Fordel StudiosCloud Infrastructure & DevOps
Infrastructure that scales with AI workloads
What this means
in practice
Cloud infrastructure for AI applications has a different cost and complexity profile than traditional application infrastructure. GPU availability is constrained. Inference costs can dominate the bill. Multi-cloud is no longer theoretical — it is how teams hedge against single-provider GPU availability and model provider outages. Getting this right requires infrastructure engineers who understand both the traditional patterns and the AI-specific constraints.
The Kubernetes + AI serving stack has consolidated around a set of patterns: GPU node pools for inference workloads, spot/preemptible instances with automatic fallback for batch embedding jobs, horizontal pod autoscaling based on queue depth for async AI processing, and edge deployment for latency-sensitive inference. We design for all of these.
Cloud Infrastructure in the GPU Age
The cloud infrastructure discipline has a new cost center that did not exist three years ago: GPU compute. Whether you are self-hosting inference servers (vLLM on your own Kubernetes cluster), running batch embedding jobs (millions of documents per week through an embedding model), or running fine-tuning experiments, GPU infrastructure is now part of the engineering equation for any serious AI application.
GPU availability on public clouds remains constrained relative to demand. Teams building AI infrastructure in 2026 design for multi-cloud from the start — not for theoretical resilience, but because the practical reality is that AWS might have better A10G availability this quarter and GCP next quarter. Locking into a single GPU provider is a capacity risk.
The Kubernetes + AI Serving Stack
The standard pattern for self-hosted inference in 2026: a Kubernetes cluster with dedicated GPU node pools, vLLM or TGI running as Deployments with pod anti-affinity to spread across GPU nodes, horizontal pod autoscaling based on GPU utilization or queue depth, and a load balancer in front routing to healthy pods. This stack handles most production inference workloads and is well-documented.
The parts that require judgment: GPU node pool sizing (too small means queuing, too large means idle GPU costs), KV cache configuration in vLLM (the right setting depends on your model and typical sequence lengths), and batch size tuning for embedding jobs (larger batches are more GPU-efficient but add latency). These are not parameters you find in the official documentation — they come from running the stack under real load.
Inference Cost Optimization Is an Engineering Discipline
The number one unexpected cost in AI applications is inference — more specifically, inference that could be avoided or made cheaper with better system design. Semantic caching (returning stored responses for semantically similar queries) is the highest-leverage optimization. Model routing (using a cheaper, smaller model for simple queries and a more capable model for complex ones) is the second. Getting these right requires instrumentation: you need to know the cost per feature, per user, and per query type before you can optimize.
Carbon-aware scheduling is an emerging practice for batch workloads. Embedding generation jobs and fine-tuning runs can typically tolerate an hour of schedule flexibility. Running them when the grid carbon intensity is low (night time in coal-heavy regions, when renewable output is high) costs nothing extra and reduces the environmental footprint of AI workloads. GCP and Azure both expose carbon intensity data via API.
- Semantic caching: store and reuse responses for similar queries — reduces volume by 20-50% for many use cases
- Model routing: use a small, fast model for simple queries; large model only for complex ones
- Spot instances for batch jobs: 60-90% cost reduction with proper checkpointing
- Prompt compression: reduce token count on long-context prompts without losing meaning
- KV cache optimization in vLLM: maximize cache hit rate with prefix-aware request routing
What is included
Our process
Workload Analysis
Map the infrastructure requirements of each component: CPU-bound APIs, GPU-bound inference, memory-intensive vector operations, high-IOPS database workloads. Each has different instance type requirements and scaling characteristics. Getting this mapping right prevents expensive over-provisioning and capacity surprises.
Architecture Design
Design the cloud topology: VPC structure, subnet layout, service mesh or not, multi-region requirements. For AI workloads, design the inference serving layer separately from the application layer — they have different scaling triggers and different uptime requirements.
IaC Implementation
Implement the infrastructure as Terraform modules or Pulumi programs. Every resource is code-defined, reviewed, and version-controlled. No manual console changes. This discipline makes environments reproducible and incidents recoverable.
CI/CD Pipeline
Build deployment pipelines that run tests, build container images, push to registry, and deploy to staging with promotion to production on approval. GitHub Actions or CircleCI for orchestration, ArgoCD or Flux for Kubernetes deployments via GitOps.
Observability Stack
Deploy Prometheus for metrics, Grafana for dashboards, Loki for log aggregation, and Tempo or Jaeger for traces. Configure alerting on the metrics that predict user impact — not just system health metrics.
Cost Optimization
Review resource utilization against provisioned capacity. Rightsize instance types. Convert consistent workloads to reserved instances. Move batch AI jobs to spot instances with proper checkpointing. Implement inference caching for repeated queries. Cost optimization is ongoing engineering, not a one-time activity.
Tools and infrastructure we use for this capability.
Why Fordel
We Understand GPU Infrastructure
Provisioning GPU nodes, configuring CUDA-aware Kubernetes scheduling, managing spot instance interruption handling for batch jobs, and optimizing inference server configuration (vLLM tensor parallelism, KV cache settings) — these are specific skills that general DevOps engineers typically do not have. We have built and operated these systems.
Cost Optimization Is Engineering, Not Guesswork
Inference costs can scale faster than any other cloud line item. We instrument cost attribution from day one, identify the high-cost patterns (unnecessary model calls, missed caching opportunities, oversized instances), and fix them systematically. Infrastructure cost is a product of engineering decisions, and it responds to engineering attention.
IaC Is Non-Negotiable
We do not manually configure infrastructure. Everything is Terraform or Pulumi. This means your infrastructure is auditable, reproducible, and recoverable. It also means onboarding a new team member does not require tribal knowledge about what got configured in the console six months ago.
Multi-Cloud Is Reality, Not a Buzzword
Your inference workloads might run on AWS. Your vector database might be managed on GCP. Your edge functions are on Cloudflare. Multi-cloud is how modern AI systems are actually built, and designing for it from the start prevents the lock-in pain that comes from over-committing to one provider.
Frequently asked
questions
How do you manage GPU costs for AI workloads?
GPU cost management has three levers. First, right-sizing: most inference workloads run fine on A10G or L4 instances rather than A100s — significant cost difference, small latency difference for typical batch sizes. Second, spot instances for batch embedding and training jobs with proper checkpointing so interruptions do not lose progress. Third, inference caching at the application layer — for applications where many users ask similar questions, a semantic similarity cache (check if a similar question was recently answered before making a new model call) can reduce inference volume by 30-50%.
When should we use Kubernetes versus simpler alternatives?
Kubernetes makes sense when you have multiple services with different scaling requirements, need GPU scheduling and node affinity for inference workloads, or require rolling deployments with health checking across a fleet. For simpler applications — a handful of services with predictable load — managed container services (ECS, Cloud Run, Fly.io) are less operational overhead. The mistake is starting on Kubernetes before the complexity justifies it.
What is GitOps and should we use it?
GitOps is the practice of using git pull requests as the mechanism for all production changes — including infrastructure and Kubernetes deployments. ArgoCD and Flux watch git repositories and automatically apply changes when they merge. The benefit is that your production state is always the result of reviewed, auditable git commits. For teams with established CI/CD discipline, GitOps is the natural next step. For teams still building their deployment process, it is worth investing in from the start.
How do you handle secrets in a Kubernetes environment?
Kubernetes Secrets provide basic secret injection but store secrets as base64 — not encrypted at rest by default. For production, we use either AWS Secrets Manager / GCP Secret Manager with the external-secrets-operator (secrets stored in your cloud provider, injected into pods at runtime) or HashiCorp Vault for multi-cloud secrets management with fine-grained access control. The key requirement: secrets are never in git, even as encrypted values, without a proper secrets management solution.
What is edge inference and when does it make sense?
Edge inference runs AI models at edge locations (Cloudflare Workers, Fastly, Vercel Edge) rather than centralized compute, reducing latency for geographically distributed users. It makes sense for latency-sensitive features where adding 100-200ms round-trip to a central region matters to users, and where the model fits within edge runtime constraints (typically small distilled models, not GPT-4o scale). Cloudflare Workers AI is the most accessible entry point for edge inference experiments.
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