Cloud AI agent deployment services

Q: Should we use a managed agent runtime or run agents on our own Kubernetes?

Managed runtimes such as Bedrock AgentCore or Azure AI Foundry Agent Service handle scaling, sessions, and much of the security plumbing, so they usually reach production faster. Self-managed runtimes on EKS, AKS, or GKE give full control over networking, versions, and portability, at the cost of more operations work.A useful test: if your platform team already runs Kubernetes to strict internal standards and portability matters for DORA, self-managed often wins. If speed matters more and the managed service meets your controls, start managed.

Q: Can AI agents run in the cloud without public internet access?

Yes. Agents can run inside a private network and reach models, tools, and data through private endpoints, such as AWS PrivateLink or an Azure virtual network. Public egress stays closed unless a policy allows a specific destination.Check one thing early: whether each model and tool your agent needs is reachable privately in your chosen region. That list decides how much of the design is standard and how much needs workarounds.

Q: How do you handle data residency when a model isn't available in our region?

We first check which models are available in the regions your data may use, then design around that list. Options include a different model with local availability, a self-hosted open-weight model in your region, or on-premise AI deployment for the most sensitive workloads.The data classification drives the answer, so bring your data owners into the first workshop rather than the last.

Q: What does DORA mean for AI agents running on AWS, Azure, or Google Cloud?

In November 2025, European supervisors named AWS, Microsoft, and Google Cloud among the first 19 critical ICT third-party providers under DORA. Supervision of those providers doesn’t remove your own duties: financial entities still manage concentration risk, keep exit strategies, and document how critical services depend on each provider.For agents, that means an exit plan per critical agent and a design that doesn’t tie agent logic to one provider’s proprietary services. Your legal and compliance teams set the exact scope; we build the technical evidence they need.

Q: Which cloud services can we use for agents that process PHI?

Only services your cloud provider lists as HIPAA eligible and that your business associate agreement covers. Amazon Bedrock AgentCore, for example, is HIPAA eligible. Every other service the agent touches, from storage to logging, has to meet the same bar.We map each component against that list during design, so nothing unapproved ends up in the data path.

Q: When is on-premise deployment the better choice?

On-premise or air-gapped deployment fits when policy forbids any cloud processing for a data class, or when latency and hardware control matter more than managed services. Many enterprises end up hybrid: most agents in the cloud and the most sensitive ones on-premise.Our on-premise AI deployment team covers that side, and both setups can share one architecture and one set of controls. If you’re unsure which fits, contact us and we’ll map it with you.

Your cloud already passed a security review, and your agents haven’t yet. Relevant Software deploys AI agents on AWS, Azure, or Google Cloud inside your own landing zone: private networks, data kept in your region, and an exit plan your regulator can read.

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Our cloud AI agent deployment services

Agent runtime selection
Landing zone for AI agents
Data residency and model availability
Agent identity and access
Infrastructure as code and CI/CD
Portability and exit planning

Agent runtime selection
Landing zone for AI agents
Data residency and model availability
Agent identity and access
Infrastructure as code and CI/CD
Portability and exit planning

Agent runtime selection

Each cloud now offers a managed agent runtime, and each one suits some agents better than others. We pick the runtime for each agent against your security, cost, and portability requirements, including systems built through our multi-agent systems development work.

Managed runtimes: Amazon Bedrock AgentCore, Azure AI Foundry Agent Service, Gemini Enterprise Agent Platform
Self-managed runtimes on EKS, AKS, or GKE when you need full control
A written decision record your architects and auditors can review

Landing zone for AI agents

Agents run inside the network boundary your platform team already governs, not beside it.

Private networking with VPC or VNet isolation and private endpoints, such as AWS PrivateLink
No public internet egress unless a policy allows it
Encryption with keys your organization controls

Data residency and model availability

Models and agent services aren’t available in every region, so region choice becomes an architecture decision. We map which models can run where your data is allowed to live.

Region and sovereign cloud selection per data classification
Model choices limited to the regions your policies allow
Documented data flows for your DPO and compliance team

Agent identity and access

Every agent gets its own identity with the smallest set of permissions its task needs, never a shared service account.

A dedicated role per agent in IAM or Microsoft Entra
Secrets management and credential rotation
Action logs tied to each agent’s identity

Infrastructure as code and CI/CD

The whole agent environment is defined in code, so staging and production match and every change goes through review.

Terraform modules for runtimes, networking, and access
Pipelines that promote agents from staging to production
Change history your auditors can trace

Portability and exit planning

Financial regulators expect a plan for leaving a critical cloud provider. We build agents so that moving is a planned project, not a rewrite.

Agent logic decoupled from provider-specific services
A documented exit plan and a fallback environment where needed
Evidence for DORA third-party risk reviews

AWS, Azure, or Google Cloud: where your agents fit

Amazon Web Services
Microsoft Azure
Google Cloud

Amazon Web Services

Amazon Bedrock AgentCore provides a managed agent runtime, gateway, and memory. Agents inside your VPC reach AgentCore Gateway through AWS PrivateLink without crossing the public internet, and AgentCore is HIPAA eligible and in scope for SOC 2 and ISO 27001.

Microsoft Azure

Azure AI Foundry Agent Service runs agents in a managed, session-isolated runtime with bring-your-own virtual network. Agents get their own identities in Microsoft Entra, with built-in tracing and evaluation for governance.

Google Cloud

Gemini Enterprise Agent Platform, formerly Vertex AI, brings the Agent Development Kit for building agents and a central place to register, manage, and govern the custom agents your teams deploy.

The cloud is already approved. The agents aren’t yet. We make the second match the first.

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Where cloud agent deployment helps

When a bank runs under DORA
Regulators now supervise the largest cloud providers directly, and they still expect each bank to manage its own dependence on them. Deployment includes the exit plan and concentration controls your third-party risk team will ask for.
When agents touch patient data
Agents that read PHI can only use services covered by your business associate agreement. We build the architecture from HIPAA-eligible services and document where every data flow goes.
When data spans several jurisdictions
An insurer with customers in several countries can't send every request to one region. Region-aware deployment keeps each customer's data where local rules require.
When a pilot runs on a public API
The pilot worked on a public model endpoint, and production traffic can't. We move it into your private network without rebuilding the agent, as one step of a wider enterprise AI agent deployment.

What you gain from cloud agent deployment

Security review moves faster
Agents run on infrastructure your security team already knows and approved, so the review covers what's new rather than everything.
Data stays inside your perimeter
Private networking and region controls keep customer data within the boundary your policies define.
No lock-in to a single provider
Portable agent logic and a documented exit plan keep your options open as prices, models, and rules change.
Costs you can forecast
Runtime, model, and network costs are modeled per agent before launch, so the bill matches the plan.

How a cloud AI agent deployment engagement works

A review of your cloud and data
Your landing zone, data classifications, and target agents go through a short review, and you leave with a list of constraints the deployment must meet.
A runtime and architecture decision
Runtime, regions, networking, and identity reach a design your platform and security teams sign off.
A landing zone built as code
Networking, access, and encryption are defined in Terraform and reviewed like any other infrastructure change.
A first agent in staging
One agent runs in a staging environment that mirrors production, with logging and access controls already in place.
A production cut-over
The agent moves to production through your change process, with rollback ready and AI agent monitoring in place from day one.
A handover your team owns
Code, runbooks, and the exit plan transfer to your team in a package they operate without us in the room.

Why choose Relevant for cloud AI agent deployment

Vendor-neutral by design

AWS, Azure, or Google Cloud. We recommend the runtime that fits your agents and your existing cloud, not the one we prefer to sell.

Certified security and compliance

ISO 27001, GDPR, and HIPAA-aligned delivery. Landing zones and access controls are built to the standards your reviewers already accept.

Experience in regulated industries

Open Banking, HL7 FHIR, and PSD2 delivery experience, so residency and third-party risk questions get answered from projects already shipped.

One team from design to handover

96% employee retention. The engineers who design your landing zone stay on through cut-over.

AI-assisted delivery on every engagement

Up to 50% faster delivery and up to 20% fewer bugs through AI-assisted development, with every change passing senior review.

A track record clients verify

200+ companies have built and scaled products with Relevant, and the case studies below are the checkable subset.

What clients say about Relevant

4.9 is our Clutch
average

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FAQ

Should we use a managed agent runtime or run agents on our own Kubernetes?

Can AI agents run in the cloud without public internet access?

How do you handle data residency when a model isn't available in our region?

What does DORA mean for AI agents running on AWS, Azure, or Google Cloud?

Which cloud services can we use for agents that process PHI?

When is on-premise deployment the better choice?

Get your AI agents running in the cloud you already trust.

Our cloud AI agent deployment services

Agent runtime selection
Landing zone for AI agents
Data residency and model availability
Agent identity and access
Infrastructure as code and CI/CD
Portability and exit planning

Agent runtime selection
Landing zone for AI agents
Data residency and model availability
Agent identity and access
Infrastructure as code and CI/CD
Portability and exit planning

Why choose Relevant for cloud AI agent deployment

Vendor-neutral by design

AWS, Azure, or Google Cloud. We recommend the runtime that fits your agents and your existing cloud, not the one we prefer to sell.

Certified security and compliance

ISO 27001, GDPR, and HIPAA-aligned delivery. Landing zones and access controls are built to the standards your reviewers already accept.

Experience in regulated industries

Open Banking, HL7 FHIR, and PSD2 delivery experience, so residency and third-party risk questions get answered from projects already shipped.

One team from design to handover

96% employee retention. The engineers who design your landing zone stay on through cut-over.

AI-assisted delivery on every engagement

Up to 50% faster delivery and up to 20% fewer bugs through AI-assisted development, with every change passing senior review.

A track record clients verify

200+ companies have built and scaled products with Relevant, and the case studies below are the checkable subset.

Get your AI agents running in the cloud you already trust.

Cloud AI agent deployment services