Scale • AI Infrastructure & GPU Optimization
AI Infrastructure
& GPU Optimization
Optimize AI compute, reduce GPU costs and scale AI workloads efficiently in production.
25+
Engineers
40+
Clients
58
Case Studies
~70%
GPU Cost Savings Demonstrated
GPU Clusters
200+ Jobs
Model Serving
Auto-Scale
Monitoring
Real-Time
Cost Savings
~70%
AI Infrastructure Services
From GPU cost optimization and model serving to pipeline orchestration and AI FinOps - we engineer production-grade AI infrastructure.
Dynamic GPU Provisioning & Auto-Scaling
Scale GPU resources up and down based on actual demand - eliminating always-on waste
Model Serving Infrastructure
Production model serving with low-latency inference, load balancing and failover
AI Pipeline Orchestration
End-to-end ML pipeline management from data processing to model deployment
Multi-Model Infrastructure
Infrastructure supporting multiple models, providers and routing strategies
GPU Cluster Design & Optimization
Kubernetes GPU scheduling, multi-instance GPU sharing and custom resource allocation controllers
AI Monitoring & Cost Management
GPU utilization tracking, inference latency, cost attribution, budget alerts and spend optimization
Inference Optimization
Reduce inference latency through batching, caching, model quantization and hardware selection
Engineering Capabilities
Deep expertise across GPU infrastructure, auto-scaling, model serving, pipeline orchestration and cost management.
GPU Infrastructure Design
- Instance type selection (A10, A100, H100)
- Spot vs on-demand strategy
- Multi-GPU and distributed training
- GPU sharing and time-slicing
- Cluster management
Auto-Scaling & Provisioning
- Dynamic GPU provisioning
- Scale-to-zero for bursty workloads
- Queue-based scaling triggers
- Predictive scaling policies
- Cost-aware scaling decisions
Model Serving
- Low-latency inference APIs
- Model versioning and A/B testing
- Batch vs real-time inference
- Response caching strategies
- Multi-model routing
Pipeline & Orchestration
- Training pipeline automation
- Data preprocessing at scale
- Model evaluation and promotion
- Experiment tracking
- CI/CD for ML models
Cost Management
- GPU utilization monitoring
- Spot instance management
- Reserved capacity planning
- Cost allocation and chargeback
- Budget alerts and governance
AI Infrastructure Approach
A structured approach to AI infrastructure - from assessment through architecture, optimization, serving, automation and continuous improvement.
01
Infrastructure Assessment
Audit current AI compute usage, costs, utilization patterns and scaling requirements.
02
Architecture Design
Design optimal GPU infrastructure - instance types, scaling policies and serving architecture.
03
Cost Optimization
Implement right-sizing, spot instances, auto-scaling and workload scheduling.
04
Serving Infrastructure
Build production model serving with load balancing, caching and failover.
05
Pipeline Automation
Automate training, evaluation and deployment pipelines.
06
Monitoring Setup
Implement GPU utilization, inference latency, cost tracking and quality monitoring.
07
Continuous Optimization
Ongoing cost optimization, capacity planning and infrastructure evolution.
Technology Stack
The exact stack is selected based on workload requirements, cost targets and scaling needs - not a fixed technology mandate.
GPU Compute
☁️AWS EC2 P4/P5
🧠AWS SageMaker
⚡AWS Inferentia
🟢NVIDIA A10 / A100 / H100
Orchestration
🐳Kubernetes
☁️AWS ECS
🔷Ray
🔄Custom Job Schedulers
Model Serving
⚡vLLM
🤖TGI
🔺Triton
🛠️Custom Inference Servers
ML Ops
📊MLflow
📈Weights & Biases
🧠SageMaker
🔧Custom Pipelines
Monitoring
🐶Datadog
📉Prometheus / Grafana
📊Custom GPU Dashboards
☁️CloudWatch
Related Solutions
Explore related capabilities that complement AI infrastructure engineering.
AI Product Engineering
Building a new AI product? Our team handles the full lifecycle from architecture to production.
ExploreAI Modernization
Add AI capabilities to existing products - LLM integration, RAG and intelligent automation.
ExploreBackend & Distributed Systems
Scale backend systems alongside AI infrastructure - event-driven architecture and microservices.
ExploreAI Infrastructure Use Cases
From GPU cost reduction and bursty workload scaling to multi-model routing and AI platform infrastructure.
GPU Cost Reduction
Optimize GPU spend for production AI workloads
Bursty Workload Scaling
Handle peak-to-trough demand ratios efficiently
Model Serving at Scale
Serve multiple models with low latency and high throughput
Training Infrastructure
GPU clusters for model fine-tuning and training
Multi-Model Routing
Route requests to optimal models based on cost and quality
Inference Caching
Cache frequent predictions to reduce GPU compute
Batch Processing
Efficient batch inference for large-scale data processing
Self-Hosted Models
Run open-source models on your own GPU infrastructure
AI Platform Infrastructure
Infrastructure for multi-tenant AI platforms
Cost Governance
FinOps practices for AI compute budgets
Optimized for Production
We engineer AI infrastructure for production - optimized for cost efficiency, low latency, reliability and observability at scale.
Cost Efficiency
Dynamic provisioning, spot instances, auto-scaling and GPU right-sizing
Low Latency
Inference optimization, caching, batching and hardware-aware model selection
Reliability
Multi-AZ deployment, failover, health checks and graceful degradation
Scalability
Horizontal scaling, queue-based processing and predictive auto-scaling
Observability
GPU utilization, inference metrics, cost attribution and quality tracking
Security
Model access controls, data encryption, network isolation and audit logging
Production Reference: AI Video Infrastructure
Designed dynamic GPU provisioning for an AI video platform handling 200+ peak concurrent jobs with a 50:1 peak-to-trough ratio - delivering approximately 70% cost savings versus always-on infrastructure.
Engineering Team Model
The team is structured around your infrastructure requirements, not a fixed package. Team composition adapts based on workload and scaling needs.
2-Person Squad
Focused infrastructure initiative. GPU optimization, cost reduction or specific infrastructure module.
3-Person Squad
Substantial infrastructure build. Model serving, auto-scaling or pipeline orchestration.
5-Person Squad
Full infrastructure engineering with Technical Lead, Infrastructure Engineers, ML Engineer and DevOps.
Custom Team
Team composition adapts to infrastructure requirements. Can include AI, data, security or cost optimization specialists.
How We Work Together
A flexible engagement model that grows with your AI infrastructure - from initial assessment to long-term engineering partnership.
1
Infrastructure Assessment
Audit AI compute usage, costs, utilization and scaling requirements
2
Architecture & POC
Design GPU infrastructure, validate scaling and cost optimization approach
3
Infrastructure Build
Implement provisioning, serving, pipelines and monitoring
4
Infrastructure Team
Ongoing team managing GPU infrastructure, optimization and evolution
5
Long-Term Partnership
Strategic technology partner for AI infrastructure scaling and cost management
Fixed-scope engagements are available when requirements are sufficiently defined. The right investment depends on infrastructure scope, technical complexity, team composition and roadmap.
Relevant Case Studies
Vector Databases
Milvus Autoscaling on Kubernetes with EC2 and S3-Backed Persistent Storage
An AI platform with rapidly growing vector data (embeddings for search, recommendations, and RAG) needed their Milvus vector database to scale automatically based on query load and data volume — with durable, cost-effective storage that wouldn't be lost if pods restarted or nodes were replaced.
MilvusAmazon EKSKubernetes HPA+8
Read Case Study
GPU Infrastructure
On-Off Scaling Pattern for AI & Video Processing Workloads
An AI-powered video processing platform needed to handle highly variable workloads — from zero jobs during off-hours to hundreds of concurrent video processing and AI inference tasks during peak times — without paying for idle GPU and compute resources.
Node.jsMongoDBRunPod API+7
Read Case Study
GPU Infrastructure
Leveraging RunPod for Scalable, Cost-Effective AI Inference
An AI-powered video analytics platform needed high-performance GPU compute for real-time object detection and inference across multiple concurrent video streams — without the prohibitive cost of dedicated GPU servers running 24/7.
RunPodDockerFastAPI+6
Read Case Study
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