Proxon vs ZeroGPU (2026)
A side-by-side comparison of Proxon and ZeroGPU on pricing, features, and fit, so you can decide which is right for you.
Quick answer
Proxon and ZeroGPU are both strong choices, but they fit different needs. Choose Proxon if you mainly need auditing all ai applications in use across a large enterprise to identify shadow it risks — its edge is provides comprehensive visibility into both approved and unauthorized ai tool usage. Choose ZeroGPU if you need deploying large language model apis without managing dedicated gpu servers — its edge is significantly reduces gpu compute costs by eliminating idle resource waste. Proxon starts at On request; ZeroGPU starts at Custom pricing based on usage and compute requirements.
Features compared
- Automated discovery and inventory of all AI tools across an organization, including shadow AI
- Ownership assignment and policy binding per AI system
- Real-time spend tracking and budget attribution for each AI tool
- Centralized compliance and governance dashboard for IT and security teams
- Serverless GPU scheduling that allocates compute only during active inference requests
- Cost-efficient resource management to reduce idle GPU spend
- Support for popular AI model types including LLMs and image generation models
- Simple developer-friendly API for integrating inference into existing workflows
Pros & cons
- Provides comprehensive visibility into both approved and unauthorized AI tool usage
- Directly links AI tools to owners, policies, and costs for clear accountability
- Designed specifically for enterprise security and IT governance workflows
- Pricing is not publicly available, making it difficult to budget without a sales conversation
- May require significant onboarding effort to fully integrate with existing IT infrastructure
- Significantly reduces GPU compute costs by eliminating idle resource waste
- Simplifies infrastructure management so developers can focus on product building
- Flexible scaling suits both small projects and large production workloads
- Cold start latency may impact applications requiring ultra-low response times
- Pricing transparency is limited and custom quotes may complicate budget planning
The verdict
Choose Proxon if
you mainly need to auditing all ai applications in use across a large enterprise to identify shadow it risks. Its edge: provides comprehensive visibility into both approved and unauthorized ai tool usage.
Choose ZeroGPU if
you mainly need to deploying large language model apis without managing dedicated gpu servers. Its edge: significantly reduces gpu compute costs by eliminating idle resource waste.
Frequently asked questions
Is Proxon better than ZeroGPU?
Neither is universally better. Proxon is stronger for auditing all ai applications in use across a large enterprise to identify shadow it risks, with an edge in provides comprehensive visibility into both approved and unauthorized ai tool usage. ZeroGPU is stronger for deploying large language model apis without managing dedicated gpu servers, with an edge in significantly reduces gpu compute costs by eliminating idle resource waste. Pick based on your main task.
Which is cheaper, Proxon or ZeroGPU?
Proxon starts at On request and ZeroGPU starts at Custom pricing based on usage and compute requirements. Free tier: Proxon — No public free tier; ZeroGPU — Limited free tier available for small-scale inference workloads.
What is Proxon best for?
Proxon is best for auditing all ai applications in use across a large enterprise to identify shadow it risks, enforcing ai usage policies and mapping each tool to a responsible owner, tracking and controlling ai-related software spend across business units.
What is ZeroGPU best for?
ZeroGPU is best for deploying large language model apis without managing dedicated gpu servers, running image generation pipelines with variable or bursty traffic patterns, reducing cloud gpu costs for ai startups and research teams in production.
Do Proxon and ZeroGPU have free plans?
Proxon: No public free tier. ZeroGPU: Limited free tier available for small-scale inference workloads. Check each tool's pricing page for current limits, as plans change.