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AI Tool Stacks
Experimental
Real-world tech stacks for building AI applications. Each includes build times, costs, examples, and trade-offs to help you choose.
From "ship in hours" to "enterprise-grade" - pick the stack that matches your timeline and requirements.
Quick Comparison
Choose based on your priorities
Fastest to Build
🥇 Bolt.new / Lovable (30 mins)
🥈 Next.js + Vercel AI (2 hours)
🥉 OpenAI Assistants (3 hours)
Lowest Cost
🥇 Ollama (free after hardware)
🥈 Next.js + Vercel ($0-20/mo)
🥉 OpenAI Assistants ($10-50/mo)
Most Powerful
🥇 LangChain (multi-agent)
🥈 Claude + MCP (secure tools)
🥉 Next.js + Vercel (full-stack)
Next.js + Vercel AI SDK
Beginner
Ship AI chat in hours, not weeks
The fastest way to build production-ready AI chat interfaces. Handles streaming, state management, and deployment out of the box.
Architecture
Frontend: Next.js 15 + React
Backend: Next.js API routes
LLM: OpenAI, Anthropic, or any provider
Database: Vercel Postgres or Supabase
Also includes:
Vercel AI SDK (useChat hook)
shadcn/ui for components
Tailwind CSS
Best For
- AI chatbots with streaming responses
- Customer support AI
- Internal tools with conversational UI
- Rapid prototyping
What You Can Build
ChatGPT Clone
2 hours
Full-featured chat with streaming, message history, and conversation management
RAG Chatbot
4 hours
Embed PDFs with pgvector, query with semantic search, stream contextual responses
AI Form Builder
6 hours
Generate forms from descriptions, validate inputs with AI, export code
✓ Pros
- • Fastest time to production
- • Excellent DX with useChat/useCompletion hooks
- • Free tier on Vercel
- • Active community and templates
- • Type-safe with TypeScript
⚠ Cons
- • Locked into Vercel ecosystem (less flexibility)
- • Limited for complex multi-agent systems
- • Less control over streaming internals
LangChain + Python
Intermediate
Build complex agents with mature tools
Industry standard for building sophisticated AI agents with tool calling, memory, and orchestration. Best for complex workflows and enterprise applications.
Architecture
Frontend: Streamlit, Gradio, or custom React
Backend: FastAPI + LangChain
LLM: GPT-5, Claude, or open source
Database: Pinecone, Chroma, or Supabase pgvector
Also includes:
LangSmith for observability
LangGraph for agent orchestration
LangServe for deployment
Best For
- Multi-step agent workflows
- Document analysis and RAG systems
- Agent teams and orchestration
- Research and experimentation
What You Can Build
Research Assistant
1 day
Agent that searches web, reads PDFs, synthesizes findings with citations
SQL Analytics Agent
2 days
Natural language to SQL, execute queries, generate visualizations
Multi-Agent System
3 days
Coordinator agent that delegates to specialist agents (research, writing, coding)
✓ Pros
- • Most comprehensive agent framework
- • Excellent documentation and community
- • LangSmith for production debugging
- • Supports all major LLM providers
- • Pre-built chains and agents
⚠ Cons
- • Steeper learning curve
- • Heavy abstraction can obscure behavior
- • Python-first (JS version less mature)
- • Can be overkill for simple use cases
Claude + MCP
Intermediate
Secure, standardized agent-tool connections
Build agents using Anthropic's Model Context Protocol. Standardized way to connect Claude to tools, databases, and APIs with security and auditability.
Architecture
Frontend: Claude Desktop or custom UI
Backend: MCP servers (TypeScript/Python)
LLM: Claude Sonnet 4.5 or Haiku 4.5
Database: Supabase, PostgreSQL, or cloud
Also includes:
MCP SDKs
Prompt caching for cost savings
Extended thinking mode
Best For
- Enterprise agent deployments
- Security-sensitive applications
- Tool-heavy agent workflows
- Long-context analysis (1M tokens)
What You Can Build
Code Analysis Agent
1 day
MCP server for git, files, and terminal. Claude reviews PRs and suggests fixes
Data Pipeline Agent
2 days
MCP servers for Postgres, S3, and Slack. Agent monitors data quality and alerts
✓ Pros
- • Standardized protocol (not vendor-locked)
- • Security by design with server isolation
- • 1M context window for massive documents
- • Prompt caching (90% cost reduction)
- • Great for enterprise compliance
⚠ Cons
- • Newer ecosystem (fewer examples)
- • Requires understanding MCP protocol
- • Claude-specific (not multi-model yet)
- • Desktop-first approach (web support coming)
OpenAI Assistants API
Beginner
Managed agents with built-in memory
Let OpenAI handle threading, memory, and tool orchestration. Build stateful agents without managing infrastructure.
3-8 hours for MVP
$10-50/month + usage
Architecture
Frontend: Any (React, Vue, vanilla JS)
Backend: OpenAI Assistants API
LLM: GPT-5, GPT-5 mini, GPT-5 nano
Database: Managed by OpenAI (threads)
Also includes:
Code Interpreter
File Search
Function calling
Best For
- Customer service bots with memory
- Persistent agent conversations
- Code interpreters and file analysis
- Teams wanting managed infrastructure
What You Can Build
Support Bot
3 hours
Multi-turn conversations with memory, knowledge base search, ticket creation
Data Analyst
5 hours
Upload CSVs, analyze with Code Interpreter, generate charts and insights
✓ Pros
- • No backend infrastructure needed
- • Built-in conversation threading
- • Code Interpreter for data analysis
- • Automatic memory management
- • Simple API, fast development
⚠ Cons
- • Vendor lock-in to OpenAI
- • Less control over internals
- • Can get expensive at scale
- • Limited customization of orchestration
Local: Ollama + Open WebUI
Intermediate
Run AI completely on your machine
Privacy-first AI stack. Run Llama, Qwen, or Mistral locally with no API costs. Perfect for sensitive data or offline use.
Architecture
Frontend: Open WebUI (self-hosted)
Backend: Ollama model serving
LLM: Llama 4, Qwen 2.5, Mistral, DeepSeek
Database: Local SQLite or Postgres
Also includes:
LangChain for orchestration
ChromaDB for vectors
GPU recommended (RTX 3090+)
Best For
- Privacy-sensitive applications
- Offline AI capabilities
- Experimentation without API costs
- Learning model internals
What You Can Build
Private Chat Assistant
4 hours
ChatGPT-like interface running entirely locally with Llama 4
Document Q&A
1 day
Embed sensitive docs locally, query with RAG, zero data leaves your machine
✓ Pros
- • Complete privacy and data control
- • No API costs (free inference)
- • Works offline
- • Full model customization possible
- • Great for learning
⚠ Cons
- • Requires powerful GPU (16GB+ VRAM)
- • Slower inference than cloud
- • Local models lag SOTA by 6-12 months
- • More setup and maintenance
Quick Start: Bolt.new / Lovable
Beginner
AI builds your app for you
No-code/low-code AI builders. Describe your app, AI generates the full stack. Best for rapid prototyping and non-technical builders.
30 mins - 2 hours
$20/month subscription
Architecture
Frontend: Generated React/Next.js
Backend: Generated Node.js/Python
LLM: Built-in (GPT-5, Claude)
Database: Supabase or cloud services
Also includes:
One-click deployment
AI iteration and debugging
Export code
Best For
- Non-technical founders testing ideas
- Rapid prototyping and demos
- Learning by seeing generated code
- Internal tools and MVPs
What You Can Build
SaaS Landing Page
30 mins
Describe features, AI generates responsive landing with forms and analytics
CRUD App
1 hour
AI generates full-stack app with auth, database, and admin panel
Internal Dashboard
2 hours
Connect to APIs, AI builds data viz dashboard with filters and exports
✓ Pros
- • Fastest possible time to working app
- • No coding required
- • Learn by seeing generated code
- • Iterate with natural language
- • One-click deployment
⚠ Cons
- • Limited customization
- • Generated code can be messy
- • Vendor lock-in
- • Not suitable for complex applications
- • Subscription cost adds up
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