AWS, Azure and GCP architecture that keeps your options, and your costs, under control.
THE CHALLENGE
Committing your entire AI stack to one provider is comfortable until pricing changes, a region lacks the service you need, or a client demands their data sit somewhere specific. Cloud-agnostic architecture is not about using every provider, it is about never being unable to move.
OUR APPROACH
AWS, Azure, GCP. We architect cloud-agnostic solutions that avoid vendor lock-in and optimize cost.
We understand your business requirements, existing systems and operational challenges.
We design an AI approach that fits your workflows, data and technology environment.
We turn the solution into a production-ready system that can be measured and improved.
WHAT'S INCLUDED
A practical engagement designed around your business requirements, technical environment and desired outcomes.
Assess providers against your workload and business requirements.
Design architecture that supports portability across cloud environments.
Build repeatable infrastructure across cloud providers.
Determine appropriate workload placement across providers and environments.
Plan infrastructure around data residency requirements.
Model cloud costs and introduce FinOps guardrails.
Create a plan for moving existing workloads where required.
Establish security and compliance requirements across the architecture.
Design recovery strategies appropriate to the workload.
HOW WE WORK
A structured process keeps every engagement focused, transparent and aligned with business outcomes.
Talk to our teamAudit your data, tech stack, and AI readiness
Architect the solution with security-first principles
Build iteratively with continuous stakeholder input
Ship to production with full observability stack
Monitor, fine-tune, and scale for sustained ROI
EXPECTED OUTCOMES
We focus on outcomes that create practical value for your organization, rather than implementing technology for its own sake.
TECHNOLOGY & PLATFORMS
We choose technologies based on your requirements, infrastructure, scalability and long-term maintainability.
LLM
LLM
Open Source
Open Source
LLM
Framework
Framework
Platform
ML
Cloud
Cloud
Cloud
Infra
Infra
Vector DB
FAQ
Everything you need to know before starting an engagement with Nitiverk.
Ask our teamNot necessarily. Multi-cloud architecture is about maintaining appropriate options and portability rather than using every provider.
The architecture is designed around the requirements of the workload rather than being tied to a single cloud provider.
Yes. Migration planning can be included as part of the cloud architecture engagement.
Data residency is incorporated into workload placement and architecture planning.
The appropriate architecture balances portability against operational complexity rather than pursuing multi-cloud for its own sake.
Ongoing cost monitoring and optimization can be incorporated into the engagement.
EXPLORE MORE
End-to-end delivery of production-grade AI products: from architecture design and model selection through to deployment, monitoring, and iteration.
Deploy powerful open-source language models within your private infrastructure. Full data sovereignty, air-gapped environments, and compliance-ready.
Reduce inference costs by up to 70% through intelligent prompt compression, caching strategies, context management, and model routing architectures.
Whether you are exploring a new AI opportunity or scaling an existing system, let's discuss how Nitiverk can help you move from strategy to production.