FlexAI
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About
FlexAI is an AI infrastructure platform designed to abstract the complexity of GPU management, allowing teams to focus on model development rather than hardware provisioning. It operates as a Workload as a Service (WaaS) layer that sits between AI models and various compute resources, including public clouds like AWS and Azure, as well as specialized neoclouds. The platform's primary mission is to eliminate the waste common in infrastructure spending by automating the orchestration of inference, fine-tuning, and training workflows across heterogeneous environments. By providing a unified interface, it helps organizations scale their AI capabilities without the typical overhead of managing fragmented hardware clusters. The system works by providing a vertical, intent-driven control plane that maximizes GPU utilization, often reaching above 90% compared to the industry average of 30%. Key technical components include intelligent caching to eliminate data egress fees, multi-tenancy for better resource packing, and a self-healing infrastructure that uses managed checkpoints. Users can deploy workloads via a WebUI, CLI, or APIs, using Blueprints to simplify initial setups. Because it is hardware-agnostic, the platform can route jobs to the most efficient chip for a specific task, whether that is an NVIDIA H100, an AMD MI300, or a specialized TPU. FlexAI is specifically tailored for AI-native startups and scaleups that need to accelerate their time-to-market without maintaining a massive internal DevOps team. It is also suitable for neocloud providers looking to deliver managed services on their own AI factories and enterprises scaling private clouds. By providing a unified interface across different architectures, it caters to developers who want to avoid vendor lock-in and require the flexibility to switch clouds or hardware configurations based on availability and cost. The platform supports a wide array of setups, from on-demand dedicated endpoints to bring-your-own-cloud (BYOC) models.
Pros & Cons
Supports a wide range of hardware including NVIDIA, AMD, and Intel architectures.
Achieves up to 90% GPU utilization through intelligent packing and multi-tenancy.
Rapid deployment capabilities with jobs launching in under one minute.
Eliminates data egress fees through an intelligent caching system.
Offers $100 in free credits for new startups signing up with a work email.
Support for certain alternative multi-architectures is still listed as available soon for some services.
Full monitoring history is restricted to only one month on the Essential plan tier.
Advanced enterprise features like self-hosting and audit logs require the Custom pricing tier.
Service availability and region selection are partially dependent on third-party cloud partners.
Use Cases
AI Startups can use the platform to deploy production models or YC demos in under 24 hours without a dedicated DevOps team.
Infrastructure Engineers can manage workloads across multiple providers like AWS and Azure from a single control plane to avoid vendor lock-in.
Machine Learning Researchers can leverage the Workload Co-Pilot to automatically select the most cost-effective hardware for training tasks.
Enterprise IT Admins can scale private cloud resources while maintaining strict security compliance like HIPAA and DORA.
Platform
Task
Features
• real-time grafana dashboards
• multi-tenancy autoscaling
• self-healing with managed checkpoints
• workload co-pilot
• intelligent caching for zero data movement
• sub-60-second job launching
• heterogeneous hardware support
• multi-cloud orchestration
FAQs
What is the FlexAI Workload Co-Pilot?
It is an intelligent selection tool that helps users choose the optimal compute architecture for their needs. It automatically aligns workloads with the best available hardware across different cloud providers to optimize cost and performance.
How does FlexAI reduce infrastructure costs?
The platform increases GPU utilization to over 90% through intelligent packing and multi-tenancy. Additionally, its intelligent caching system eliminates data egress fees, leading to an average reported saving of $87,000 per year for teams.
Can I use my existing cloud accounts with FlexAI?
Yes, FlexAI supports a Bring Your Own Infrastructure model. You can deploy compute next to your data on hyperscalers like AWS, Azure, and GCP, or specialized providers like Coreweave and Nebius while using the FlexAI control plane.
How fast can jobs be launched on the platform?
FlexAI is optimized for speed, with jobs typically launching in under 60 seconds. This avoids the long provisioning delays often associated with manual cluster configuration and traditional cloud hardware setups.
What security and compliance standards are supported?
All plans include standard GDPR compliance. The Essential and Custom tiers offer advanced support for regulated industries, including HIPAA and DORA compliance, along with enterprise-grade audit logs and IT admin policies.
Pricing Plans
Starter
Unknown Price• $100 Credits for startups
• 2 workspace seats
• On-demand dedicated endpoints
• 99% availability SLA
• Smart Sizing Calculator
• Grafana Monitoring dashboard
• Standard security (GDPR)
• Email and Slack support
Essential
Unknown Price• 8 workplace seats
• Concurrency support
• Multi-fractional support
• Smart Co-pilot with multi-architecture
• 99.5% availability SLA
• HIPAA and DORA support
• 1 Month monitoring history
• Premium private Slack support
Custom
Unknown Price• Unlimited seats
• Self-hosting add-on
• 99.9% availability SLA
• Geo redundancy
• IT admin policies and audit logs
• Self-healing managed checkpoints
• Dedicated customer success team
• Personalized integration
Job Opportunities
IT Manager
Optimize AI infrastructure costs and performance across any cloud or hardware with automated GPU orchestration, sub-60-second job launches, and 90% utilization.
Experience Requirements:
6+ years of experience in IT administration or IT operations
Prior experience as an IT Manager or Senior IT Administrator
Experience supporting 50–300 employee environments
Other Requirements:
Google Workspace
SaaS administration and access management
Endpoint management / MDM tools
Office networking and on-site support
Responsibilities:
Own day-to-day IT operations for the Bangalore office
Handle employee onboarding and offboarding
Administer internal SaaS tools
Own identity and access management
Act as the primary on-site IT support and escalation point
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Senior DevOps Engineer/SRE
Optimize AI infrastructure costs and performance across any cloud or hardware with automated GPU orchestration, sub-60-second job launches, and 90% utilization.
Education Requirements:
Bachelor's or higher degree in Computer Science, Software Engineering, or a related field
Experience Requirements:
Proven experience as a DevOps or SRE Engineer
Strong proficiency in scripting languages (e.g. Python, Bash)
Experience with cloud platforms (AWS, Azure, GCP)
Hands-on experience with infrastructure as code (IaC) tools like Terraform
Other Requirements:
Familiarity with cloud-native technologies (Docker, Kubernetes)
Experience managing multi-architecture deployments
Entrepreneurial & start-up mindset
Responsibilities:
Design, implement, and maintain CI/CD pipelines
Develop and manage infrastructure as code (IaC) using Terraform
Implement and manage containerization and orchestration tools
Monitor and optimize system performance
Collaborate with security teams to ensure infrastructure meets security best practices
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Staff AI Runtime Engineer
Optimize AI infrastructure costs and performance across any cloud or hardware with automated GPU orchestration, sub-60-second job launches, and 90% utilization.
Benefits:
A competitive salary and benefits package
Opportunity to collaborate with leading experts in AI
Environment that values innovation and collaboration
Support for personal and professional development
Pivotal role in the AI revolution
Experience Requirements:
8+ years of experience in systems/software engineering
Experience in delivering PaaS services
Proven experience optimizing and scaling deep learning runtimes
Strong programming skills in Python and C++
Start up previous experience
Other Requirements:
Familiarity with distributed training frameworks
Experience working with multi-GPU, multi-node, or cloud-native AI workloads
Solid understanding of containerized workloads
Responsibilities:
Own the core runtime architecture supporting AI training and inference at scale
Design resilient and elastic runtime features within a custom PyTorch stack
Profile and enhance low-level system performance
Design and maintain libraries and services that support model lifecycle
Guide technical discussions and mentor junior engineers
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