Deployment Options
Agentic AI systems can be deployed in many ways. The right approach depends on your security requirements, existing infrastructure, and operational preferences. I support all of these methods and help you choose the right one during planning.
Deployment methods at a glance
From maximum isolation to maximum convenience — and everything in between.
| Method | Description | Security | Ease of Use | Best For |
|---|---|---|---|---|
| Air-Gapped / On-Premises | Systems run entirely on your own hardware with no external network access. Models, orchestration, and data stay within your physical environment. | Classified data, strict compliance, zero external exposure requirements | ||
| Private Cloud (AWS / GCP / Azure) | Deployed to your own cloud accounts — VPCs, private subnets, your IAM policies. Infrastructure-as-code for reproducibility and control. | Organisations with existing cloud infrastructure and DevOps capability | ||
| Managed Cloud / API-Hosted | Orchestration runs on managed infrastructure. Models are accessed via API calls to providers like Anthropic, OpenAI, or Google. Your data flows through external APIs. | Teams that want frontier model access without managing GPU infrastructure | ||
| Software as a Service (SaaS) | Fully managed platforms where agents run on hosted infrastructure. Minimal setup, no infrastructure management. Trade control for convenience. | Quick deployments, non-sensitive workloads, teams without DevOps resources |
Understanding your security surface
Deployment method is only part of the picture. The full security posture of an agentic system depends on multiple layers — and understanding the trade-offs is a key part of the planning process.
Model hosting vs. model access
You can self-host the orchestration framework and still call a model via API — meaning your data leaves your environment for inference even though the "system" is on your infrastructure. Conversely, you can run open-source models locally for complete data isolation. These are independent decisions with different implications.
The API disclosure surface
When a model is accessed via API, your prompts and data are sent to the provider's servers. Most major providers offer data processing agreements, zero-retention policies, and regional endpoints — but data still transits externally. For some workloads this is fine; for others it's a dealbreaker. I help you evaluate this honestly.
Self-hosted models
Running open-source models (Llama, Mistral, Qwen) on your own infrastructure eliminates the API disclosure surface entirely. The trade-off is reduced model capability compared to frontier models, and the need for GPU hardware. For many use cases, self-hosted models are more than capable.
Hybrid architectures
The most practical approach is often hybrid: self-hosted models for sensitive data processing, API-based frontier models for tasks where capability matters more than isolation. I design architectures that route data to the right model tier based on sensitivity.
Orchestration framework security
The agentic framework itself — the system that coordinates tools, manages state, and executes workflows — can be self-hosted or cloud-managed independently of the model layer. Self-hosting the framework gives you full control over execution logs, tool access, and data flow.
Part of the planning process
Finding the right deployment and security posture isn't an afterthought — it's a core part of the planning phase. I assess your regulatory environment, data sensitivity, team capabilities, and budget to recommend an architecture that fits.
Front-end and UI layers
The deployment method for the back-end is independent of how users interact with the system. I work with multiple front-end approaches.
Built-in platform UIs
Many orchestration platforms (n8n, Langflow, FlowiseAI) include their own interfaces for monitoring, triggering, and interacting with agents. These are fast to deploy and often sufficient for internal use.
External SaaS front-ends
Chat interfaces like Open WebUI, LibreChat, or custom Streamlit/Gradio apps can sit in front of your agents. These provide polished user experiences without building a UI from scratch.
Custom-built GUIs
When the use case demands it, I build custom dashboards, chat interfaces, and reporting tools tailored to your specific workflow. Full control over the user experience and branding.
Working with me on deployment
Deployment planning is part of every build, from air-gapped on-premises to managed SaaS. If you already have a system or a trained model and need it deployed properly, that can be the engagement.
Who it's for
- Teams with strict data, residency or air-gap requirements
- Organisations choosing between self-hosted models and API access
- Teams with a working prototype that needs a production home
- Anyone who needs a front end on top of an existing agent system
What you get
- A deployment architecture matched to your security posture
- Model hosting: self-hosted, API or hybrid
- The front-end layer: platform UI, SaaS or custom GUI
- Monitoring and observability
- Runbooks for your team
What I need from you
- Your security and compliance requirements
- Access to the target infrastructure
- The system or model to be deployed
- An IT or infrastructure contact
How it runs
Typically 2–6 weeks when deployment is the whole engagement.
- Week 1
Requirements
Security, infrastructure and who uses it.
- Weeks 1–2
Architecture
Choose method, hosting and front end.
- Weeks 2–5
Deploy
Stand it up and test in your environment.
- Weeks 4–6
Hand over
Monitoring, runbooks and training.
Scoped after a short call and quoted as a fixed price, paid against milestones.