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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.

CodeFast logo
CodeFast
✓ verifiedPaid

Beginner-friendly online course teaching entrepreneurs to build and launch a real SaaS in about 14 days using web basics and AI.

53K visits/mo
Runware logo
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
Convex logo
Convex
✓ verifiedFreemium

TypeScript backend-as-a-service with a reactive database, server functions, auth and file storage for full-stack and AI apps.

692K visits/mo20K saves
Raster logo
Raster
✓ verifiedFreemium

Digital asset manager built for teams and AI agents needing structured, searchable image libraries with fast tagging and delivery.

4.2K visits/mo
Ultralytics logo
Ultralytics
✓ verifiedFreemium

End-to-end computer vision platform for teams annotating data, training YOLO models, and deploying them at scale.

1.1M visits/mo
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

Free & Starter: $0/mo (pay-as-you-go, 1-6 developers)
Professional: $25/developer/mo
Business & Enterprise: $2,500/mo minimum
Personal: Free forever (1 GB storage, 3 libraries)
Team: $9/user/month (unlimited storage, 50 libraries)
Free: $0/month (100GB storage, 100 models, 3 concurrent trainings)
Pro: $29/seat/month (500GB storage, 500 models, 10 concurrent trainings)
Core features
  • ~12 hours of structured video lessons
  • Web fundamentals (HTML, CSS, JavaScript)
  • Build a real SaaS with Next.js, React, Tailwind, MongoDB
  • Authentication (magic link, Google OAuth)
  • Stripe and LemonSqueezy subscriptions
  • Deployment, domains, DNS and hosting
  • AI-assisted coding and design, Git/GitHub
  • 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
  • Reactive real-time database
  • TypeScript server functions (queries/mutations/actions)
  • Built-in authentication
  • Cron jobs and backend workflows
  • File storage, text and vector search
  • ACID transactions; open-source/self-host
  • AI-based auto-tagging of images
  • Reverse image search
  • Live team collaboration on libraries
  • Built-in image editing with 20+ adjustments
  • CDN delivery with persistent, editable image URLs
  • Agent-facing API for automated asset retrieval
  • Integrations with Contentful, DatoCMS, Figma, Sanity
  • Smart data annotation with SAM-powered one-click masks across six task types
  • Cloud training with 22+ GPU configurations from RTX 2000 Ada to B200
  • Support for YOLOv5 through YOLO26 model families
  • One-click deployment across 43 global regions with auto-scaling
  • Export to 18 formats including ONNX, TensorRT, and CoreML
  • Live training metrics and experiment comparison dashboard
Use cases
  • Learn to code as a founder
  • Build and launch a first SaaS
  • Ship an MVP quickly
  • Add payments and auth to an app
  • 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
  • Building real-time reactive apps
  • Backends for AI agents
  • Replacing Firebase or Supabase
  • Full-stack TypeScript development
  • Centralizing product photos before a launch
  • Giving an AI agent approved brand imagery to pull from
  • Managing ecommerce image variants
  • Sharing design references with a team or client
  • Serving optimized images to a CMS or storefront
  • Building and training custom object detection or segmentation models
  • Labeling large image/video datasets for computer vision projects
  • Deploying vision models to edge or mobile devices
  • Running quality control or defect detection in manufacturing
  • Powering retail, logistics, or agriculture vision applications
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