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Strategic ML infrastructure planning for enterprise ROI

We provide expert TCO analysis and optimization services for organizations investing in ML systems.

From CapEx vs OpEx decisions to cloud resource allocation, our experienced team helps you uncover and optimize the complete financial picture of your ML infrastructure. We provide detailed TCO assessments that account for training, deployment, maintenance, and operational costs.

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ML infrastructure cost optimization

Infrastructure costs

High-performance ML systems generate costs across multiple dimensions. Compute costs vary through the ML lifecycle – from model building and experimentation, through intensive training phases, to deployment and inference. Storage costs accumulate from dataset management, model artifacts, and versioning. Network costs stem from data transfer, model synchronization, and serving predictions. We optimize each component while maintaining performance

Operational costs

ML operations require skilled engineering teams for ongoing maintenance and optimization. We help you assess and optimize your operational expenses based on team size requirements, engineer salaries, and support needs over multi-year periods. Through workflow automation, efficient monitoring systems, and streamlined maintenance procedures, we reduce the human-hours needed while maintaining operational excellence

Security & Compliance

ML workloads process large volumes of sensitive and proprietary data during model building and training. Security breaches and compliance issues can lead to substantial financial impact. Our expertise ensures your ML infrastructure meets security standards and compliance requirements while optimizing associated costs. We implement robust security measures that protect your valuable data assets without overprovisioning expensive security resources

ML infrastructure services

Data Center TCO Analysis

Data Center TCO analysis

  • Comprehensive assessment of on-premises ML infrastructure costs
  • Granular analysis of power consumption, cooling, maintenance, and operational expenses
  • Detailed reporting on hardware utilization and depreciation metrics
  • Strategic recommendations for cost optimization and resource allocation
Competitive pricing

Cost optimization strategy

  • Development of custom expense prioritization frameworks
  • CapEx vs OpEx analysis aligned with organizational objectives
  • Historical procurement analysis and future cost modeling
  • Implementation of AI-specific cost-tracking mechanisms (GPU usage, training cycles)
Predictive maintenance systems

Self-Managed ML infrastructure (EC2)

  • End-to-end setup and optimization of DIY ML environments
  • Configuration of DLAMI with ML frameworks and libraries
  • Implementation of automated scaling and failure recovery systems
  • Security and compliance framework implementation
Managed Kubernetes Deployment

Managed Kubernetes deployment

  • EKS cluster setup and performance optimization
  • Memory, compute, and network requirement analysis
  • Integration of ML-specific tools like Kubeflow
  • Infrastructure cost management and optimization
SageMaker Migration & Optimization

SageMaker migration & optimization

  • Migration planning and execution to fully managed ML services
  • Workload optimization for cost-efficient scaling
  • Security and compliance configuration
  • Integration with existing ML workflows
ML Performance Optimization

ML performance optimization

  • Model operator parallelization implementation
  • Custom model format optimization for specialized hardware
  • Batch processing configuration for GPU workloads
  • Integration with specific model servers (e.g., Triton)
Infrastructure Monitoring & Maintenance

Infrastructure monitoring & maintenance

  • Continuous performance monitoring and optimization
  • Regular security updates and patch management
  • Cost tracking and optimization recommendations
  • Resource utilization analysis and reporting
Recommendation engine development

Data quality & model management

  • Implementation of automated data validation pipelines
  • Regular model performance monitoring and updates
  • Drift detection and retraining schedule optimization
  • Quality assurance and validation processes
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Benefits of ML infrastructure optimization

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Cost efficiency & resource optimization

 

  • Reduce infrastructure costs by up to 40%
  • Optimize compute resources across build, train, and deploy phases
  • Eliminate unnecessary storage and networking expenses
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Enhanced developer productivity

  • Automate manual infrastructure setup and management
  • Streamline environment configuration and deployment
  • Reduce time spent on maintenance tasks
  • Allow teams to focus on core ML development
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Accelerated ML development

  • Speed up model experimentation and iteration cycles
  • Reduce time-to-production for ML initiatives
  • Enable faster model updates and improvements
  • Streamline deployment workflows
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Team efficiency

  • Maximize value from expensive data science talent
  • Reduce time spent on infrastructure management
  • Enable focus on high-impact ML tasks
  • Improve collaboration between teams
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Infrastructure scalability

  • Scale resources efficiently based on workload demands
  • Optimize costs during peak and off-peak periods
  • Enable seamless growth of ML operations
  • Maintain performance while controlling costs
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Better resource planning

  • Clear visibility into infrastructure costs
  • Predictable budgeting and resource allocation
  • Informed decision-making for ML investments
  • Long-term cost forecasting
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Operational excellence

  • Streamlined ML lifecycle management
  • Improved monitoring and maintenance
  • Reduced operational overhead
  • Enhanced system reliability
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Risk mitigation

  • Prevent unexpected cost overruns
  • Ensure compliance with budget constraints
  • Maintain performance standards
  • Reduce technical debt

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How to modernize legacy software without business risks and service disruption

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Why choose Xenoss for ML infrastructure TCO optimization

Multi-model TCO expertise

We specialize in both Cloud and On-Premises TCO optimization, helping enterprises evaluate and optimize infrastructure costs across different deployment models to find the most cost-effective solution

Infrastructure cost optimization

Process and analyze complex infrastructure expenses, from model serving and training costs to storage and network costs, delivering comprehensive cost reduction strategies while maintaining performance

Real-time cost monitoring

Track and optimize costs in real-time across your ML infrastructure, from training data storage to model serving expenses, ensuring efficient resource utilization and preventing cost overruns

Rapid TCO assessment

Launch faster with Xenoss pre-built assessment frameworks designed for enterprise ML infrastructure. Quickly identify cost optimization opportunities and implement solutions to reduce TCO

Tech stack agnostic

Select the tools and platforms that best align with your enterprise’s ML infrastructure. Our engineers bring deep expertise across diverse technologies, ensuring optimal cost-performance balance regardless of your stack

Proven cost reduction

Achieve up to 40% reduction in ML infrastructure costs through our optimization strategies, covering compute, storage, and operational expenses while maintaining model performance

Secure and compliant

Optimize costs while ensuring your ML infrastructure meets security and compliance requirements, implementing cost-effective security measures without compromising protection

Specialized ML expertise

Our engineers excel in ML infrastructure optimization, bringing experience from working with enterprises like Microsoft, Toshiba, and Activision Blizzard to deliver cost-efficient ML operations at scale

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Reduce your ML infrastructure costs by up to 40%

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Featured projects

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Alex Belyansky

Director of Engineering, INMAR

It was a great pleasure working with the Xenoss team. The project was complex and challenging - a rich media editor supporting animation, timeline editing, special effects, undo/redo functionality, and other unique features not commonly found. The project was time-boxed for 3 months. It was a ground-up development incorporating niche technologies that required extensive research and prototyping. Not only did the team deliver a fully working MVP on time, but they also exceeded the requirements in several key instances. The architecture was thoroughly designed, and the UX was executed according to the specifications. I'm very grateful for this experience and highly recommend Xenoss.

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Ben Dzamba

VP of Product, Powerlinks

Before turning to Xenoss, we had a demand-side platform that was costly and not scalable. Having access to a wealth of experience on the Xenoss team related to our domain of real-time bidding, we’ve cut costs and now have a much more efficient, reliable DSP for our customers. I’d gladly recommend Xenoss as a technology partner. I’ve found the team to be very professional and diligent, ensuring that our needs and expectations are met through every step of the development process.

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Brandon Keenan

CMO, ViVV LABS

We loaded a huge client into the ViVV Labs Platform today—with the incredible support of the Xenoss team. We’ve done this a number of times already, and it’s worked flawlessly every time, but this one was different. It was a key client for our business. If you want to experience what it’s like to pull your walled garden data effortlessly and apply data science to your spend to potentially save 30–40%, partner with Xenoss.

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David Philippson

CEO & Co-Founder, Dataseat

We were looking for an experienced vendor to develop a performance-based media buying solution from scratch. One of the main reasons why we chose Xenoss was their extensive domain knowledge. It allowed us to save time and effort at the initial stages and dive right in product development. The team’s been very professional and responsive to our needs and was able to deliver the MVP under just several months. Later on, they’ve transformed it into a fully featured platform for in-game advertising, which already proved highly scalable and able to manage high load. I’ve been truly happy with their work, high quality standards, and communication.

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Edward Lyon

Head of Product, Smartly

Our business has grown since we started working with Xenoss by an enormous amount and much of that has to do with the software that they’re developing. The most impressive aspect of our collaboration is that the Xenoss team keeps on solving challenges we put in front of them and these are challenges that anecdotally, other businesses have tried solving but are not successful.

Matt Cannon about Xenoss

Matt Cannon

COO, Venatus

We've been a client of Xenoss for a year now and find them an excellent technology partner. Highly skilled and knowledgeable with the ability to rapidly adapt to our needs. We intend to double the size of our current team with them in 2021.

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Oli Marlow Thomas

CEO & Founder, Smartly

At some point in our business journey, we had a frustrating experience with our product, from barely managing its instability to fixing errors on the fly. Xenoss team helped us build a well-balanced tech organization and deliver the MVP within a very short timeline. It let us timely onboard huge clients such as Adidas, Tesco, Uber, and keep up our growth pace. I’m glad we’ve been working with such a highly-productive team. I particularly appreciate their ability to hire extremely fast and to generate great product ideas and improvements.