Fordel StudiosCloud Infrastructure & DevOps
Cloud infrastructure built for inference costs and scale from day one.
What this means
in practice
AI workloads have a different infrastructure profile than traditional web applications — GPU availability is constrained, inference costs can dominate the monthly bill, and multi-cloud isn't optional when you're hedging against single-provider GPU shortages. We've designed and operated these systems, so we know where the cost surprises hide and what prevents them. Our scope covers everything from initial cloud architecture through CI/CD pipelines, observability stacks, and ongoing cost rightsizing.
On the inference layer, we work with vLLM and Text Generation Inference for self-hosted model serving, configure KV cache settings and tensor parallelism, and implement semantic similarity caches at the application layer to cut repeated inference costs. For Kubernetes, we handle GPU node pools, CUDA-aware scheduling, and spot instance interruption handling for batch embedding jobs. Everything — every resource, every policy, every pipeline — is defined in Terraform or Pulumi, not clicked together in a cloud console.
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
- 01
Cloud architecture design for AI workloads (AWS, GCP, Azure)
- 02
GPU node pool configuration and scheduling for inference workloads
- 03
Kubernetes cluster setup with AI serving stack (vLLM, TGI)
- 04
CI/CD pipeline design with staging environments and deployment automation
- 05
Infrastructure as Code with Terraform or Pulumi
- 06
Inference cost optimization: spot instances, caching, model routing
- 07
Monitoring and alerting: Prometheus, Grafana, PagerDuty
- 08
Security hardening: IAM policy design, network segmentation, secrets management
Our process
- 01
Workload Analysis
We map each component to its compute profile: CPU-bound APIs, GPU-bound inference endpoints, memory-intensive vector operations, high-IOPS database workloads. This mapping drives instance type selection and prevents the expensive over-provisioning that happens when teams treat everything as a generic compute problem.
- 02
Architecture Design
We design the cloud topology — VPC structure, subnet layout, service mesh decision, multi-region requirements. The inference serving layer gets its own design pass separate from the application layer, because the two have different scaling triggers, different uptime requirements, and different failure modes.
- 03
IaC Implementation
Every resource is implemented as Terraform modules or Pulumi programs — reviewed, version-controlled, and reproducible. No manual console changes, no configuration drift, no tribal knowledge required to rebuild an environment from scratch.
- 04
CI/CD Pipeline
We build pipelines that run tests, build and push container images, deploy to staging, and promote to production on approval. GitHub Actions or CircleCI for orchestration; ArgoCD or Flux for Kubernetes deployments via GitOps, where every production change traces back to a reviewed git commit.
- 05
Observability Stack
We deploy Prometheus, Grafana, Loki, and Tempo (or Jaeger) for metrics, logs, and traces. Alerting is configured on metrics that predict user impact — inference latency p95, queue depth, error rates — not just CPU and memory.
- 06
Cost Optimization
We review utilization against provisioned capacity, rightsize instance types, convert consistent workloads to reserved instances, and move batch jobs to spot with checkpointing. Inference caching for repeated queries is evaluated early — it's frequently the highest-leverage cost reduction available.
Tools and infrastructure we use for this capability.
Why work
with us
- 01
We've Operated GPU Infrastructure
Provisioning CUDA-aware Kubernetes node pools, tuning vLLM tensor parallelism and KV cache settings, and handling spot instance interruptions for batch embedding jobs are specific skills — not general DevOps skills. We've built and run these systems in production, not just read the documentation.
- 02
Cost Attribution From Day One
Inference costs scale faster than any other cloud line item if you're not watching them. We instrument cost attribution at the start, identify the patterns that drive the bill — unnecessary model calls, missed cache hits, oversized GPU instances — and fix them systematically, not in a one-time cost review.
- 03
IaC Is Non-Negotiable
We don't manually configure infrastructure. Terraform or Pulumi for everything means your infrastructure is auditable, reproducible, and recoverable after an incident. Onboarding a new engineer doesn't require reconstructing what someone clicked together in the AWS console.
- 04
Multi-Cloud Is How These Systems Are Actually Built
Your inference workloads might run on AWS, your managed vector database on GCP, and your edge functions on Cloudflare. We design for this from the start — the right provider per workload, with the cross-provider networking and IAM policies to make it operational.
Frequently
asked questions
How do you manage GPU costs for AI workloads?
Three levers: 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), spot instances for batch embedding and training jobs with checkpointing so interruptions don't lose progress, and inference caching at the application layer. A semantic similarity cache — checking if a close-enough question was recently answered before making a new model call — can cut inference volume by 30-50% for many applications.
When does Kubernetes make sense versus simpler alternatives?
Kubernetes is the right call when you have multiple services with different scaling profiles, need GPU scheduling and node affinity for inference workloads, or require rolling deployments with health checking across a fleet. For a handful of services with predictable load, managed container services like ECS, Cloud Run, or Fly.io carry less operational overhead. Starting on Kubernetes before the complexity justifies it is a common, expensive mistake.
What is GitOps and should we use it?
GitOps means git pull requests are the mechanism for all production changes — including Kubernetes deployments. ArgoCD and Flux watch your repos and apply changes when they merge, so your production state always traces back to a reviewed, auditable commit. For teams with solid CI/CD discipline already, GitOps is the natural next step; for teams building their deployment process from scratch, it's worth investing in at the start rather than retrofitting.
How do you handle secrets in a Kubernetes environment?
Kubernetes Secrets are base64, not encrypted at rest by default — not adequate for production. We use AWS Secrets Manager or GCP Secret Manager with the external-secrets-operator (secrets live in your cloud provider, injected into pods at runtime), or HashiCorp Vault for multi-cloud environments where you need fine-grained access control. The hard rule: secrets never live in git, even encrypted, without a proper secrets management solution behind them.
What is edge inference and when does it make sense?
Edge inference runs AI models at edge locations — Cloudflare Workers AI, Fastly, Vercel Edge — rather than centralized compute, cutting latency for geographically distributed users. It makes sense when the round-trip to a central region adds 100-200ms that users will notice, and when 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 evaluating whether edge inference is worth the operational complexity for your use case.
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