Kong icon

Kong

★★★★ 4.3
VS
Penpot icon

Penpot

★★★★ 4.3
Feature Kong Penpot
Pricing Free / from $0.05/mo Free / from $8/mo
Free Plan ✓ Yes ✓ Yes
Rating 4.3 / 5 4.3 / 5
Best For platform-engineers, microservices-teams, api-gateway-users, devops-teams open-source-teams, privacy-focused-designers, developers, educational-institutions
Founded 2010 2015
Api Gateway
Service Mesh
Load Balancing
Authentication
Rate Limiting
Plugins
Observability
Kubernetes Ingress
Vector Editing
Prototyping
Components
Design Tokens
Real Time Collaboration
Css Output
Self Hostable

✓ Kong Pros

  • Open-source core with large plugin ecosystem
  • Sub-millisecond latency for API requests
  • Platform-agnostic deployment (cloud, on-prem, hybrid)
  • Strong Kubernetes-native support

✗ Kong Cons

  • Enterprise features require paid license
  • Configuration complexity for advanced setups
  • Documentation could be more beginner-friendly

✓ Penpot Pros

  • Open-source and self-hostable for free
  • CSS-based design outputs production-ready code
  • Real-time collaboration (Figma-like experience)
  • SVG-native (no proprietary formats)

✗ Penpot Cons

  • Performance slower than Figma on complex files
  • Smaller plugin and community ecosystem
  • Missing some advanced design features

The Verdict

Kong is built for platform engineers and microservices teams, with a focus on api-gateway and service-mesh. Penpot targets open source teams and privacy focused designers and leads with vector-editing and prototyping.

On pricing, Kong is the clear winner for budget-conscious users — starting at $0.05/mo compared to $8/mo for Penpot. That $7.95/mo difference adds up quickly for growing teams.

Both offer free plans, so you can test each with your real workflow before committing to a subscription.

Feature-wise, Kong offers broader built-in capabilities (8 features vs 7), while Penpot takes a more focused approach — which can mean a simpler, faster onboarding experience.

This is a genuinely close comparison. If you can, sign up for both free trials (where available) and run a one-week test with your actual team tasks before deciding.

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