Enterprises don’t need to build AI from scratch to add real intelligence to their apps: Oracle Cloud Infrastructure (OCI) offers a portfolio of managed services that cover the major modalities and the orchestration layer. You can analyze images and documents, extract structured data, and process unstructured text with out-of-the-box AI services, while tapping a fully managed Generative AI service to access and customize state-of-the-art Large Language Models (LLM) for chat, summarization, and embeddings—delivered via simple APIs.

OCI’s global regions give you two complementary paths for AI: consume fully managed services where they’re enabled or run your own models anywhere with high-performance GPUs. Managed offerings like Generative AI are rolling out across regions, so you can align proximity and compliance without guesswork, you can always check the up-to-date list of managed AI services regions. OCI regions offer diverse GPU shapes and memory profiles, enabling fine-tuning and low-latency serving next to your data with a consistent architecture and a tight control over governance .

Cross-region API Connections

Using AI services across regions can be a strength when it’s an architectural choice, not an accident: you gain access to the best-fit models and diversify failure domains, while fronting them with multi-region APIs and global routing to keep user latency low and availability high. The key is to treat latency as a first-class SLO—measure TTFT and p95 end-to-end, trace calls, and budget for warmups and retries—so tail behavior doesn’t dominate user experience. Watch the economics, too: cross-region traffic could introduce data egress charges, so design with caching, batching, and locality to help avoid surprise bills. Finally, help address sovereignty requirements by keeping sensitive data resident and moving only prompts or embeddings; techniques like regional sharding and data boundaries lets you addresscompliance without sacrificing global reach.

OCI Local GPU High-Availability Architecture

This is a base High Level Design when you need a resilient, enterprise-grade landing zone for containerized AI apps: a managed Kubernetes control plane (OKE) with multiple node pool options, fronted by regional load balancing and anchored in segmented VCN subnets with policy-driven access. Use it for regulated or mission-critical workloads that demand zonal fault tolerance, consistent networking controls, and application and database high availability, whether you’re running serverless virtual nodes, GPU pools, or both. It fits best when uptime, compliance, and scale are first-class requirements and you want CNCF-conformant Kubernetes without the control-plane toil.

Figure 1. Logical High-level Architecture Diagram

APP Layer: Run your application on OKE Virtual Nodes—a serverless Kubernetes data plane fully managed by Oracle— to deploy containers without managing worker nodes, and scale at pod level on demand. Combine this with a regional load balancer and multi-AD placement for high availability and resilient ingress.

GPU Farm: Back your AI backend with an OKE managed GPU node pool on OCI GPU shapes and run LLM servers that need direct control of accelerators and stable throughput. Use the Cluster Autoscaler to grow/shrink this pool based on unschedulable pods, and spread nodes across fault domains for HA. For LLM serving, engines like vLLM give high throughput via continuous batching and PagedAttention KV-cache management, plus support tensor/pipeline parallelism when a model spans multiple GPUs; TensorRT-LLM (often paired with Triton Inference Server) adds optimized kernels, quantization, and paged/quantized KV-cache to reduce TTFT and memory pressure. On OKE specifically, GPU nodes are provisioned with the required drivers and toolkits, simplifying day-2 operations for LLM workloads. Finally, pick shapes that match context length and concurrency, then tune batching and KV-cache size to your prompt patterns—the fastest wins in production usually come from right-sizing hardware and exploiting KV-cache optimizations rather than model swaps.

Database: Anchor chat history (“model memory”) and business data in Autonomous Database, then add AI Vector Search to store embeddings and run semantic/hybrid search for RAG right in the database. This lets your LLM call out for context with low latency using SQL/PL-SQL (or frameworks like LangChain) and Select AI with RAG, while the service inherits Oracle’s MAA high-availability patterns—automated failover, protection, and rolling maintenance. For Kubernetes apps on OKE, use OCI-IAM workload identity or the OCI Service Operator to connect to ADB without baking credentials, keeping secrets out of pods and simplifying day-2 ops.

Day 0 considerations

“Day-0” is the planning window before anything is deployed—the phase where you translate business requirements (risk, compliance, latency, cost, SLAs) into concrete architectural choices so the platform is fit for purpose from the start. In cloud/Kubernetes parlance, Day-0 covers designing the landing zone, networking and identity boundaries, security baselines, data-residency posture, capacity and cost models, and selecting delivery paths (managed APIs vs. self-hosted)—work that sets up Day-1 deployment and Day-2 operations to be predictable rather than reactive.

Clear Day-0 decisions align risk, cost, compliance, and latency so your AI platform scales without surprises.

Delivery path. Decide whether to consume a managed GenAI API (fastest time-to-value, Oracle-operated SLAs) or self-host inference on Kubernetes for maximum control and locality; most enterprises mix both.

SLAs & SLOs. Define user-visible reliability targets (SLOs) and the metrics that prove them, then align contracts and error budgets; measure p95 latency and TTFT for AI calls explicitly.

Budget. Model unit economics early (e.g., $/1K tokens, $/request, $/conversation) and tie autoscaling ceilings to these limits to avoid surprise bills.

Data constraints. Map data residency and sovereignty requirements to where prompts, outputs, and embeddings live; use regional sharding and minimize cross-border movement. Define data accessed by models to help avoid unauthorized access.

Security minimums. Establish a baseline (CIS Kubernetes Benchmark plus OCI security guidance) for clusters, images, identities, and network egress before the first deploy.

Model selection. Pick models by task, latency, and memory budget; plan for optimizations like quantization to fit hardware and reduce cost without derailing quality.

Infrastructure considerations

Compute + Storage

Standardize your serving layer. Use Triton (dynamic/in-flight batching), KServe (Kubernetes-native routing/auto-scaling), or vLLM (PagedAttention KV-cache efficiency) instead of bespoke servers.

Design for TTFT and tail latency. Warm servers and pre-pull images so first requests don’t stall on weight loading or image downloads.

Autoscale on workload signals. Meter queue depth, p95 latency, tokens/sec—rather than CPU. KServe documents patterns for LLM autoscaling on custom metrics.

Keep pods stateless; keep state decoupled. Store models/checkpoints and artifacts on PVCs via StorageClasses; pre-stage large files to cut cold starts.

Segment pools. Run general API/ingress on a standard node pool and LLM inference on a dedicated GPU pool; KServe shows simple GPU autoscaling patterns.

Observability

Collect all three pillars end-to-end. Unify traces (user request → router → model server), metrics, and logs so you can correlate tail latency with specific hops and components.

Expose model-server metrics. Enable Prometheus scraping on your inference stack: Triton’s /metrics (GPU/CPU/utilization, request counters) and vLLM’s /metrics (TTFT, tokens/sec, queue time, cache usage) provide the core signals for LLM UX and throughput tuning.

Monitor the GPUs themselves. Publish device health (utilization, memory, temperature, ECC, power); alert when health or capacity signals degrade.

Alert on user-visible SLOs. Track percentiles (p95/p99) for end-to-end latency and TTFT; averages hide pain, so alert on percentile breaches alongside backlog/queue depth.

Measure under load, not just in prod. Use performance analyzer to generate traffic and collect server-side metrics, then review CSVs to balance latency vs inferences/sec before rollout.

Integrate with the cloud platform. On OCI, consolidate metrics, logs, and APM traces in Observability & Management for shared dashboards and cross-service drilldowns, take advantage of OKE monitoring and Logging Analytics power.

Resilience

Distribute failure risk by design. Spread replicas across Availability Domains and Fault Domains, and enforce even placement with topology spread constraints (or anti-affinity) so a node/rack/zone loss doesn’t take out capacity.

Prepare for regional events. Use OCI Full Stack Disaster Recovery to orchestrate cross-region failover of the full stack—networking, Kubernetes components, databases, and app tiers—when you need more than zonal resilience.

Survive planned disruptions. Set PodDisruptionBudgets (PDBs) and use controlled rollouts (canary/blue-green) so maintenance, upgrades, or node drains don’t dip below your minimum healthy pods.

Engineer multi-layer HA. Combine Kubernetes placement controls with database HA patterns (Oracle MAA with Autonomous Database) to help protect both stateless services and stateful context (chat history, embeddings).

Verify under load, not theory. Regularly chaos- and failover-test (node/zone drains, rollout pauses) while watching SLOs; keep a small buffer of idle GPUs to absorb spikes during failover before autoscaling catches up.

FinOps

Make unit cost visible. Track $/1k characters (for managed GenAI) or $/request next to product KPIs; OCI Generative AI prices on a per-character basis, which maps cleanly to conversation costs.

Set hard caps, not hopes. Use Compartment Quotas to limit expensive resources (e.g., specific GPU shapes) per team/env from day one.

Budget with early warnings. Create Budgets and alert rules (including hourly forecast-based alerts) to help catch overruns  before month-end.

Right-size and help secure capacity. Reserve baseline GPU capacity with Capacity Reservations; use Preemptible Instances for fault-tolerant batch/embed jobs to help cut spend.

Tag for clean cost allocation. Require cost-tracking defined tags on every resource (compartment, project, environment) so showback/chargeback is reliable and audits are trivial.

Measure normalized cost, not just cloud bills. Track “cost per 1k chars / per request / per token” alongside product metrics; CNCF FinOps guidance recommends normalized cost to tie engineering choices to business value

Next Steps

You now have a clear path to ship resilient, GPU-backed AI on OCI: choose the right delivery model per workload, land it on OKE with a split pool design (Virtual Nodes + GPU nodes), make Day-0 choices explicit, and wire observability, resilience, and FinOps in from the start. The result is faster time-to-value without surprises in latency, uptime, or cost.

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