AWS: fit for application and AI workloads, and the proof behind it
AWS fits a project that needs elastic compute, managed databases and queues, and GPU capacity for AI inference, rather than racked hardware or a single all-in-one host. OutsourcingVN is operated by Netbase JSC, and this page separates that platform fit from AWS's own marketing: where it earns its complexity, the account and infrastructure patterns a delivery partner should already run, and the Netbase work actually built on it.
Submit a project Assess an inference workload firstReviewed by David Nguyen (CEO) · Updated 1 Oct 2026
Where AWS fits, and where it doesn't
Good fit
- An application or an AI inference workload that needs managed compute, databases and queues, sized up and down rather than fixed to one box
- A team that wants infrastructure as code so every environment is reproducible, the starting point Cloud Platform and DevOps Engineering builds toward
- An inference workload whose production readiness is still open; the production AI inference assessment settles the serving route before any capacity is sized
- Data residency, access control and audit needs that a named AWS region and account structure can satisfy, checked against the data security and compliance guide
Another route fits better
- A single low-traffic site, where a simpler managed host costs less to operate
- Infrastructure as code is not yet a priority and a PaaS gets a first version live faster
- The model has to run on a device in the field rather than in the cloud; the edge AI deployment assessment covers that separate case
- A hard requirement for an on-premise or client-controlled data centre, which a public cloud account cannot satisfy on its own
Setting up accounts, IAM and the platform
AWS's own IAM guidance is specific about the starting posture: require workloads to use temporary credentials delivered through IAM roles rather than long-lived access keys, require multi-factor authentication for any human user with privileged access, and grant only the permissions a task actually needs, then narrow further as real usage is observed. A delivery partner should already run this way by default, not retrofit it after an incident.
Two delivered patterns show AWS carrying different kinds of workload. The multi-tenant cloud ERP SaaS platform pairs a Next.js front end with a back end that includes Laravel and Strapi, across PostgreSQL, MySQL, MariaDB and MongoDB, all on AWS, for a client and product that are not named. The loyalty and reward shop platform, for a Dubai-based loyalty and rewards technology company that is not named, pairs a Magento 2 Open Source back end with a Next.js front end and was built for AWS or on-premise deployment, so the same application design travels between a public cloud account and a client's own data centre when that is the requirement.
Trade-offs to weigh
- Elasticity against operating discipline. Scaling compute, storage and queue capacity up and down on demand is AWS's main advantage over fixed hardware, and it only pays off when infrastructure as code, monitoring and cost controls are in place to manage that elasticity rather than let it run unwatched.
- Managed services against portability. A managed database, queue or search service removes operational work, and it also ties part of the architecture to that service's interface; weigh which services are easy to replace later against which ones justify the lock-in.
- Breadth of services against the skill to use them well. AWS's catalogue covers nearly every infrastructure need, but a team unfamiliar with its IAM model, networking and service limits can misconfigure any of them; this is why account setup is treated as its own milestone, not an afterthought.
- Multi-region resilience against idle cost. Running redundant capacity across regions improves availability and also runs up a bill when nobody is watching it, which is why cost controls belong in the same conversation as the resilience target.
A worked scenario: adding a recommendation feature to an AWS-hosted store
A retailer already runs its storefront on AWS and wants a product-recommendation feature that reads live catalogue and order data without duplicating it into a separate system.
The first milestone confirms the account can isolate the new workload: a scoped IAM role for the recommendation service, least-privilege read access to the existing catalogue and order data stores, and its own budget alert rather than sharing the storefront's. The second milestone stands up the serving infrastructure, sized against a load test on real catalogue size and traffic shape rather than a vendor benchmark, with the serving route decided the way the production AI inference assessment guide sets out. The third milestone wires observability so a slow or wrong recommendation alerts a named owner before a customer notices, and ships behind a flag so it can be turned off without touching the storefront itself.
Each milestone closes on evidence the next one depends on, which is also how an incident gets contained later; the reliability and incident readiness guide covers that operating discipline once the feature is live.
Where AI fits on AWS
An inference workload on AWS typically runs on GPU-backed compute, a managed endpoint, or a queue for batch jobs that can wait; which of the three fits is a workload question, not a platform question, and the production AI inference assessment above is where that gets decided before capacity is sized. Netbase works with commercial and open-source AI models chosen per project, model-agnostic, with no vendor partnership implied, so a model hosted on AWS goes through the same evaluation, versioning and rollback discipline as a hosted API would.
What to check before committing
- Account and IAM structure. Confirm workloads use IAM roles with temporary credentials rather than long-lived keys, privileged human access requires multi-factor authentication, and permissions start narrow and are reviewed down further as usage is observed.
- What is defined as code today, and what is not. An environment assembled by hand in the console cannot be reproduced or reviewed the same way a version-controlled one can.
- Which managed services the design depends on, and how hard each one would be to replace if a requirement or a cost changes later.
- Who owns the AWS bill, with budgets and alerts wired before launch rather than discovered on an invoice.
Failure modes
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An IAM policy copied once and never narrowed
Broad permissions granted to get a feature working tend to stay broad long after the real access pattern is known.
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Infrastructure assembled by hand under deadline pressure
The first environment built outside infrastructure as code is rarely the last, and drift compounds from there.
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GPU or managed-endpoint capacity sized on a demo
A workload that behaves well in a test often behaves differently at real concurrency and context length, which is why a load test precedes a capacity commitment.
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A single region treated as a resilience plan
Redundancy decided after an outage costs more than redundancy scoped before one.
What Netbase has built on AWS
For a US client that is not named, Netbase has worked since 2020 as offshore development and managing partner on a multi-tenant cloud ERP SaaS platform; its first phase, delivered from 2020 to 2023, covered CRM, real-time messaging, HR, a knowledge base, custom fields and workflows, work and project management and API integrations, on a stack of Next.js, Laravel, Strapi and four database engines, on AWS. See the multi-tenant cloud ERP SaaS platform record. For a Dubai-based loyalty and rewards technology company that is not named, Netbase delivered a headless, multi-store reward shop platform under NDA, built for AWS or on-premise deployment so the client could choose its hosting location without a redesign.
See the loyalty and reward shop platform record
This record covers phase-one scope for agency SMEs; the client and product are not named, and no usage or business result is claimed.
Keep Reading
Netbase delivered a headless multi-store reward shop platform for a Dubai-based loyalty and rewards technology company.
Keep ReadingCommon questions
No. Netbase works with commercial and open-source AI models chosen per project, model-agnostic, with no vendor partnership implied; AWS hosts the serving infrastructure, and the model choice is a separate decision made on its own evaluation.
Not always. A clear separation between production and non-production, with scoped IAM roles for each, matters more at the start than the number of accounts; a larger organisation structure can be layered on once the workload and team have grown into it.
Yes, in most cases. The usual path imports existing resources into code gradually, environment by environment, rather than tearing everything down to start over.
It can be, when the application is designed for it. The loyalty and reward shop platform above was built so the same codebase deploys to AWS or on-premise, which is the pattern to design for when that flexibility is a real requirement rather than a future maybe.
The workload's compute and inference shape, data residency and access rules, the team's existing skills, and whether the budget owner wants a wide managed-service catalogue or a narrower, simpler bill; the fit table above is the starting checklist for that conversation.
Where Netbase applies this stack
Cloud platform and DevOps engineering: one platform for apps and AI workloads
Infrastructure as code, CI/CD, inference hosting, observability and cost controls for apps and AI workloads.
Learn More
From platform decision to project
Bring the workloads AWS would carry, today's account and IAM structure if one already exists, and the inference or data-residency questions that are still open, and Cloud Platform and DevOps Engineering scopes the account, infrastructure-as-code and observability design before a resource is created; the other platform pages sit under Technologies. OutsourcingVN is operated by Netbase JSC and is Netbase's own outsourcing-services platform; submit a project with the workload AWS needs to carry.