toolspool

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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.

Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.

👁 1.3M/mo17K
Lovable
✓ verifiedFreemium

Chat-based AI builder turning ideas into full software products.

👁 35M/mo69K
Code Autopilot
✓ verifiedFreemium

AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.

👁 21K/mo
Pricing
CPU (Small): $0.000025/sec ($0.09/hr)
Nvidia A100 80GB: $0.0014/sec ($5.04/hr)
Nvidia H100: $0.001525/sec ($5.49/hr)

Free trial available

No public pricing

Free trial available

No public pricing

DEVELOPER: FREE
STARTER: $119 / month
GROWTH: $599 / month
ENTERPRISE: Starting at $1,800 / month
Core features
  • One-line API calls to run community and proprietary AI models
  • Support for image, video, speech, and LLM generation models
  • Fine-tuning and custom model deployment via Cog
  • Per-second usage billing on shared or dedicated hardware
  • Automatic scaling for high-traffic private models
  • Thousands of community-published models with production APIs
  • AI-powered software development
  • Chat-based interface for specifying requirements
  • Full-stack engineering capabilities
  • Rapid app prototyping
  • Chat inside GitHub issues and PRs
  • Task-to-implementation plans with code
  • Automatic bug-fix suggestions
  • Pull-request summaries for faster review
  • Full-codebase context
  • GitHub-native integration
  • Developer-first platform for AI-powered integrations
  • Secure, isolated sandboxes for running JavaScript/Python code
  • Automatic management of npm/PyPI dependencies
  • Built-in platform plumbing: secrets, webhooks, scheduling, logs, and audit
  • Yep Agent (prompt → runnable processes)
  • MCP Server/Tools (convert code into AI agent tools)
  • Serverless runtime (YepCode Run) and SDK access
Use cases
  • Developers embedding image/video/speech generation into an app via API
  • Teams deploying and scaling their own fine-tuned models
  • Builders comparing outputs from multiple AI models in one playground
  • Companies avoiding GPU infrastructure management for ML inference
  • Creating developer portfolios
  • Building real estate listings applications
  • Developing file uploaders
  • Generating slide presentations
  • Speeding up pull-request reviews
  • Implementing features from task descriptions
  • Debugging with AI-proposed solutions
  • Answering questions about a repo
  • Boosting a solo developer's output
  • Building complex API integrations that require custom code and logic beyond what no-code tools offer.
  • Safely running AI-generated scripts in isolated environments with secrets management.
  • Automating workflows that require large datasets, loops, branching, or custom dependencies.
  • Connecting AI agents to external databases, APIs, and services using MCP tools.
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