Continue
Kong
| Feature | Continue | Kong |
|---|---|---|
| Pricing | Free only | Free / from $0.05/mo |
| Free Plan | ✓ Yes | ✓ Yes |
| Rating | 4.3 / 5 | 4.3 / 5 |
| Best For | developers, open-source-advocates, privacy-focused-devs, self-hosters | platform-engineers, microservices-teams, api-gateway-users, devops-teams |
| Founded | 2023 | 2010 |
| Autocomplete | ✓ | ✗ |
| Chat | ✓ | ✗ |
| Inline Editing | ✓ | ✗ |
| Multi Model Support | ✓ | ✗ |
| Context Providers | ✓ | ✗ |
| Custom Commands | ✓ | ✗ |
| Api Gateway | ✗ | ✓ |
| Service Mesh | ✗ | ✓ |
| Load Balancing | ✗ | ✓ |
| Authentication | ✗ | ✓ |
| Rate Limiting | ✗ | ✓ |
| Plugins | ✗ | ✓ |
| Observability | ✗ | ✓ |
| Kubernetes Ingress | ✗ | ✓ |
✓ Continue Pros
- Fully open-source (Apache 2.0)
- Works with any LLM provider
- VS Code and JetBrains support
- Local model support
✗ Continue Cons
- Requires self-configuration of LLM
- Less polished than Copilot
- Setup can be complex for beginners
✓ 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
Continue is built for developers and open source advocates, with a focus on autocomplete and chat. Kong targets platform engineers and microservices teams and leads with api-gateway and service-mesh.
Continue uses custom enterprise pricing, while Kong starts at $0.05/mo — a tangible advantage for teams with a fixed budget.
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 Continue 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.