AI Infrastructure
5 posts tagged #ai-infrastructure
5 posts on AI Infrastructure, 3 of them in the Local LLM Hardware pillar, with 1 in AI Agent Architecture & Frameworks — most read: “Docker Compose vs Kubernetes for AI/ML: When to Use Which” and “Local LLM Cost vs Cloud API: Break-Even Math + Calculator”.
Technology Docker Compose vs Kubernetes for AI/ML: When to Use Which
A practical decision guide for AI teams: when Docker Compose is enough for a single GPU box, when Kubernetes is mandatory, and the cleanest migration triggers for serving and training.
AI and Machine Learning Local LLM Cost vs Cloud API: Break-Even Math + Calculator
A workload-specific break-even framework with real per-token math — hardware amortization vs. API spend — for coding, RAG, and batch workloads in 2026.
AI and Machine Learning pgvector vs Pinecone 2026: Which Vector DB Should You Use?
pgvector wins for teams already on Postgres who want simplicity and cost control; Pinecone wins for production AI apps that need managed, millisecond-scale vector search at massive scale. Your infrastructure context is the deciding factor.
Developer Tools vLLM vs Ollama: Production Power or Developer Ease?
vLLM wins for high-throughput production deployments where every token/second counts; Ollama wins for local developer workflows where setup speed and portability matter most. Pick wrong and you'll either over-engineer a side project or under-power a real API.
AI and Machine Learning DSPy vs LangChain: Prompt Optimization vs Orchestration
DSPy wins for teams who want the model to optimize its own prompts automatically; LangChain wins for teams who need fast, flexible prototyping with a massive ecosystem. The right choice depends entirely on whether you're tuning for performance or building for breadth.
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