Stack 04 · Cloud & DevOps
Shipping is only the beginning. Aviox designs deployment, monitoring, rollback, secrets, infrastructure and cost controls around the availability and operating requirements of the application.
How we choose
Three properties decide whether an AI system survives its first bad week. None of them are about the model.
Reproducible means the environment comes from the repository, not from someone's memory of what they clicked. Observable means that when answer quality drops, something notices before a customer does. Reversible means a release can be rolled back in minutes, which is the only thing that makes shipping often actually safe.
We also treat inference cost as an engineering property rather than a finance problem. Caching, routing cheap requests to cheap models, batching where latency allows, and a dashboard that shows spend per workflow — because an AI feature with unmeasured unit economics is a liability waiting for a volume increase.
Non-negotiables
The toolchain
Each row says what the tool is used for and links to related pages. Named tools are examples of what can sit in each layer, not a list of partnerships or certifications.
Where most of what we run lives. Managed databases, container orchestration, queues and model hosting — chosen service by service rather than adopting the whole catalogue by default.
Considered where a client's estate already runs on Microsoft. Support for Azure is confirmed during scoping against the project's requirements, with the same Terraform discipline, CI pipelines and observability on top.
One build artefact that runs the same way on a laptop, in CI and in production. Unglamorous, and the reason environment-specific bugs are rare rather than routine.
Where the workload genuinely needs it — multiple services, autoscaling inference, self-hosted models. We are equally happy on managed container services when it does not.
Every environment defined in the repository and reviewed like application code. The answer to both 'why does staging behave differently' and 'can we run this in another region'.
Tests, type checks, security scans and — for anything with a model in it — the evaluation suite, all gating the merge. If the evals regress, the pipeline fails like any other test.
Traces through the whole agent path, not just the HTTP request — which tool was called, what was retrieved, what the model returned, how long each step took and what it cost.
Response caching, model routing by request complexity, batching where latency allows, and spend attributed per workflow and tenant so the economics are a known quantity.
Built on this stack
The useful question about a technology list is what has been built with it. These are examples of where the practices on this page are applied.
An Aviox-operated product where deployment, monitoring and inference cost are ours to manage, which keeps cost control, caching and routing practical rather than theoretical.
Automated tests, security checks and AI evaluations placed in the release path before production, with the rollback defined up front.
Deployment into an account the client controls, with infrastructure defined in code so residency and access control are properties of the environment.
Note Products we run ourselves patterns are starting points configured to your data and rules Read the case study
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Talk to an engineer about your architecture, or get a quote for the work.
In the field
Three sectors where this particular layer carries the most weight. Each page shows the five-step automation pattern in that sector's own vocabulary.
Lending, KYC and servicing workflows with auditable review.
Explore Industry 03Referral and authorization administration with human review.
Explore Industry 06Permit and case workflows with audit records and human approval.
ExploreAlso All ten industries run on some combination of these four stacks
Connected
No system we build uses only one of these four layers. Here is the service line that leads the work, and the stacks that sit either side of this one.
Services that use this stack
Model CI/CD, versioned datasets, canary rollouts and cost-aware inference — the plumbing that keeps it up.
Explore Service 09Prompt-injection defence, PII redaction, model risk registers and role-scoped retrieval your regulator can read.
Explore Service 08Monoliths re-platformed into cloud-native services, business rules extracted with AI assistance, APIs published.
ExploreThe other stacks
Questions
Asked most often by engineering and operations teams.
The major providers, including the one you already use. We prefer your existing platform over a new dependency.
Through repeatable builds, automated tests, staged rollouts and an easy way back, so a bad release is a small event rather than an incident.
Ownership is agreed up front and written into the runbook, so it is clear who responds, how they are alerted and when to escalate.
With budgets, tagging, right-sizing and regular reviews, so cost is visible per workload rather than discovered on the invoice.
Request a quote
Tell us how you deploy today and what keeps you up at night. We will suggest where release gates, observability and rollback would help most.
