FlexAI vs. Run:AI, Anyscale, Modal.com, and Rafay — The Unified AI Infrastructure Platform

Why FlexAI

FlexAI unifies workload orchestration, policy governance, and multi-cloud automation into a single platform.

This means that dev teams can train, fine-tune, and serve models with one click across any cloud and any compute.

The Unified AI Infrastructure Platform

While point solutions specialize in a slice of the stack, such as GPU scheduling, Ray-based scaling, serverless inference, or Kubernetes ops, FlexAI delivers a full, vertically-integrated "AI factory" with end-user consoles, admin controls, and productized SKUs.

Competitive Comparison

Full support
Full Support
Partial support
Partial Support
No support
No Support
Capability FlexAI Run:AI Anyscale Modal.com Rafay
One-click workload orchestration (train, fine-tune, serve) Full support Partial support Partial support Partial support Partial support
Intelligent workload co-pilot (right-sizing, budgets, SLOs) Full support Partial support Partial support

(Ray focus)

Partial support

(serve focus)

No support
Self-healing automation & retries Full support Partial support Partial support Partial support Partial support
Auto-scaling (inference & batch) Full support Full support

(GPU focus)

Full support

(Ray autoscaling)

Full support

(serverless)

Partial support
Multi-cloud / hybrid / BYOC Full support Partial support Partial support Partial support

(cloud first)

Full support

(infra/K8s ops)

Framework / runtime agnostic Full support Partial support

(NVIDIA-centric)

Partial support

(Ray-centric)

Partial support

(serve-centric)

Full support

(K8s-agnostic infra)

Governance, RBAC, compliance, FinOps Full support Partial support Partial support Partial support Full support
End-user console + admin consoles (SKUs by persona) Full support Partial support Partial support Partial support Partial support
Kubernetes management (clusters, multi-cluster) Full support Full support Partial support Partial support Full support
Serverless model hosting Full support Partial support Partial support Full support Partial support

Why Our Customers Love FlexAI

Our customers get measurable utilization and cost outcomes, not just a scheduler or a serving endpoint.

Notes: Numbers vary by model size, traffic pattern, and cloud pricing. We share customer-specific TCO models during evaluation. (Additional pricing comps available in our sales appendix.)

Time to launch

<60 seconds from CLI/console to running workloads cost.

Deploy in minutes, not weeks.

GPU utilization

typically >90% with workload-aware scheduling

Cost per Workload

50–80% lower cost per workload depending on baseline and mix, backed by our pricing and performance workups

In our H100 node annualized comparison, at 0.8–0.9 utilization ~46–47% vs. Run:AI and ~87% vs. Anyscale

Target SLOs for inference

p95 latency ≤50 ms, self-healing success >98% (design SLOs used in our inference PRD).

What Sets Us Apart

  • Breadth of the stack, from UX → control plane → infra abstraction
  • Multi-cloud portability
  • On-demand access to the right GPU for the job
  • Built-in governance and FinOps for complete control and compliance

One platform. Any cloud. Any hardware. Anywhere.

Get Started with $100 Credit