#ai-infrastructure
5 posts tagged with #ai-infrastructure
Every article below is hand-written, technically reviewed, and focused on ai-infrastructure. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
Technology Docker Compose vs Kubernetes for AI/ML [2026]: Use Which?
A practical 2026 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: 2026 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 Actually 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 2026: 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 2026: Which LLM Framework Actually Wins?
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.