We run the infrastructure your AI runs on.
AI workloads are not like conventional cloud workloads. GPU instance costs escalate quickly. Model serving requires autoscaling that behaves differently from API compute. Vector databases introduce distinct sizing and retrieval-performance tradeoffs. MLOps pipelines fail in ways that are hard to diagnose without purpose-built instrumentation.
CirOps provisions, engineers, and operates the cloud infrastructure layer that AI workloads run on - GPU compute, model serving, MLOps pipeline infrastructure, vector databases, LLM API gateways - with the same operational discipline and cost governance we apply to every infrastructure domain we manage.
Within AI infrastructure and AI operations, this service owns the compute, storage, networking, and serving platform. MLOps and GenAI application integration are separately scoped capabilities.
AWS Advanced Tier Partner · AWS AI/ML Platform Expertise
SageMaker · Bedrock · GPU
Platforms managed
AI-Augmented
Operations standard
End-to-End
Managed delivery
The Challenge
AI workloads are expensive and complex to run reliably
The infrastructure challenge of AI is not provisioning a GPU instance. It is running GPU compute reliably and cost-efficiently over time, at the utilization levels that make it economically viable, with the monitoring that surfaces problems before they cascade.
Cost and reliability issues emerge when AI resources lack ownership and guardrails: a training job left running after its work is complete, an inference endpoint with the wrong minimum capacity, a vector database still sized for development traffic, or an LLM API gateway without rate limits, cost allocation, and usage visibility.
AI adds significant compute complexity to every cloud environment it enters. That complexity requires the same operational rigor that your core infrastructure does - and then some.
The cost is not just a large bill. It is serving degradation during traffic spikes, training jobs that stall and stay billed, and a complete absence of visibility into what is consuming compute and why.
Capabilities
What we provision and operate
GPU and Compute Provisioning on AWS
We architect and provision GPU compute for training and inference workloads: EC2 GPU instances (G and P families), right-sized for the actual model and workload type. Spot Instance strategies for training jobs where interruption is tolerable. Auto Scaling groups for inference endpoints. EKS-based GPU clusters for teams running ML workloads in Kubernetes.
Model Serving Infrastructure
We build and operate the infrastructure layer for serving models at production scale - Amazon SageMaker inference endpoints, self-managed inference servers on EC2 or EKS, autoscaling policies calibrated to latency and throughput requirements, and health monitoring for serving endpoints. Model performance in production is an infrastructure responsibility as much as a model responsibility.
MLOps Pipeline Infrastructure
The compute, orchestration, and storage infrastructure that training and evaluation pipelines run on: SageMaker Pipelines, Step Functions for ML workflows, S3-based feature stores and model registries, experiment tracking infrastructure. We provision and operate the platform; your ML team runs the experiments.
Vector Database Setup and Operation
We provision and operate vector databases at the scale your retrieval workloads require: pgvector on RDS or Aurora PostgreSQL, OpenSearch with k-NN vector search enabled, or Pinecone where managed vector database is the right fit. Index configuration, hardware sizing, backup, and monitoring - all operated as managed infrastructure.
LLM API Gateway and Rate Limiting
For teams calling foundation model APIs (Amazon Bedrock, OpenAI, Anthropic) at scale: we build and operate an API gateway layer with request routing, rate limiting by team or feature, cost allocation by consumer, and observability into token usage and latency. Cost control at the API call level is where LLM spend governance lives.
Cost Optimization for AI Workloads
GPU and ML compute costs are significant and require dedicated governance. We apply FinOps discipline specifically to AI infrastructure: right-sizing, Spot strategy for training, anomaly detection tuned for ML spend patterns, and cost allocation by model, pipeline, team, and environment. AI infrastructure cost management is a real discipline - we deliver it as standard practice.
Scope Boundary
What we do - and what we don't
We engineer and operate the infrastructure layer. Compute, networking, storage, serving, databases, API gateways, cost governance - everything the AI workloads run on.
We do not work on the models themselves. We do not train, fine-tune, evaluate, or modify models. That work belongs to your ML team or a specialist partner. Our scope is the infrastructure platform they operate on.
This boundary is deliberate. Infrastructure expertise and ML research expertise are different disciplines. We are expert cloud infrastructure engineers who understand AI workload requirements deeply - not ML researchers who provision cloud as a side function. The infrastructure we build gives your ML team a stable, cost-efficient, production-ready platform to do their work on.
Who This Is For
Who this service is for
SaaS companies building AI features into their product
LLM-based features, AI search, recommendation engines, classification at scale. You need the infrastructure to run those features in production reliably and cost-efficiently.
Data science and ML teams at growth-stage companies
Your team is running training and evaluation workloads on AWS but not operating the compute infrastructure with the rigor it requires. You need a managed infrastructure platform so your ML engineers focus on the models.
Companies deploying LLM-based products
Model serving, RAG pipelines, API gateways with rate limiting and cost controls. You are building a product that runs on foundation models and need the infrastructure layer that makes it production-grade.
Process
From audit to ongoing operations - four steps.
Infrastructure Audit
We assess your current AI/ML compute setup: what is running, on what, at what cost, with what monitoring. We establish the baseline and identify the most significant cost, reliability, and scaling gaps.
Architecture Design
We design the target infrastructure architecture: compute selection, autoscaling policy, serving configuration, vector database sizing, API gateway design, and cost governance framework - specific to your workload patterns and team structure.
Provision and Deploy
We build and deploy the infrastructure. GPU instances configured. Serving endpoints deployed. Vector databases provisioned and indexed. API gateways live with rate limiting and cost allocation. MLOps pipeline infrastructure operational.
Operate and Optimize
Ongoing operations: monitoring for serving endpoint performance and GPU utilization, cost anomaly detection tuned for AI workloads, Spot interruption handling for training infrastructure, monthly cost reviews with optimization actions. AI infrastructure does not operate itself.
AI-Augmented Operations
We use AI to manage AI infrastructure costs
CirOps uses AI-assisted tooling across every infrastructure domain we manage. On AI infrastructure, that creates a specific and high-value application: using AI to manage the cost and performance of AI workloads.
AI-assisted cost anomaly detection tuned for GPU and ML compute - idle instances, underutilized GPU clusters, training jobs that have stalled and are still billed. Endpoint monitoring tracks infrastructure signals such as latency, throughput, errors, and utilization. It does not assess prediction quality. AI-assisted pattern detection supports engineer-reviewed investigation of degradation and configurations associated with GPU waste.
Managing AI infrastructure costs is a discipline in its own right. We apply AI-augmented operational tooling to it as standard practice.
How we use AI in our engineering work →Why CirOps
What we bring to AI infrastructure
AWS Advanced Tier Partner
Our current AWS delivery stack includes SageMaker, Bedrock, EC2 GPU compute, EKS for ML workloads, and OpenSearch vector search.
FinOps for AI
Cost allocation, rightsizing, utilization review, anomaly detection, and commitment planning adapted to GPU and ML compute.
Dedicated AI Cost Governance
Right-sizing, Spot strategies, anomaly detection, and cost allocation by model, pipeline, team, and environment - built into every Managed AI Infrastructure engagement as standard practice.
Frequently asked questions
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Start with a conversation about your AI infrastructure
Request a no-cost Cloud Architecture Review. We assess your current AI/ML compute setup, identify the highest-impact cost and reliability gaps, and give you a clear picture of what Managed AI Infrastructure delivery would look like for your environment - before you commit to anything.