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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

Kilo Code logo
Kilo Code
✓ verifiedFreemium

Open-source AI coding agent for VS Code, JetBrains, CLI and cloud, with 500+ models at zero inference markup and BYOK.

10K visits/mo57K saves
389K visits/mo
248K visits/mo4.8K saves
Augment Code logo
Augment Code
✓ verifiedPaid

Agentic coding platform (Cosmos) that runs software-dev agents at org scale, using a codebase context engine to cut token cost.

544K visits/mo
PromptLayer logo
PromptLayer
✓ verifiedFree

Prompt engineering, management, and LLM observability platform.

212K visits/mo
Pricing
Free: $0 (open source; AI usage billed separately)
Teams: $15/user/mo (14-day free trial)
KiloClaw hosting: from $55/mo

Free trial available

Pay-as-you-go: 42.8% platform fee for corporate, 33.3% for academic/non-profit (no monthly fee)
Participant payment: minimum $8.00/hr, recommended $12.00/hr

No public pricing

Business: $100/mo flat (up to 50 seats, $100 usage included)

Free trial available

No public pricing

Core features
  • 500+ AI models at zero inference markup
  • Bring-your-own-keys and local model support
  • MIT-licensed, fully open source
  • Works in VS Code, JetBrains, CLI and cloud
  • Agent modes (Code, Architect)
  • Parallel isolated worktrees
  • Slack code reviewer and gateway
  • Access to a verified and engaged participant pool
  • Self-serve platform for easy task setup and launch
  • Tools for AI training and evaluation
  • Fair compensation for participants
  • Audience checker
  • Lifelike voice AI agents
  • 24/7 availability
  • Customer-led conversational platform
  • Integration with enterprise systems
  • Context Engine for codebase understanding
  • Agents across the full SDLC
  • Model routing / bring-your-own-keys
  • Automated code review and test coverage
  • CLI, MCP and native tool integrations
  • Enterprise security (SOC 2, ISO 42001, SSO)
  • Prompt management
  • Prompt evaluations
  • LLM observability
  • Team collaboration
  • Version control for prompts
  • A/B testing of prompts
  • Prompt Registry
  • Historical backtests
  • Regression tests
  • Usage monitoring
Use cases
  • Writing and refactoring production code with AI
  • Planning features before implementation
  • Running agents across multiple IDEs and the CLI
  • Academic research
  • AI training and evaluation
  • Market research
  • User research & testing
  • Data annotation
  • Training & alignment
  • Evaluation & safety
  • Answering customer service calls
  • Providing information and support
  • Resolving customer issues
  • Automating call center operations
  • Automating PR code review
  • Raising test coverage
  • Incident investigation and remediation
  • Large-scale migrations and onboarding
  • Scaling customer support automation with LLMs
  • Empowering non-technical teams with prompt engineering
  • Building personalized AI interactions
  • Debugging LLM agents
  • Improving content creation processes
  • Managing and monitoring prompts with a team
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