The Kilo Team is a remote-first team that operates out of San Francisco and Amsterdam. Their "Kilo Speed" philosophy is to have all product features owned by the same developers who are also the owners of their own AI agents.
The Kilo Team has developed a range of different products, including Kilo Sessions, Code Reviewer, Cloud Agents, Kilo Man, Autocomplete, and Parallel Agents. They have developed these products quickly with the help of their AI agents, and have shipped them in just six weeks.
The Kilo Team uses a number of different methods to promote the adoption of AI across their user base. One method is to monitor and support the use of AI in teams across large development organizations.
In addition to promoting the adoption of AI, the Kilo Team also promotes transparency and reliability by using the open source MIT License and by dogfooding, or using Kilo to develop Kilo.
Remote-first with San Francisco and Amsterdam offices
Orchestration layer supporting 500+ models including Architect Agent, Orchestrator Agent, Code Agent, Debug Agent, and Code Reviewer Agent with parallel execution capabilities
Inferred from feature descriptions and open-source nature; specific stack details from official website
| Service | Cost | Details | Source |
|---|---|---|---|
| Free Plan | $0 (pay per token usage) | Core agent features included. Pay only for AI model tokens consumed via built-in provider credits at cost or bring your own API keys. Includes $5 free credits on signup. | Kilo official documentation |
| Team Plan | $15/user/month | Centralized billing, team dashboard, usage analytics, shared modes, role-based permissions, audit logs, collaboration and governance capabilities. | Kilo official documentation |
| Enterprise Plan | Custom pricing | All Team features plus enterprise-grade tooling, advanced collaboration, and custom requirements. | Kilo official documentation |
| Budget Models (Pay-per-use) | $0.20-$0.30 per million input tokens | Ultra-budget options like Mistral Devstral Small ($0.20/M tokens, 85% of premium capability) and Llama 4 Maverick ($0.30/M tokens) for cost-effective development. | Kilo documentation - Free and Budget Models |
| Free Models | $0 | Completely free models available through Kilo Gateway and OpenRouter free tier. No token costs. | Kilo documentation - Free and Budget Models |
| Feature | Kilo Code | Cursor | Windsurf | GitHub Copilot |
|---|---|---|---|---|
| Core Functionality | Full AI coding agent (plan, code, test, debug, review, deploy) | AI code editor with inline editing | AI-native IDE | Code completion and chat |
| Model Support | 500+ models, 60+ providers (OpenAI, Anthropic, Google, Mistral, Meta Llama) | Limited to Cursor-selected models | Limited to Windsurf-selected models | OpenAI GPT-4 only |
| IDE Integration | VS Code, JetBrains, Cursor, Windsurf, CLI | Standalone editor only | Standalone IDE only | VS Code, JetBrains, Visual Studio |
| Starting Price | $0 (free) + token costs | $20/month | $0-20/month | $10/month |
| Free Tier | Yes - fully functional, no tool cost | Limited trial | Partial free tier | Limited usage free tier |
| Team Features | Yes ($15/user/month) | Enterprise only | Enterprise only | Enterprise only |
| Open Source | Yes | No | No | No |
| Bring Your Own Model | Yes (any provider) | Limited | Limited | No |
| Transparent Pricing | Yes - exact list price, no commissions | Flat monthly | Flat monthly | Flat monthly |
While Copilot is primarily a tool for providing suggestions for code completion and chat functionality within the confines of existing Integrated Development Environments (IDEs), Kilo is a full-fledged end-to-end AI-based coding agent capable of planning, architecture, testing, debugging, and deployment. Kilo has the ability to utilize over 500 models versus Copilot's capability of utilizing GPT-4 only. Additionally, Kilo is an open-source and vendor-neutral product, while Copilot is a proprietary and closed system locked to OpenAI. Finally, both Copilot and Kilo have similar initial price points (i.e., approximately $10-$15 per month).
Use Copilot if you want an integrated IDE experience as well as use Copilot Chat; use Kilo if you are working with multi-agent workflows, need flexible modeling, and want to take advantage of open-source transparency.
While Cursor is a highly-specialized AI-based code editor with significant inline editing capabilities, Kilo is a platform-independent agent and integrates with existing IDEs. Kilo provides greater flexibility with regard to model selection (over 500 models) compared to Cursor, which is limited in this regard. In terms of pricing, Cursor has a higher monthly cost (approximately $20 per month) compared to Kilo's cost structure (free base tier + token based costs) which could result in Kilo being more economical for light users, or Cursor being more economical for heavy users.
Use Cursor if you want to utilize an AI-first editing experience; use Kilo for the flexibility of choosing what you want to do when it comes to cost and using the IDE that you already know and love.
WindSurf is a stand-alone AI-native IDE while Kilo is an integrated agent that is able to integrate into existing development environments. Both products offer a free tier version, however, Kilo is functionally complete at no cost whereas WindSurf has a limited free tier version. Kilo has a number of advantages when compared to WindSurf, including its model flexibility (over 500 models) and open-source nature versus WindSurf's proprietary and curated approach. WindSurf appears to appeal to users who desire an all-encompassing IDE, whereas Kilo appears to appeal to users who wish to enhance their current development environment.
Use Windsurf if you want to replace your entire IDE; use Kilo if you want to add AI to your current IDE.
Works across all major programming languages via 500+ AI models
Works in IDEs, terminal, browser, and syncs across devices
Generates intelligent multiline code completion
Generates and refactors coding across multiple files
Plans a project's overall structure and generates blueprints
Automatically detects errors and recovers them
Automatically analyzes and improves pull requests (PRs)
Caches common coding patterns and contextual knowledge about projects
Simultaneously run multiple AI-based agents to process workloads
Directly deploy to production from an integrated development environment (IDE) with automatic configuration
