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Development & APIs

Technical resources for developers building AI-powered applications. APIs, frameworks, hosting solutions, and development tools for creating custom AI solutions.

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Content created with AI assistance - may contain errors or become outdated.

AI APIs & SDKs​

Core APIs for integrating AI capabilities into your applications.

OpenAI API​

  • Link: platform.openai.com
  • Description: Industry-leading API for GPT models, DALL-E, Whisper, and text-to-speech.
  • Pricing: Pay-per-use, starting at $0.0005/1K tokens (GPT-4o mini)
  • Best for: General AI applications, chatbots, content generation, code assistance
  • Key features: GPT-4, DALL-E 3, Whisper ASR, function calling, fine-tuning, batch processing
  • SDKs: Python, Node.js, Go, .NET, Java, official libraries

Anthropic API (Claude)​

  • Link: console.anthropic.com
  • Description: API access to Claude models with focus on safety and helpfulness.
  • Pricing: Pay-per-use, starting at $0.00025/1K tokens (Claude 3 Haiku)
  • Best for: Long-form content, analysis, coding assistance, safety-critical applications
  • Key features: Large context windows, constitutional AI, document processing, function calling
  • SDKs: Python, TypeScript, Go, official SDKs available

Google AI API (Gemini)​

  • Link: ai.google.dev
  • Description: Google's multimodal AI models with competitive pricing and performance.
  • Pricing: Free tier available, pay-per-use pricing starts at $0.000125/1K tokens
  • Best for: Multimodal applications, Google ecosystem integration, cost-effective solutions
  • Key features: Multimodal inputs, competitive pricing, function calling, long context
  • SDKs: Python, Node.js, Go, Swift, Dart, Android, REST API

Hugging Face API​

  • Link: huggingface.co/inference-api
  • Description: Access to thousands of open-source models via unified API.
  • Pricing: Free tier, Pro ($9/month), Enterprise (custom)
  • Best for: Open-source models, specialized tasks, model experimentation
  • Key features: 150K+ models, custom model deployment, AutoTrain, datasets integration
  • SDKs: Python (transformers), JavaScript, curl/REST

Cohere API​

  • Link: cohere.ai
  • Description: Enterprise-focused NLP API with multilingual capabilities and enterprise features.
  • Pricing: Free tier, usage-based pricing, enterprise plans available
  • Best for: Enterprise NLP, multilingual applications, semantic search, classification
  • Key features: Command models, Embed models, Rerank, multilingual support, enterprise security
  • SDKs: Python, Node.js, Go, Java, curl/REST

Replicate API​

  • Link: replicate.com
  • Description: Run and scale AI models in the cloud with simple API access.
  • Pricing: Pay-per-second of compute time, no monthly fees
  • Best for: Image/video generation, specialized models, cost-effective scaling
  • Key features: Thousands of models, custom model deployment, automatic scaling, no infrastructure management
  • SDKs: Python, Node.js, Go, REST API

Frameworks & Libraries​

Development frameworks for building AI-powered applications.

LangChain​

  • Link: langchain.com | GitHub
  • Description: Framework for developing applications with language models and external data sources.
  • Pricing: Open source (free), LangSmith platform has usage-based pricing
  • Best for: RAG applications, agent systems, complex AI workflows, chatbots
  • Key features: Chain composition, document loading, vector stores, agents, memory, prompt templates
  • Languages: Python, JavaScript/TypeScript, extensive ecosystem

LlamaIndex​

  • Link: llamaindex.ai | GitHub
  • Description: Data framework for connecting custom data sources to large language models.
  • Pricing: Open source (free), LlamaCloud has usage-based pricing
  • Best for: RAG applications, document question-answering, knowledge base creation
  • Key features: Data connectors, indexing strategies, query engines, chat engines, evaluation tools
  • Languages: Python, TypeScript, growing ecosystem

AutoGen​

  • Link: microsoft.github.io/autogen
  • Description: Microsoft's framework for multi-agent conversation systems and automated workflows.
  • Pricing: Open source (free)
  • Best for: Multi-agent systems, automated workflows, collaborative AI agents
  • Key features: Multi-agent conversations, code execution, group chat, human-in-the-loop
  • Languages: Python, growing community contributions

CrewAI​

  • Link: crewai.com | GitHub
  • Description: Framework for orchestrating role-playing, autonomous AI agents for collaborative tasks.
  • Pricing: Open source (free)
  • Best for: Task automation, collaborative AI agents, business process automation
  • Key features: Role-based agents, task delegation, process automation, tool integration
  • Languages: Python

Haystack​

  • Link: haystack.deepset.ai
  • Description: End-to-end framework for building search systems and question-answering applications.
  • Pricing: Open source (free)
  • Best for: Search applications, document question-answering, enterprise search
  • Key features: Pipeline architecture, document stores, retrievers, readers, evaluation
  • Languages: Python

Semantic Kernel​

  • Link: aka.ms/semantic-kernel
  • Description: Microsoft's SDK for integrating AI models into applications with planning and memory.
  • Pricing: Open source (free)
  • Best for: .NET/C# applications, enterprise integration, structured AI workflows
  • Key features: Planners, plugins, memory, connectors, enterprise-ready
  • Languages: C#, Python, Java

Model Hosting & Deployment​

Platforms for hosting, deploying, and scaling AI models in production.

Replicate​

  • Link: replicate.com
  • Description: Cloud platform for running AI models with automatic scaling and simple API.
  • Pricing: Pay-per-second compute, no monthly fees or infrastructure costs
  • Best for: Model deployment without infrastructure management, cost-effective scaling
  • Key features: Automatic scaling, version control, custom models, pre-built models, simple API

RunPod​

  • Link: runpod.io
  • Description: GPU cloud platform optimized for AI workloads with serverless and dedicated options.
  • Pricing: Serverless (pay-per-second), Secure Cloud (hourly), Community Cloud (competitive rates)
  • Best for: GPU-intensive workloads, model training, cost-effective GPU access
  • Key features: Serverless functions, Jupyter notebooks, template library, global infrastructure
  • Link: modal.com
  • Description: Serverless platform designed specifically for AI/ML workloads with Python focus.
  • Pricing: Pay-per-second compute, generous free tier
  • Best for: Python AI applications, batch processing, model inference, research
  • Key features: Serverless functions, containers, distributed computing, automatic scaling

Together AI​

  • Link: together.ai
  • Description: Platform for running and fine-tuning open-source models with competitive pricing.
  • Pricing: Pay-per-token, competitive rates for open-source models
  • Best for: Open-source model deployment, fine-tuning, cost-effective inference
  • Key features: 50+ models, fine-tuning, inference API, model comparisons

Hugging Face Inference Endpoints​

  • Link: huggingface.co/inference-endpoints
  • Description: Managed service for deploying Hugging Face models with automatic scaling.
  • Pricing: Usage-based, starts at $0.032/hour for CPU, GPU options available
  • Best for: Hugging Face model deployment, custom model hosting, auto-scaling
  • Key features: Any Hugging Face model, auto-scaling, secure endpoints, monitoring

AWS SageMaker​

  • Link: aws.amazon.com/sagemaker
  • Description: Fully managed machine learning platform for building, training, and deploying ML models.
  • Pricing: Pay-per-use for compute, storage, and data processing
  • Best for: Enterprise ML workflows, large-scale deployments, AWS ecosystem integration
  • Key features: Model training, real-time inference, batch transform, model registry, monitoring

Vector Databases & Search​

Specialized databases for storing and querying vector embeddings.

Pinecone​

  • Link: pinecone.io
  • Description: Fully managed vector database optimized for machine learning applications.
  • Pricing: Free tier (1M vectors), Starter ($70/month), Enterprise (custom)
  • Best for: Production vector search, recommendation systems, semantic search
  • Key features: Real-time updates, metadata filtering, hybrid search, high performance, managed service

Weaviate​

  • Link: weaviate.io
  • Description: Open-source vector database with GraphQL API and ML model integration.
  • Pricing: Open source (free), Weaviate Cloud Services (usage-based)
  • Best for: Self-hosted solutions, complex queries, multi-modal search
  • Key features: GraphQL API, automatic vectorization, multi-modal, hybrid search, open source

Chroma​

  • Link: trychroma.com
  • Description: Open-source embedding database designed for AI applications.
  • Pricing: Open source (free), hosted service in development
  • Best for: Development and prototyping, Python/JavaScript applications
  • Key features: Simple API, local development, metadata filtering, open source, active community

Qdrant​

  • Link: qdrant.tech
  • Description: Vector search engine with advanced filtering and high performance.
  • Pricing: Open source (free), Qdrant Cloud (usage-based)
  • Best for: High-performance vector search, complex filtering requirements
  • Key features: Advanced filtering, quantization, distributed deployment, REST and gRPC APIs

Milvus​

  • Link: milvus.io
  • Description: Open-source vector database built for scalable similarity search.
  • Pricing: Open source (free), Zilliz Cloud (managed service)
  • Best for: Large-scale vector search, enterprise deployments, high availability
  • Key features: Horizontal scaling, multiple index types, cloud-native, ACID compliance
  • Link: redis.io/docs/stack/search/reference/vectors
  • Description: Vector search capabilities built into Redis for real-time applications.
  • Pricing: Redis pricing model, Redis Cloud available
  • Best for: Real-time applications, existing Redis users, low-latency search
  • Key features: Real-time search, Redis ecosystem integration, high performance, familiar tooling

AI Development Platforms​

Comprehensive platforms for AI development workflows.

Weights & Biases (W&B)​

  • Link: wandb.ai
  • Description: Platform for ML experiment tracking, model versioning, and collaboration.
  • Pricing: Free for personal use, Team ($20/user/month), Enterprise (custom)
  • Best for: Experiment tracking, model registry, team collaboration, MLOps
  • Key features: Experiment tracking, hyperparameter tuning, model registry, reports, artifacts

MLflow​

  • Link: mlflow.org
  • Description: Open-source platform for managing ML lifecycle including experiments and deployment.
  • Pricing: Open source (free), Databricks Managed MLflow (usage-based)
  • Best for: ML lifecycle management, model versioning, experiment tracking
  • Key features: Tracking, projects, models, registry, deployment, open source

Streamlit​

  • Link: streamlit.io
  • Description: Framework for building and sharing machine learning web applications.
  • Pricing: Open source (free), Streamlit Cloud (free for public apps), Enterprise (custom)
  • Best for: ML app prototypes, data dashboards, model demos
  • Key features: Python-first, rapid development, easy deployment, community cloud

Gradio​

  • Link: gradio.app
  • Description: Library for creating web interfaces for machine learning models.
  • Pricing: Open source (free), Hugging Face Spaces integration (free/paid tiers)
  • Best for: Model demos, quick interfaces, sharing ML models
  • Key features: Simple API, automatic interface generation, sharing capabilities, Hugging Face integration

LangSmith​

  • Link: smith.langchain.com
  • Description: Platform for debugging, testing, and monitoring LLM applications.
  • Pricing: Free tier, Plus ($39/month), Pro ($199/month), Enterprise (custom)
  • Best for: LangChain applications, LLM debugging, production monitoring
  • Key features: Tracing, evaluation, testing, monitoring, prompt management, LangChain integration

Development Tools & SDKs​

Tools and libraries for AI application development.

OpenAI Libraries​

  • Python: pip install openai
  • Node.js: npm install openai
  • Description: Official SDKs with comprehensive API coverage and streaming support
  • Best for: OpenAI API integration, type safety, streaming responses

Anthropic Libraries​

  • Python: pip install anthropic
  • TypeScript: npm install @anthropic-ai/sdk
  • Description: Official SDKs with async support and streaming capabilities
  • Best for: Claude API integration, async operations, type safety

Transformers (Hugging Face)​

  • Python: pip install transformers
  • Description: State-of-the-art machine learning library for natural language processing
  • Best for: Model inference, fine-tuning, tokenization, local model deployment

LangChain Libraries​

  • Python: pip install langchain
  • JavaScript: npm install langchain
  • Description: Comprehensive framework for building applications with LLMs
  • Best for: Complex AI workflows, RAG applications, agent systems

OpenAI Cookbook​

  • Link: github.com/openai/openai-cookbook
  • Description: Example code and guides for using OpenAI API effectively
  • Best for: Learning best practices, implementation examples, use case inspiration

Getting Started Guide​

For Backend Developers​

  1. Choose an API: Start with OpenAI, Anthropic, or Google AI APIs
  2. Learn a framework: Try LangChain for complex workflows or direct API calls for simple use cases
  3. Add vector search: Integrate Pinecone or Chroma for semantic search capabilities
  4. Deploy: Use Modal, Replicate, or your preferred cloud platform

For Frontend Developers​

  1. Start with SDKs: Use official TypeScript/JavaScript SDKs
  2. Build interfaces: Try Streamlit or Gradio for quick prototypes
  3. Add streaming: Implement streaming responses for better UX
  4. Deploy: Use Vercel, Netlify, or similar platforms with API routes

For ML Engineers​

  1. Experiment tracking: Set up Weights & Biases or MLflow
  2. Vector databases: Learn Pinecone or Weaviate for production deployments
  3. Model serving: Use RunPod, Modal, or SageMaker for model deployment
  4. Evaluation: Build evaluation pipelines with LangSmith or custom solutions

For Data Scientists​

  1. Exploration: Use Jupyter notebooks with AI APIs for data analysis
  2. RAG systems: Build knowledge bases with LlamaIndex or LangChain
  3. Evaluation: Create benchmarks and evaluation metrics
  4. Visualization: Use Streamlit for interactive data applications

API Cost Optimization​

Token Efficiency​

  • Prompt optimization: Write concise, effective prompts
  • Response limiting: Set max_tokens appropriately
  • Caching: Cache repeated requests and responses
  • Batch processing: Use batch APIs where available

Model Selection​

  • Use appropriate models: Smaller models for simple tasks (e.g., GPT-4o mini vs GPT-4)
  • Open-source alternatives: Consider Hugging Face models for cost-sensitive applications
  • Local deployment: Host models locally for high-volume applications

Architecture Patterns​

  • Hybrid approaches: Combine multiple APIs and local models
  • Preprocessing: Filter and prepare data before sending to APIs
  • Fallback strategies: Implement graceful degradation with cheaper models
  • Rate limiting: Implement proper rate limiting to avoid overage fees

Security Best Practices​

API Key Management​

  • Use environment variables for API keys
  • Implement key rotation policies
  • Use different keys for development/production
  • Monitor API key usage and set spending limits

Data Privacy​

  • Review each provider's data usage policies
  • Consider self-hosted solutions for sensitive data
  • Implement data anonymization where possible
  • Use VPCs or private endpoints for enterprise deployments

Rate Limiting & Error Handling​

  • Implement exponential backoff for retries
  • Handle rate limits gracefully
  • Log errors appropriately (without exposing sensitive data)
  • Monitor API usage and costs regularly

Next Steps: Explore Data & Models for training resources or Learning & Education for structured development learning paths.