Cloud decisions made by the engineers who run the system.
Most cloud problems start as architecture decisions made by people who never had to operate the result. AnAr designs and runs the cloud that production software and AI systems depend on, so the team choosing the architecture is the team accountable for the bill and the uptime.
The bill and the outage are downstream of one decision
Almost every painful cloud story traces back to the same moment: someone chose an architecture without owning what happened next. A strategist drew a diagram, a reseller recommended a tier, and then a different team had to live with it. These are the three ways that decision comes back.
The monthly bill grows and no one can explain why.
Over-provisioned instances, forgotten environments, and storage no service reads anymore. When the people who set up the infrastructure are gone, the spend keeps running and nobody is confident enough to turn anything off.
It scales fine in the demo and falls over in production.
Architectures sized for a pilot meet real traffic, real data volume, and real concurrent load. The failure shows up as latency, dropped requests, or a 2 a.m. page, long after the team that designed it has moved on.
Cloud was bolted on, not built in.
An application written for one server gets lifted into the cloud unchanged. It runs, but it cannot use the things the cloud is for: elastic scale, managed services, isolated failure. You pay cloud prices for server-era architecture.
Four kinds of cloud work, most of it inside a larger build
Some cloud work stands on its own, like cutting a bill that has run away. Most of it lives inside building, running, or modernizing software. These are the four we are brought in for.
Cloud-native architecture for ground-up builds
When AnAr builds a product from scratch, the cloud architecture is a design decision made alongside the code, not an afterthought handed to operations later.
- Managed services chosen per workload, not per habit
- Elastic scale and isolated failure designed in from day one
- Infrastructure defined as code, versioned with the application
- Spend modeled before the architecture is committed
Infrastructure for agentic and AI systems
Production AI changes the infrastructure question. The cost driver is no longer servers, it is inference. The scaling problem is no longer page traffic, it is concurrent model requests.
- Model hosting: managed endpoints versus self-hosted, decided on latency and spend
- Inference spend controlled with routing, caching, and batching
- Scaling for bursty agentic and document-processing workloads
- Governance, isolation, and audit built into the platform
Cloud cost optimization for the bill you already have
When the monthly spend keeps climbing and no one can fully explain it, the answer is an audit, not a guess. We find what is over-provisioned, idle, or forgotten, and restructure what costs more than it should.
- Right-sizing compute and storage to real usage
- Finding and removing idle and orphaned resources
- Reserved and spot capacity where the workload allows
- Spend made visible, so it stays down after we leave
Cloud inside a modernization or migration
Most cloud work arrives attached to a legacy system. Moving it well means understanding the codebase first, not just re-hosting what already exists.
- Re-architecting, not lift-and-shift, where it pays off
- Migrating without breaking production for the client's users
- SAP and enterprise platform integration where it applies
- Handover that leaves your team able to run it
An AI system has a different cloud bill and a different failure mode
When you put a model in production, the infrastructure questions change. The line item that grows is inference, not compute hours. The thing that buckles under load is concurrent model requests, not page views. And a wrong answer can be a compliance event, not just a bug. These need to be engineering decisions, made by the people building the system.
Model hosting
Managed endpoints get you to production faster. Self-hosting gives you control over latency and unit economics at volume. We choose per workload based on data sensitivity, throughput, and what the numbers say, not on a single vendor relationship.
Inference spend
Token usage is the cost center most teams discover too late. We control it with request routing to right-sized models, caching of repeated context, batching, and the monitoring to see where the spend actually goes.
Scaling and governance
Agentic workloads are bursty and stateful. The platform has to absorb spikes, isolate tenants, retain audit trails, and keep sensitive data inside the boundary the client's compliance team requires.
These are decisions we make for the AI systems we build, sized to your data, your throughput, and the spend you can defend to your finance team. If you are standing up a model in production and the infrastructure question is still open, this is the conversation to start.
Cloud work that had to survive real operations
Cloud is rarely the thing a client names. For most, it runs quietly under a larger build. Here is one engagement where cutting the bill was the whole job, and where to find the rest.
A climbing cloud bill that no one could explain, brought back under control
An Australian fintech product company building in the insurance domain ran a calculation-heavy platform whose monthly cloud spend kept rising with no clear cause. AnAr audited the infrastructure, right-sized over-provisioned compute, removed idle and orphaned resources, and put monitoring in place so the spend stayed visible rather than drifting back up.
For most clients, cloud runs quietly under the build
Across most of our engagements, cloud is the environment the software lives in: the architecture, the deployments, the monitoring, and the bill we keep an eye on. The modernization and migration work where cloud played a central part, across manufacturing, regulated education, and clinical SaaS, sits with our case studies.
The team that picks the architecture is the team that runs it
A cloud reseller earns more when you provision more. A strategy firm hands you a diagram and an invoice, then leaves before the bill arrives. AnAr's incentive is different, because we are the engineers who have to make the architecture work in production and keep it working.
We are vendor-neutral by default
We are not a reseller for any single cloud. We pick services per workload on latency, data residency, and spend, then tell you why. No partnership quota is steering the recommendation.
Every engineer ships with AI in the loop
Our delivery baseline is AI-assisted development on every change, which means infrastructure code, deployment scripts, and config are reviewed and tested with the same discipline as application code.
We model the spend before we commit the architecture
The bill is a design input, not a year-end surprise. We size for real load and tell you what it will cost to run before you are locked into a shape that is expensive to change.
You can run it after we hand it over
Infrastructure as code, documented decisions, and a handover that leaves your team able to operate the system. We build to remove the dependency on us, not to extend it.
Most cloud work connects to a larger engagement
What teams ask before bringing us into their cloud
If your situation is not covered below, a short note to our engineers is the fastest way to a specific answer.
AWS, Azure, or Google Cloud? Which do you recommend?
We pick per workload, not per partnership. AnAr is not a reseller for any single cloud, so there is no quota steering the answer toward one provider.
In practice the decision turns on a few real constraints: where your data is allowed to live, which managed services match your workload, what your team already operates, and what each option costs to run at your volume. Our deepest production experience is on Azure and AWS. We work with Google Cloud as well, though less extensively, and we will tell you honestly where our hands-on depth is strongest rather than talk you into the provider that suits us. The right answer is usually the one that fits your existing operations and compliance boundary, not the one with the best conference keynote. If a single workload runs better split across providers, we will say that too.
We are already on a cloud. Why would we need you?
Being on a cloud is not the same as being cloud-native. Most systems that moved to a cloud run as a single application on a virtual machine, which is the lift-and-shift pattern. It works, but it pays cloud prices for server-era architecture and cannot use elastic scale, managed services, or isolated failure.
Teams bring us in when the bill keeps climbing with no clear reason, when the system buckles under real load, or when they want to run AI workloads that the current shape cannot support. We start by understanding what you have, then recommend only the changes that pay off.
How do you keep inference spend from blowing the budget?
The most common reason AI features overrun in the first quarter is sending every request to the most expensive model. The fix is a tiered design: smaller, cheaper models for routine traffic, frontier models reserved for the requests that genuinely need them, and caching for repeated context.
We model the spend before committing the architecture, then add the monitoring to see where tokens actually go after launch. The estimate is re-baselined against real usage in the first 30 days with your finance team, so the number you plan around is the number you live with.
Do you self-host models or use managed endpoints?
Both, decided per workload. Managed endpoints, such as cloud-hosted model services, get you to production faster and carry less operational overhead. Self-hosting gives you tighter control over latency and unit economics once volume is high enough to justify it, and it can be the only option when data sensitivity rules out a third-party endpoint.
We architect so the model layer is replaceable. That way the hosting decision can change as your volume grows or the model landscape shifts, without rewriting the application above it.
Will this lock us into AnAr or into a single cloud?
Neither is the goal. Infrastructure is defined as code and versioned alongside the application, decisions are documented, and the handover is built so your team can operate the system without us.
On cloud lock-in: some managed services are genuinely worth the dependency because they save more than they cost, and we will tell you which ones and why. Where portability matters to you, we design for it deliberately rather than discovering the constraint later.
Can you take over cloud infrastructure someone else set up?
Yes, and it is a frequent starting point. We begin with an assessment of the existing setup: what is running, what it costs, where the risk sits, and what nobody on the current team can explain anymore. The output is a written picture of the environment and a prioritized list of what to fix first.
From there we can stabilize it, take over ongoing operations, or fold it into a larger modernization, depending on what you need. The same engineering discipline applies whether we built the system or inherited it.
Is cloud a separate engagement, or part of a larger project?
Usually part of a larger project. Cloud architecture decisions live inside building a product, running production AI, or modernizing a legacy system, which is why we do not sell cloud as a standalone consulting deck.
If your cloud need is really about getting a legacy system ready for AI, the AI-Driven Application Modernization engagement is the right entry. If it is about moving an existing system without breaking production, see Cloud Migration. If it is a ground-up build, cloud is simply one of the design decisions we make alongside the code.
Tell us what you are running. We will tell you what we see.
The first step is not a sales meeting. Send us the shape of your situation: what you are building or running, which cloud you are on, and what is actually hurting, whether that is the bill, the load, or an AI workload the current setup cannot carry.
Get an engineer's read on your cloud
Submit your details and AnAr's engineers reply in writing with a first read: where the risk and the spend are likely sitting, what we would look at first, and how a cloud-native or AI-infrastructure engagement would be scoped. The read is not a paid engagement. You decide what happens next once you have it.
Talk to our engineersMoving a legacy system to the cloud? See Cloud Migration → or AI-Driven Application Modernization →