What is Cloud Infrastructure Audit?

Ensure your cloud environment is optimized for AI workloads before scaling up compute spend. Oaken AI provides cloud infrastructure audit services for established businesses looking to implement AI that delivers measurable results.

Who needs cloud infrastructure audit?

Cloud Infrastructure Audit is designed for established businesses — professional services firms, local businesses, agencies, and e-commerce companies — that want to save time and reduce manual work through AI automation. If your team spends hours on repetitive tasks each week, this service can help.

How long does cloud infrastructure audit take to implement?

Oaken AI delivers working systems in as little as 7 days. We start with your highest-impact bottleneck and build a functional system before expanding to other areas. No multi-month assessments or slide decks — just results.

Do I need technical expertise for cloud infrastructure audit?

No. Oaken AI handles the entire technical implementation. You do not need to hire an AI team, learn to code, or understand machine learning. We build systems your existing team can use and maintain.

Cloud Infrastructure Audit | Oaken AI

Infrastructure Assessment

AI workloads place unique demands on cloud infrastructure that differ significantly from traditional web application hosting. Model inference requires GPU instances or specialized accelerators. Training pipelines need burst compute capacity. Data processing stages demand high-throughput storage and networking. Most organizations discover these requirements reactively, resulting in over-provisioned resources, unexpected bills, and performance bottlenecks. A proactive infrastructure audit ensures your cloud environment is right-sized for AI workloads before you commit to scaling.

Resource Utilization

We analyze compute, storage, and networking utilization across your AWS, Azure, or GCP environment. AI workloads often show extreme utilization patterns: GPUs idle 80% of the time during development then spike to 100% during training, storage grows linearly with dataset size, and network bandwidth becomes a bottleneck during data transfers between regions. We identify waste and right-sizing opportunities.

Cost Optimization

Cloud AI costs escalate quickly. A single p4d.24xlarge instance on AWS costs over $30 per hour. We audit your spending patterns, identify reserved instance opportunities, evaluate spot and preemptible instance suitability for training workloads, and recommend architectural changes that reduce cost without sacrificing performance. Typical findings save 30 to 60 percent on AI compute costs.

Scaling Policies

AI inference workloads need auto-scaling policies tuned to their specific latency and throughput requirements. We review your scaling triggers (CPU, memory, request queue depth, custom metrics), scale-up and scale-down timing, minimum and maximum instance counts, and warm pool configuration. Poorly tuned scaling causes either wasted spend during low traffic or degraded experience during spikes.

Disaster Recovery

Model artifacts, training data, and configuration represent significant investment. We assess backup strategies, cross-region replication, model versioning, and recovery procedures. For production AI systems, we evaluate failover capabilities: can inference continue if a region goes down? Is there a fallback model or graceful degradation path?

Audit Workflow

Discover

Inventory all cloud resources

Analyze

Profile utilization and costs

Benchmark

Compare against best practices

Optimize

Implement improvements

Discover

Inventory all cloud resources

Analyze

Profile utilization and costs

Benchmark

Compare against best practices

Optimize

Implement improvements

Cloud Infrastructure Assessment

AI-Specific Infrastructure Patterns

We evaluate your infrastructure against proven patterns for AI workloads. These include separated compute environments for training versus inference, object storage (S3, GCS, Azure Blob) configured for high-throughput data loading, container orchestration (EKS, GKE, AKS) with GPU-aware scheduling, model serving infrastructure (SageMaker, Vertex AI, Azure ML, or self-hosted options like vLLM and TGI), and observability stacks configured for AI-specific metrics.

For organizations using managed AI services (Azure AI, AWS Bedrock, Google Vertex AI), we assess provisioned throughput configuration, regional deployment strategy, quota management, and cost tracking. Managed services simplify operations but require careful configuration to avoid throttling and cost surprises.

Infrastructure decisions compound. Choosing the right GPU instance type, storage tier, and networking configuration early prevents expensive migrations later. Our audit helps you make these decisions with data rather than guesswork.

Multi-Cloud Considerations

Some organizations run AI workloads across multiple cloud providers to access specific services or avoid vendor lock-in. We assess cross-cloud data transfer costs, API compatibility layers, identity federation, and the operational overhead of multi-cloud management. In many cases, consolidating AI workloads on a single provider reduces both cost and complexity while improving performance.

Who This Is For

Cloud infrastructure audits are valuable for organizations planning to deploy AI workloads at scale, teams experiencing unexpected cloud costs from AI experiments, platform engineering teams building shared AI infrastructure, and CTOs evaluating cloud strategy for AI initiatives. The audit is cloud-agnostic and covers AWS, Azure, and GCP environments.

Cloud Infrastructure Audit