AI Infrastructure Deployment
Run AI in production with predictable speed, sensible failure modes and a bill you can forecast.
- Typical timeline
- 6 to 12 weeks
- Typical timeline
- Listed below
- 8 deliverables
- Listed below
- No obligation
- Quoted after scoping
- No obligation
Built for
- Platform and infrastructure teams running AI features in production
- Organisations that need models in a controlled environment rather than a public API
- Teams hit by provider outages, rate limits or usage caps
You recognise this
- One provider outage or rate limit takes your AI features down
- You cannot say what AI costs per team or per feature
- Data rules mean some workloads cannot go to a public API
The problem
AI features usually start with one provider and one API key. In production that becomes a single point of failure, a cost nobody can forecast and, for some data, a place it should not go. This package puts a proper layer between your applications and the models they depend on.
What you receive
- 01
Infrastructure assessment
How your applications call models today: traffic, costs, data rules and failure history.
- 02
Inference architecture
A design for routing across hosted and self-hosted models, with failover between them.
- 03
Self-hosted models, where needed
Open models deployed on dedicated GPU infrastructure, including NVIDIA hardware, in your cloud or data centre, for workloads that need a controlled environment.
- 04
Routing and failover layer
One way for your applications to call models, with fallback to other models when a provider is down, slow or at its limit.
- 05
Cost and usage controls
Token visibility and budgets per team or feature, so spend is measured and capped rather than discovered.
- 06
Observability
Dashboards and alerts for speed, errors, usage and cost.
- 07
Hardening and load testing
Tests for provider outages, timeouts, rate limits and retry storms, with the results written up.
- 08
Runbook and handover
What to do when something goes wrong, and a working session with the team that will run it.
Phases and expected timeline
- 01
Assessment
1 to 2 weeks
We measure how models are called today, what it costs and where it fails.
- 02
Design
1 to 2 weeks
We agree the target architecture, including what to self-host, if anything, and the failover rules.
- 03
Build and migration
3 to 6 weeks
The routing layer, any self-hosted models and the cost controls are built, and your applications are moved onto them one at a time.
- 04
Hardening and handover
1 to 2 weeks
Load and failure testing, dashboards and alerts in place, and handover to your team.
Typical timeline: 6 to 12 weeks
Typical range for a single, well-defined scope. The clock starts once access and the people we need are in place. Your quote confirms the timeline after a scoping call.
Before we start
- Access to your cloud accounts or infrastructure, with an engineer who knows them
- Current usage and cost data from your model providers
- Your data residency and security requirements
- A decision owner for any hardware or cloud capacity that needs to be bought
What you can do afterwards
- A provider outage or limit no longer takes your AI features down
- You can see and cap AI spend per team or feature
- Sensitive workloads run in an environment you control
- Your team has the dashboards and the runbook to operate it
What this work is designed to achieve. Results depend on your systems and your data.
Questions about this package
No. Many workloads are best served by hosted APIs. We recommend self-hosting only where data rules, cost or control make it worth it, and we show you the trade-off.
We design for the environment you already run, whether that is a public cloud or your own hardware.
Because the work depends on your systems, your data and how far the scope reaches. After a scoping call we send a written quote that states the scope, the deliverables, the timeline and the cost. Larger enterprise projects are quoted separately, based on scope, integrations, deployment requirements and ongoing support.
Yes. Say so in your enquiry and we will sign one before you share details of your systems.
Yes. For example, an architecture review can lead straight into a build, and we can quote the combined work as one engagement with one plan.
Handover includes the documentation, runbook and working session your team needs to run what we delivered. If you want us to stay involved, ongoing support is quoted separately.
Get a quote for this package
Tell us about your systems and what you need. We reply with questions or times for a scoping call, then send a written quote that sets out the scope, the deliverables, the timeline and the cost.
Larger enterprise projects are quoted separately, based on scope, integrations, deployment requirements and ongoing support.
Prefer email? info@bastionshieldtechnologies.com
Often considered alongside
AI Architecture Review
A second opinion on your AI design before you commit budget to it, with a written report and a prioritised roadmap.
Custom AI Agent Development
A production AI agent built around your workflow, with evaluation, permissions and the admin screens your team needs to run it.