Cloud Cost Management and Optimization
Our cloud cost management work finds where your AWS or Azure bill has outgrown what the business needs. Senior engineers size services to real demand, shut down what sits idle, and change the architecture where that saves more than tuning.
Sized to real usage
- Always-on serversServerless functionsDone
- Oversized databaseRight-sized databaseDone
- Idle test environmentsScheduled shutdownNow
- Unused storageNext
- Untagged resourcesNext
SIZED TO REAL DEMAND
Cloud bill
Tracked against the plan
What Is Cloud Cost Optimization?
Cloud cost optimization means paying only for the cloud capacity your applications actually use. It covers right-sizing servers and databases, shutting down idle resources, choosing pricing and storage options, and sometimes changing the architecture. Cloud bills grow quietly, because adding capacity is easy and removing it rarely has an owner.
Some of the largest savings come from how a workload is built. Usage-based services such as AWS Lambda charge only for what you use, which suits uneven traffic better than servers sized for the peak.
Is FinOps the same as cloud cost management?
FinOps is the practice of making engineering, finance, and business teams share responsibility for cloud spend, with regular reviews of what is used and what it costs. Cost optimization is the engineering work inside it, which means finding waste and fixing it.
WARNING SIGNS
Signs Your Cloud Bill Needs a Review
Cloud costs drift when services are oversized or run when nobody needs them. These are the usual signs.
The bill grows faster than usage
Spend climbs month over month while traffic, users, and revenue stay flat.
Servers are sized for a rare peak
Instances and databases run at full size around the clock to handle a busy hour or a seasonal spike.
Environments run when no one is working
Test, staging, and demo environments stay on nights and weekends with no one using them.
Nobody can say who owns the spend
Resources have no tags or owners, so no one can tie a line on the bill to a product or team.
The move to the cloud raised the bill
Applications moved as-is, still sized like on-premises servers, and now cost more to run than before.
AI usage costs keep climbing
Model and token costs grow with every new AI feature, and nobody tracks them by workload.
OUR APPROACH
How We Cut Cloud Costs
We treat a high cloud bill as an engineering problem. Most of the fixes live in how services are sized and built, so senior engineers make the changes and test them before they ship. Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
Billing tools alone
- Dashboards nobody acts on
- Discounts on oversized resources
- One-time cleanups that drift back
- Savings that slow the applications
Engineering-led optimization
Size to real demand
We build for the scale you need now, not the scale you might need one day, and size each service to real usage data.
Pay for what runs
Serverless services such as AWS Lambda charge only for what you use. A regional bank's daily pipeline on AWS Lambda processes about 30,000 records, and the bank pays only for the AWS resources it uses.
Fix the architecture when it's the cost
Some bills can't be tuned down. A wine retailer moved its database to Amazon Aurora and cut database costs by about 50%.
Keep performance intact
Every change is tested against real workloads before it ships, so savings don't come back as slow pages or failed jobs.
PRICING OPTIONS
Choosing the Right Cloud Pricing Model
AWS and Azure both sell capacity in four main ways, and most environments should use more than one.
| OPTION | HOW YOU PAY | WHEN IT FITS |
|---|---|---|
| On demand | By the hour or second, with no commitment | Workloads that change or are still being sized |
| Savings plans and reservations | A lower rate in exchange for one or three years of committed usage | Steady workloads that will run all year |
| Spot capacity | A deep discount on spare capacity that can be taken back | Batch jobs and work that can restart |
| Serverless | Per request or per second of run time | Uneven or bursty traffic |
Commit only after you right-size
A discount on an oversized resource locks in the waste. We size services first, then recommend commitments only for the workloads that will stay steady, and leave spiky work on demand or serverless.
WHAT WE OPTIMIZE
Where We Find Cloud Savings
Our cloud cost optimization services cover the parts of an AWS or Azure environment where spend usually hides.
Compute right-sizing
Instances and containers matched to real CPU and memory use. For a bulk liquids storage and logistics provider, we helped the client use the AWS Billing Dashboard to find cost reductions through instance downsizing.
Databases
Database engines, instance sizes, and replicas matched to the workload, including moves to managed services such as Amazon Aurora and Amazon RDS.
Serverless and architecture changes
Workloads with uneven traffic moved from always-on servers to services such as AWS Lambda, which charge only when code runs. See AWS consulting and development.
Idle and forgotten resources
Test environments, unattached storage, old snapshots, and resources nobody owns, shut down or scheduled to run only when needed.
AI and model usage
Token and model costs tracked by workload, with smaller or local models used where they do the job. See generative AI development.
Cost plans for migrations
For applications still moving to the cloud, the expected monthly bill is planned before cutover, not after. See cloud migration services.
HOW IT WORKS
How a Cloud Cost Review Runs
A cost review runs in five steps, from reading the bill to tuning with real usage data.
STEP 1
Bill and usage review
We break the bill down by service, environment, and team, and compare it with real usage.
STEP 2
Findings
We list each saving with its estimated value, effort, and risk.
STEP 3
Quick fixes
We shut down idle resources and right-size the obvious cases first.
STEP 4
Architecture changes
Where tuning isn't enough, we change how the workload runs, such as moving it to serverless.
STEP 5
Ongoing tuning
We review spend as usage changes, so savings don't drift back.
Where an AWS review starts
AWS cost optimization starts with the bill and the usage data behind it, then the architecture producing both. Cost optimization is also one of the six pillars of the AWS Well-Architected Framework, which our AWS Well-Architected Review Session covers.
Savings without slowdowns
Changes go out in stages, each tested against real workloads, so any one of them can be reversed. A saving that slows the site or breaks a nightly job isn't a saving.
RESULTS
Cloud Cost Results
For a direct-to-consumer wine retailer, JBS Dev moved the website to serverless AWS Lambda and the database to Amazon Aurora. Database costs fell by about 50%, and the site absorbed a 400% traffic increase with no outages. For an industrial sensor manufacturer, JBS Dev assessed the whole AWS environment and worked with its IT team on the first optimization initiative.
50%
Approximate drop in database costs
E-commerce
Serverless Ecommerce for Wine Access
Challenge: Wine Access ran its site on costly self-hosted servers from the 1990s.
Result: A move to AWS Lambda and Amazon Aurora absorbed a 400% traffic increase with no outages and cut database costs by about 50%.
Read the Wine Access case study$1M+
Cut from annual cloud costs
Automotive
Generative AI Photo Pipeline for a Used-Car Retailer
Challenge: Vehicle photo capture was inefficient, costly to maintain, and hard to scale.
Result: A GenAI pipeline on Kubernetes automates plate blurring, cropping, and background edits, and cut annual cloud costs by over $1 million.
Read the used-car retailer case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
EFFORT AND TIMELINE
What Drives Cloud Cost Review Effort and Timeline
The effort depends mostly on how large the environment is and how many of the savings need code or architecture changes. Six factors drive most estimates:
- 01Size of the monthly bill and number of accounts
- 02Number of applications and environments
- 03Tagging and cost visibility today
- 04How much change needs code or architecture work
- 05Performance and uptime requirements
- 06Commitments and contracts already in place
How the timeline is set
The review sets it. Quick fixes such as idle resources and obvious oversizing come early, while architecture changes run in stages. As a reference point, a wine retailer's move to AWS Lambda ran component by component over about four months.
How cost is set
We don't quote before we understand the environment. The review turns the factors above into an estimate, and we check that each change pays for itself before recommending it. If a saving costs more to make than it returns, we'll tell you to leave it.
ENGAGEMENT MODELS
Ways to Work With Us
Each model stands alone or leads into the next. Compare all the ways to work with us.
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you're not sure what to change first. We review the system component by component, weigh difficulty against business impact, and recommend a path for each part, including what to leave alone. You get a roadmap you can act on with us or on your own.
- Technical assessment
- Architecture review
- AI opportunity mapping
- Modernization roadmap
- Prioritized recommendations
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to build or modernize a defined system.
For a defined scope, senior engineers lead delivery from architecture through release. We work in stages, write tests for core components, and ship through CI/CD. You own everything we build, plus knowledge transfer and documentation tailored to your team.
- Modernization projects
- Custom applications
- Cloud platforms
- Data systems
- Integrations
- AI agents
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you need more than a single project, services we can provide include architectural guidance for your existing team, hands-on development, QA, and ongoing support. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work.
- Senior-led teams
- Embedded engineering
- Ongoing development
- Knowledge transfer
- Flexible support
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
WHY JBS DEV
How to Choose a Cloud Cost Optimization Partner
Whether you call it FinOps consulting or a cost review, ask these four questions before you hand over your billing data.
- Will they change the architecture, or only the billing settings?
- Both, when the savings justify it. Pricing commitments and cleanup help, but some bills stay high until the workload runs differently. Our senior engineers make those changes, such as moving uneven traffic to AWS Lambda, and test them before they ship.
- Do they know your cloud?
- JBS Dev is an AWS Advanced Tier Services Partner with Generative AI, Migration, and Retail competencies, and also builds on Azure. Read more about our AWS partnership.
- Will they protect performance?
- They should test every change against real workloads. A saving that slows the site or breaks a nightly job costs more than it saves. We ship changes in stages so each one can be reversed.
- Where is the team?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work. You will know exactly who is reviewing your environment and making the changes.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
Cloud Cost Management FAQs
What is cloud cost management?
It's the ongoing work of seeing, controlling, and reducing what you spend on cloud services. It covers tracking spend by team and application, right-sizing resources, shutting down idle ones, choosing pricing options, and changing the architecture where that saves more. Ours is led by senior engineers and covers AWS and Azure.
How much can cloud cost optimization save?
It depends on how the environment was built and how long it has gone without a review. We don't promise a percentage before we've seen the bill. As one reference point, a wine retailer cut its database costs by about 50% after moving to Amazon Aurora. The review estimates each saving before you commit to it.
Do you work on AWS and Azure?
Yes. JBS Dev is an AWS Advanced Tier Services Partner with Generative AI, Migration, and Retail competencies, and we also build on Azure. On both, we size to real usage, remove idle resources, commit only to steady workloads, and change the architecture where tuning isn't enough.
What is right-sizing?
Right-sizing matches each server, container, or database to the CPU, memory, and storage it actually uses. Many resources are sized for a peak that rarely comes, or were never resized after launch.
Should we buy savings plans or reserved instances?
Only for workloads that are already right-sized and will run steadily. A one-year or three-year commitment lowers the rate but locks in whatever size you commit to. We right-size first, then recommend commitments for the steady base, and leave spiky workloads on demand or serverless.
Can serverless lower our cloud bill?
Often, when traffic is uneven. Serverless services such as AWS Lambda charge only when code runs, so you stop paying for idle servers. A wine retailer's site moved to AWS Lambda component by component and later absorbed a 400% traffic increase with no outages. Steady, heavy workloads can cost less on reserved servers.
How do you control AI and LLM costs?
We track model and token spend by workload, use smaller or local models where they do the job, and avoid running a model constantly when a scheduled or triggered call works. Running a large model on every event gets expensive fast, so we size the model to the task.
How do you keep cloud costs from creeping back?
Tag resources by team and application, review spend as usage changes, and set budgets that alert before the bill surprises anyone. Our cloud optimization services can include those reviews as ongoing support, or we can set them up for your own team to run.
