Infrastructure & Cloud
Production AI infrastructure that finance funds and security signs off.
Take your AI prototype from a Streamlit demo to a hardened, observable, cost-controlled production system. We design, deploy, and operate AI infrastructure across AWS, Azure, Google Cloud, and Alibaba — with data residency for all nine Asian markets.
The problem we solve
Your AI proof-of-concept ran on someone's laptop. Production needs an answer.
You shipped a Streamlit demo of a RAG-powered support assistant in two weeks. Leadership loved it. Then you tried to put it in front of 12,000 weekly users and discovered: no autoscaling, no observability, no rate limiting, no audit trail, costs spiking unpredictably, and no clear answer to where the model is hosted relative to your data residency commitments.
A16Z's 2024 Cost of AI Compute analysis tracked 200 enterprise AI deployments and found that infrastructure cost overruns hit 3.2x the original estimate when deployed without evaluation, observability, and cost controls in place from day one.[^1] Gartner separately reports that 38% of enterprise AI workloads in APAC are blocked at the production stage by infrastructure or compliance gaps.[^2]
We design, deploy, and harden the AI infrastructure stack — from data pipelines through model serving to observability — that turns a promising prototype into a system finance is willing to fund and security is willing to sign off on.
Who this is for
- The Head of Engineering at a 400-person SaaS company in Hong Kong who has shipped two AI features into a production stack with no evaluation, no caching, and a model bill that doubled month-on-month.
- The CIO of a regulated financial institution in Tokyo who needs all model inference to stay in Japan, with audit logs, model lineage, and SOC2-compliant access controls.
- The Head of Data at a 700-person retail group in Malaysia building a unified data platform that supports BI today and AI workloads tomorrow.
Outcomes
Predictable AI cost. Across the last 19 infrastructure engagements, model inference cost dropped a median 47% in the first 90 days post-deployment of caching, model-tier routing, and prompt compression — without measurable accuracy loss. One Hong Kong SaaS client's monthly AI spend dropped from US$58,000 to US$27,000 while serving 23% more requests.
Production-stable latency. P99 latency targets met or beaten on 17 of 19 deployments. Two cases required architecture revision (adding a regional cache layer in one, splitting an over-loaded model in the other) inside the first 30 days.
Audit-ready compliance evidence. Every infrastructure deployment ships with a compliance evidence pack indexed to SOC2 controls, ISO 27001 Annex A controls, and the relevant regional law. Average customer security questionnaire turnaround post-handover dropped from 14 days to 3.
Quantified business case for AI infrastructure spend. IDC estimates that for every US$1 spent on production-grade AI infrastructure, mid-market enterprises see US$3.50 in avoided rework, downtime, and security remediation over the following 24 months.[^3] Our clients hit a US$2.80-US$4.10 range across the last 12 months of measured outcomes.
Engagement formats
| Tier | Duration | US$ price band | Best for |
|---|---|---|---|
| Starter — Infrastructure Audit | 4 weeks | US$24,000 - US$48,000 | Reference architecture, gap analysis, prioritised roadmap, compliance posture review. No build. |
| Scale — Production Infrastructure Build | 12-18 weeks | US$120,000 - US$320,000 | Full build of data, serving, retrieval, observability, and governance layers. Compliance evidence pack included. |
| Strategic — Platform Partner | 12 months | US$280,000 - US$680,000 | Quarterly architecture reviews, on-call SRE escalation, multi-region rollout, FinOps for AI workload. |
All tiers include the post-deployment cost optimisation review at month three.
Our approach
Five steps from current-state assessment to a hardened, observable, cost-controlled AI infrastructure layer.
1. Infrastructure assessment (week 1-2)
We audit your current stack: data warehouse, model serving, vector store, observability, identity, and cost. We score against a 32-point reference architecture. Output: a gap analysis with prioritised fixes and US dollar estimates for each.
2. Reference architecture and roadmap (week 2-3)
We design the target architecture across five layers — data, model serving, retrieval, observability, governance. Each layer documents the chosen technology, the data residency posture, the cost model, and the failure mode. Decisions follow Wardley Mapping principles — commodity layers go to managed services, differentiating layers stay in your control.
3. Build and migration (week 3-14)
We deploy infrastructure as code (Terraform for cloud resources, Helm for Kubernetes-hosted services). Typical deployments use AWS Bedrock, Azure OpenAI, or Google Vertex AI for managed model access; Snowflake, Databricks, or BigQuery for the data layer; pgvector or Qdrant for vector storage; Datadog, Grafana, or Langfuse for observability. Cost controls (per-tenant budgets, per-model rate limits, prompt caching) are first-class concerns, not bolt-ons.
4. Hardening and compliance (week 12-16)
We implement audit logging, model lineage, prompt-injection guardrails, and data residency controls. We deliver evidence packs aligned to SOC2, ISO 27001, MAS-TRM, and the relevant regional data protection laws (PDPA Singapore, APPI Japan, PIPL China, UU PDP Indonesia). The pack ships ready for your auditor or your customer's security questionnaire.
5. Handover and SRE coaching (week 14-18)
We hand over to your platform or SRE team with runbooks, dashboards, and an on-call playbook. We coach for 30 days post-handover and provide quarterly architecture reviews for the next 12 months.
What you get
- 32-point infrastructure assessment with gap analysis
- Reference architecture across data, serving, retrieval, observability, governance
- Terraform / Helm infrastructure as code in your repository
- Model serving stack (managed or self-hosted, single or multi-region)
- Vector storage and retrieval (pgvector, Qdrant, or Pinecone)
- Cost controls: budgets, rate limits, caching, model-tier routing
- Observability stack: latency, errors, cost, accuracy drift
- Audit logs, model lineage, prompt-injection guardrails
- Compliance evidence pack (SOC2, ISO 27001, regional laws)
- Runbooks, dashboards, 30-day SRE coaching
Where this service shows up
Industries and APAC markets where AIMenta delivers this pillar most often.
Beyond this pillar
Cross-reference our practice depth across the other pillars, all 10 industries, and 9 Asian markets.
Other service pillars
By industry
By Asian market
Proof in market
Common questions
Which cloud providers do you work with for AI infrastructure?
AWS, Azure, GCP, and Alibaba Cloud as primaries. We are vendor-neutral — selection is driven by your existing footprint, data-residency requirements (especially CN, ID, VN), and which provider has GPU capacity in your target region. We will tell you when single-cloud is wrong for your workload.
Can you help with on-premises or air-gapped AI deployments?
Yes — primarily for financial services, defense, and healthcare clients with hard sovereignty constraints. Stack is typically NVIDIA DGX or H100 hosts, vLLM for inference, and an internal model registry. We have shipped six air-gapped deployments across HK and SG since 2024.
How do you handle data sovereignty across APAC jurisdictions?
Per-market data plane: CN data stays in CN regions (Alibaba or Tencent), JP/KR data stays in-country, ASEAN data lands in SG by default. Cross-border movement requires written justification logged to an audit trail. We map this against your DPO sign-off before the first deployment.
What is your approach to GPU capacity planning?
We size against measured P95 inference QPS, not vendor estimates. Reserved capacity for steady baseline, on-demand for peaks, and a fallback model on a smaller GPU class for graceful degradation. We benchmark Anthropic and OpenAI hosted APIs as a cost ceiling on every plan.
Ready to mentor your AI?
Tell us where you are. We'll tell you the smallest engagement that gets you to your next milestone.