AI cloud solutions that use the commitments you have already made
Our AI cloud solutions show you whether AI belongs inside the AWS, Azure or Google Cloud account you already run. We weigh commitments, data requirements, procurement and operating costs before recommending a platform, so you avoid paying twice for capability.
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What are AI cloud solutions?
AI cloud solutions deploy artificial intelligence workloads on major cloud platforms using their managed AI services, AWS Bedrock and SageMaker, Azure OpenAI and Machine Learning, or Google Vertex AI. As a result, this keeps data within the customer’s own cloud account, uses existing commercial commitments, and inherits established security controls.
Adding a vendor when you already pay for the capability
Organizations with substantial cloud commitments frequently add a separate AI vendor, triggering a new security review, a new procurement cycle, a new data processing agreement, and spend that does not count toward existing commitments.
Meanwhile the same models are frequently available through the cloud platform they already run, inside the account and security perimeter their compliance team has already approved.
Deploy inside the perimeter you already have
Managed AI services on AWS, Azure and Google Cloud run inside your own account, under your existing IAM and network controls, billed against commitments you have already made.
Consequently, one decision removes a security review, a procurement cycle and a compliance question. That is frequently worth more than any marginal difference in model capability.
What our AI cloud solutions include
Platform AI services first: Bedrock, Azure OpenAI or Vertex AI provide model access inside your account with the platform’s own controls, logging and billing.
Then the supporting infrastructure: vector storage, data pipelines, serving and orchestration, built with the platform’s managed services where they fit and self-managed where they do not.
Security architecture also matters here: private networking, IAM scoping, encryption and audit logging integrated with what your organization already runs rather than bolted alongside.
Finally, there is cost governance, because cloud AI spend is easy to accumulate invisibly across accounts and teams.
Our cloud AI services cover AWS AI deployment, Azure AI solutions and Google Cloud AI services. Each build delivers cloud-based AI solutions inside the account you already run, with AI cloud integration into your existing security and billing.
- Managed AI service deployment: Bedrock, Azure OpenAI, Vertex AI
- Private networking, IAM scoping and encryption integrated with your standards
- Vector storage and retrieval infrastructure within the platform
- Data pipelines using the platform’s own managed services
- Cost governance: tagging, attribution, budgets and alerting
- Multi-cloud and portability design where required
Who should run AI inside their own cloud?
Organizations with meaningful cloud commitments or committed-use discounts, where routing AI spend through the platform has direct commercial value.
In addition, regulated organizations whose compliance position is already established for their cloud provider, where using a separate AI vendor means reopening a settled question.
Running AI on your own cloud platform gives you scalable AI infrastructure without onboarding a new vendor, and keeps spend inside the commitments you have already made.
- Organizations with significant AWS, Azure or Google Cloud commitments
- Regulated companies whose cloud compliance position is already approved
- Teams whose data residency requirements rule out external AI vendors
- Enterprises where adding a vendor means a nine-month procurement cycle
- Companies wanting AI spend visible within existing cost governance
- Organizations standardizing AI deployment across business units
What AI cloud solutions save you
Spend against existing commitments
AI usage counts toward committed-use discounts rather than becoming separate vendor spend.
No new security review
Deployment inside an account and perimeter your security function has already approved.
Procurement already done
No new vendor onboarding, data processing agreement or contract negotiation cycle.
Data stays in your account
Processing within your own cloud boundary under your own IAM and network controls.
Unified cost governance
AI spend visible in the same tagging and attribution system as the rest of your infrastructure.
Familiar operations
Monitoring, logging and incident response using the tooling your team already operates.
Procurement and compliance problems this solves
New vendor triggering full procurement
Nine-month cycles blocking AI adoption. Instead, platform deployment uses existing agreements.
Security review from scratch
A new perimeter to assess. However, deployment inside an approved account avoids it.
Spend outside commitments
AI costs not counting toward committed-use discounts. By contrast, platform routing captures them.
Data residency constraints
External vendors ruled out by compliance. Instead, cloud deployment stays inside the boundary.
AI spend invisible
Costs accumulating across accounts untracked. Tagging and attribution, therefore, make them visible.
Inconsistent deployment by unit
Each team choosing differently. In contrast, platform standardization creates consistency.
What every cloud AI deployment includes
Everything below is standard on every engagement. In other words, nothing here is an upsell that surfaces halfway through the build.
Platform service deployment
AWS Bedrock and SageMaker, Azure OpenAI and ML, or Google Vertex AI configured within your account.
Security architecture
Private networking, VPC endpoints, IAM scoping, encryption and audit logging integrated with your standards.
Vector and retrieval infrastructure
OpenSearch, pgvector on managed Postgres, or platform-native vector services depending on fit.
Data pipeline services
Managed pipeline services configured for AI workloads rather than assembling infrastructure from scratch.
Cost governance
Tagging strategy, per-workload attribution, budget alerts and anomaly detection integrated with existing cost tooling.
Infrastructure as code
Terraform or platform-native IaC so deployments are reproducible and reviewable.
Multi-region and resilience
Regional deployment for latency or residency, with failover design where availability requirements demand it.
Portability layer
Abstraction so platform choice remains reversible despite deep integration.
AWS, Azure or Google Cloud: which AI services do we use?
We are vendor-neutral. Instead, we choose models and infrastructure on accuracy, cost per task, latency, and where your data is allowed to live.
How we deliver AI cloud, step by step
Every AI cloud engagement runs through the same five stages, starting from the platform you already use. That way, you always know what comes next.
Discovery
First, we review your cloud commitments, data residency rules and approved compliance boundary, and audit existing security controls.
AI Strategy
Next, we compare Bedrock, Azure OpenAI, Vertex AI and direct providers on cost, procurement, compliance and time to value.
Pilot Build
Then we deploy one workload inside your account as a fixed-scope pilot, under your existing IAM controls.
Implementation
After that, we add private networking, vector storage, data pipelines and infrastructure as code, integrated with your standards.
Optimization
Finally, we track tagged AI spend per workload, tune caching and model routing, and report savings monthly.
Cloud platform or direct model provider: which should you use?
The models are often the same either way; the difference is procurement, compliance and commercial terms.
- A cloud platform usually wins if you hold meaningful cloud commitments, work in a regulated industry, or face a long vendor onboarding process.
- A direct model provider is simpler if you have no cloud commitment and want the newest models the day they release.
For example, on a recent engagement, the platform route turned a nine-month procurement block into an eleven-week delivery.
How much does an AI cloud project cost?
AI cloud projects are priced on the platform you already use, your security standards and the workloads you plan to run. You get a fixed written price before we start, built around the cloud commitments you already pay for, so you avoid paying twice for the same capability.
Use the free 30-minute call to tell us which cloud you run and what you want AI to do. We will show you how much your existing commitments can cover and which platform route is fastest to approve.
- A written savings estimate before any paid work
- Fixed scope and fixed price, agreed up front
- Full ownership of everything we build for you
- An honest recommendation when the numbers do not work
Figures are internal measurements across recent engagements, reported to every client monthly in writing.
AI cloud FAQs
Should we use a cloud platform or go direct to a model provider?
If you hold meaningful cloud commitments, are in a regulated industry, or have a lengthy vendor onboarding process, the platform route usually wins, not on capability but on procurement, compliance and commercial terms. On a recent engagement it turned a nine-month procurement block into an eleven-week delivery. If you have no cloud commitment and want the newest models immediately, direct API access is simpler.
Which cloud platform is best for AI?
Mostly the one your data already lives in. Adjacency to your data estate, your existing commitments and your approved compliance boundary matter more for most organizations than marginal model capability differences. AWS Bedrock offers the broadest model choice, Azure suits Microsoft-centric estates, and Vertex AI has strong BigQuery and Workspace adjacency.
Does data stay in our cloud account?
With the managed AI services, yes, processing happens inside your own account or tenancy, under your IAM and network controls, and the platforms’ terms state that data is not used to train their models. This is frequently the deciding factor for regulated organizations, because it means the compliance question is one they have already answered.
Do we lose access to the newest models?
Somewhat. Models typically reach cloud platforms weeks after direct API availability. However, whether that matters depends on your use case. For most production workloads it does not, because the procurement and compliance advantages outweigh it. If you need frontier capability the day it releases, direct API access is the right choice.
How do you keep cloud AI costs under control?
Tagging and per-workload attribution from the start so spend is visible rather than accumulating invisibly across accounts, budget alerts and anomaly detection, and caching and model routing at the application layer. Cloud AI spend is particularly easy to lose track of because it appears within a much larger bill.
What does cloud AI deployment cost?
It depends on your cloud platform, your security requirements and the AI workloads you want to run. We start with a short architecture review, then give you a fixed written price for the deployment, built to use the commitments you already have. Ongoing operations are available monthly if you want them. A free 30-minute consultation is the fastest way to see what your account can already cover.
Ready to scope your AI cloud solutions project?
Book a free 30-minute consultation. Bring one workflow. You will leave with a realistic estimate of what it would cost to automate and what it would save.