Cloud AI Services in Israel: AWS, Azure, or GCP
Cloud AI services in Israel, without the reseller pitch
If you’re looking for AWS’s, Microsoft’s, or Google’s own managed AI services, their consoles are the right place, not this page: we hold no reseller partnerships and don’t sell hyperscaler seats. If you’re an Israeli company trying to work out who actually builds and owns the system on top of that cloud, that’s what this page is for.
A recurring pattern in AI consulting: the vendor deploys on their infrastructure, and the client discovers at handover that the system, the data pipeline, and the bills all live somewhere they don’t control. We work the other way: DataWise builds in your cloud account, AWS, Azure, or Google Cloud, or entirely inside your closed network. Your identity and access management, your data boundaries, your ownership at handover.
The buying options
Once you’ve decided the system belongs in your own cloud account, there are still three ways to get it built. Configure the hyperscaler’s managed AI service yourself, if you have the in-house engineering to own it end to end. Bring in a large systems integrator, if this is an organization-wide program with its own budget and governance. Or hire a boutique like DataWise for one specific decision, forecasting, inventory, inspection, a knowledge agent, built inside your account and handed over as yours. Three ways to buy AI maps all three routes in more depth; the pattern we see most from Israeli companies at this decision point is a choice between the first option and the third, since a full integrator program is rarely the right size for one operational problem.
What we deploy
The same production systems described across this site, engineered for your environment: demand and sales forecasting pipelines graded monthly against actuals; per-unit inventory intelligence integrated with ERPs; computer vision workloads; and knowledge agents with retrieval over your own content.
On AWS, ML pipelines and model serving in your account, integrating with the data services you already use. On Azure, including organizations whose identity and compliance world is Microsoft-first. On Google Cloud, including teams already invested in its data stack. On-prem and air-gapped, the option most cloud-AI vendors don’t offer: fully closed-network AI, self-hosted models, zero egress. If your requirement is “no internet, ever,” that is a design requirement we know how to meet.
The honest platform note stands regardless of which of the three you pick: we don’t hold reseller partnerships that bias the recommendation. The right cloud is usually the one you already run, and characterization says so explicitly when it’s true.
Security and data residency first
Every deployment starts from the boundary questions: where may data live, what may leave, who approves access. Systems run inside your VPC with your keys; sensitive workloads run in closed networks. Your data is never used to train anything outside your environment, architecturally, not just contractually.
Cost discipline
Cloud AI budgets die from two causes: models larger than the decision requires, and infrastructure running when nothing needs it. Right-sizing both is part of our engineering scope. The engagement itself follows our staged structure, bounded characterization at a fixed price, feasibility on real data, then rollout, so the spend decision is made on evidence.
The first conversation
Tell us what you’re deciding, where your data lives, and what may never leave. In a scoping conversation we’ll map the realistic architecture on your cloud, or inside your closed network, whichever of the buying options above fits, and the number that will tell you it worked.
Are you AWS, Azure, or Google Cloud, or do you resell their AI services?
Neither. DataWise is an independent boutique with no reseller partnerships to any hyperscaler, so there is no seat count or platform meter biasing the recommendation. We build inside whichever cloud account you already run, AWS, Azure, or Google Cloud, or entirely inside a closed network, and hand ownership of the models, code, and pipelines to you at delivery.
What are the buying options for AI on the cloud, concretely?
Three, broadly: configure a hyperscaler's own managed AI service yourself if you have the in-house engineering to own it end to end; bring in a large systems integrator if this is an organization-wide program with its own governance; or hire a boutique that builds one specific system inside your account and hands it over as yours. In our experience, Israeli companies weighing this decision are usually choosing between the first option and the third, a full integrator program is rarely the right size for one operational problem.
Should we build on AWS, Azure, or GCP?
The honest answer: usually on whichever cloud you already run, because your data, identity, and procurement are already there. The differences matter at the edges, specific managed AI services, existing enterprise agreements, data-residency constraints, and that comparison is part of characterization, not a slogan.
Can AI run on our data without it leaving our environment?
Yes, and for us it's a design requirement, not an obstacle. We deploy inside your own cloud account (your VPC, your keys) and, when required, entirely inside a closed network with no internet at all, self-hosted models included.
What do cloud AI services cost?
Two separate costs, and conflating them is how budgets blow up: our engagement (a bounded fixed-price characterization, then a binding quote per stage) and your cloud bill (which we design deliberately, right-sizing models and infrastructure is part of the work, not an afterthought).
Managed AI service or custom model, how do we choose?
By the decision the system serves. Managed services win when your need matches what they do; custom models win when your data or constraints are the point. Characterization answers this per case, we sell neither hyperscaler seats nor complexity for its own sake.
Do we stay locked into you or the cloud afterwards?
Not into us: models, code, and pipelines transfer to your ownership at handover, documented so any good engineer can run them. Cloud lock-in is a design decision we make explicit during characterization, portable where it's worth the cost, native where it isn't.