two weeks of audit work pay for the engagement within the first billing cycle after implementation.

👉 Book a CostControl Assessment