01
Your AI POC worked. Now it can’t scale.
The model runs on a developer’s laptop or a single notebook instance. Moving it to production requires infrastructure your team hasn’t built — GPU compute, ML pipelines, monitoring, and security.
AI Infrastructure
AI infrastructure.
The cloud setup your AI needs to actually run.Your AI model works in a notebook. But production requires GPU compute, ML pipelines, vector databases, and security controls that standard cloud setups don’t include. We build the infrastructure so your AI runs reliably and affordably.
NMSDC MBE Certified
U.S.-Based
GPU & ML Pipelines
AWS · Azure · GCP
01 · Why this is hard
AI needs different infrastructure than standard cloud.
02
GPU costs are unpredictable and climbing.
GPU instances run 24/7 whether models are training or not. Spot instances aren’t configured. Auto-scaling doesn’t exist. You’re paying production prices for idle compute.
03
Security and compliance haven’t been addressed.
AI workloads access sensitive data but lack IAM policies, network isolation, encryption at rest, and audit logging. Compliance teams have questions you can’t answer.
04
There’s no pipeline — just manual steps.
Training, evaluation, and deployment happen through manual scripts and ad-hoc processes. There’s no reproducibility, no versioning, and no way to roll back.
02 · What we build
Cloud infrastructure purpose-built for AI.
GPU compute, ML pipelines, vector databases, model serving, security controls, and cost optimization \u2014 all production-grade.
GPU compute provisioning
Configure and manage GPU instances for model training, fine-tuning, and inference — with auto-scaling, spot instances, and cost controls built in.
Discuss this →ML pipeline infrastructure
Build the cloud infrastructure that runs your training, evaluation, and deployment pipelines — reproducible, versioned, and automated.
Discuss this →Vector database hosting
Deploy and manage vector databases (Pinecone, Weaviate, pgvector, Qdrant) for RAG, semantic search, and AI-powered retrieval at scale.
Discuss this →AI workload security
IAM policies, network isolation, encryption, logging, and compliance controls designed specifically for AI workloads and sensitive data.
Discuss this →Model serving and inference
Production-grade model serving infrastructure with load balancing, auto-scaling, A/B testing, and latency monitoring.
Discuss this →Cost optimization
Reserved instances, spot scheduling, right-sizing, and auto-shutdown policies that keep GPU costs under control without sacrificing performance.
Discuss this →Your AI model is ready. Your infrastructure isn’t. Let’s fix that.
Start an infrastructure review03 · Problems we solve
Real problems teams bring to us.
01
Challenge
Our AI model works in notebooks but can’t run in production.
How we solve it
We build the production infrastructure — GPU compute, serving endpoints, monitoring, and auto-scaling — so your model runs reliably at scale.
02
Challenge
GPU costs are out of control.
How we solve it
We configure spot instances, auto-scaling, scheduling, and right-sizing. Most companies see 40–60% GPU cost reduction with proper infrastructure.
03
Challenge
We don’t know which cloud to use for AI.
How we solve it
We assess your workload requirements, data location, compliance needs, and team skills — then recommend AWS, Azure, or GCP with a clear rationale.
04 · How we work
From assessment to production AI infra.
01
Assessment
We review your AI workloads, model requirements, compute needs, data volumes, and cloud environment.
→ Scope defined
02
Architecture
We design the infrastructure with cost optimization, performance, security, and scalability built in.
→ Architecture designed
03
Build
We provision, configure, test, and document the AI infrastructure. Everything is IaC (Terraform/Pulumi).
→ Infrastructure live
04
Optimize
Ongoing cost optimization, scaling policies, performance tuning, and security hardening.
→ AI infra optimized
06 · Common questions
Frequently asked questions.
Which cloud providers do you support for AI?
AWS, Azure, and Google Cloud. We recommend based on your model requirements, GPU availability, data location, and compliance needs — not vendor preferences.
Can you manage GPU costs?
Yes. We configure auto-scaling, spot/preemptible instances, scheduling, and right-sizing. Most companies overspend 40–60% on GPU compute because they lack these controls.
Do you support on-prem AI infrastructure?
Our focus is cloud-based AI infrastructure. For on-prem needs, we can assess and recommend hybrid approaches that keep sensitive workloads local while scaling compute in the cloud.
What ML pipeline tools do you work with?
We work with AWS SageMaker, Azure ML, Vertex AI, MLflow, Kubeflow, and custom pipeline orchestration depending on your stack and team preferences.
How long does it take to build AI infrastructure?
A focused AI infrastructure setup typically takes 4–8 weeks. More complex multi-model deployments with custom pipelines take 8–12 weeks. We scope clearly before any work begins.
Can you help us move an AI POC to production?
Yes. That’s one of our most common engagements. We take what your data science team built and give it the infrastructure it needs to run reliably, securely, and cost-effectively at scale.
Get Started
Build AI infrastructure that scales.
Start with an infrastructure review.
Tell us what AI workloads you need to run and we’ll design and build the cloud infrastructure \u2014 with a clear architecture, cost model, and timeline.
Contact info@evolveblue.com · +1 215-882-3133