Evidence
Kong
| Feature | Evidence | Kong |
|---|---|---|
| Pricing | Free / from $49/mo | Free / from $0.05/mo |
| Free Plan | ✓ Yes | ✓ Yes |
| Rating | 4.3 / 5 | 4.3 / 5 |
| Best For | data-analysts, data-engineers, startups, analytics-teams | platform-engineers, microservices-teams, api-gateway-users, devops-teams |
| Founded | 2021 | 2010 |
| Sql Reporting | ✓ | ✗ |
| Markdown Pages | ✓ | ✗ |
| Charts | ✓ | ✗ |
| Static Deployment | ✓ | ✗ |
| Git Integration | ✓ | ✗ |
| Data Sources | ✓ | ✗ |
| Api Gateway | ✗ | ✓ |
| Service Mesh | ✗ | ✓ |
| Load Balancing | ✗ | ✓ |
| Authentication | ✗ | ✓ |
| Rate Limiting | ✗ | ✓ |
| Plugins | ✗ | ✓ |
| Observability | ✗ | ✓ |
| Kubernetes Ingress | ✗ | ✓ |
✓ Evidence Pros
- Code-based reports (version controlled)
- Beautiful output
- Fast static deployment
- SQL-first approach
✗ Evidence Cons
- Requires SQL knowledge
- Not for ad-hoc exploration
- Smaller community
✓ 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
The Verdict
Evidence is built for data analysts and data engineers, with a focus on sql-reporting and markdown-pages. Kong targets platform engineers and microservices teams and leads with api-gateway and service-mesh.
On pricing, Kong is the clear winner for budget-conscious users — starting at $0.05/mo compared to $49/mo for Evidence. That $48.95/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 6), while Evidence 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.