Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
⇄ Comparison dimension — pick the market you're actually shopping in
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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
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Codeamigo
✓ verifiedFree
AI-assisted coding-tutorial tool for learning to code; now unmaintained as its creator moved to another project.
481 visits/mo
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Runware
✓ verifiedFreemium
Pay-as-you-go API aggregating thousands of image, video, audio and LLM models with custom inference hardware for lower per-request cost.
249K visits/mo
Pricing
Business: $100/mo flat (up to 50 seats, $100 usage included)
Free trial available
No public pricing
No public pricing
vCPU compute: $0.016/hr
RTX PRO 6000: $1.99/hr (as low as $0.99)
H100: $2.76/hr
H200: $3.18/hr
B200: $4.99/hr
Free trial available
Core features
- ✦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)
- ✦Weekly newsletter with high-quality insights
- ✦Deep dives into ML topics
- ✦Tools used by Machine Learning engineers
- ✦ML System design course (coming soon)
- ✦YouTube channel (coming soon)
- ✦Archive of past articles
- ✦AI-powered coding tutorials
- ✦Interactive, developer-style lessons
- ✦Guided learning with modern tools
- ✦Demo project walkthrough
- ✦Waitlist sign-up (courses coming soon)
- ✦Single API for image, video, audio, 3D and LLM models
- ✦Standardized model addressing across hosted, partner and custom uploads
- ✦Support for LoRAs, ControlNets, VAEs and embeddings on open-source models
- ✦WebSocket and REST access with async webhook delivery
- ✦Pay-per-request billing with no infrastructure to manage
- ✦Raw serverless GPU/CPU compute for custom workloads
Use cases
- →Automating PR code review
- →Raising test coverage
- →Incident investigation and remediation
- →Large-scale migrations and onboarding
- →Upskilling as a Machine Learning engineer
- →Learning about ML systems at scale
- →Staying updated on the latest ML tools and techniques
- →Understanding ML system design principles
- →Learning to code with an AI assistant
- →Following interactive coding tutorials
- →Practicing with guided project examples
- →Adding AI image or video generation to an app without managing infra
- →Batching multi-modal generation tasks in one API call
- →Running custom fine-tuned models via Model Upload
- →Cutting inference costs at high generation volume
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