Capacity Planning for LLM Workloads
Sizing LLM capacity needs different math than traditional workloads. The 2026 patterns for forecasting, peak handling, and reserve planning.
Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Sizing LLM capacity needs different math than traditional workloads. The 2026 patterns for forecasting, peak handling, and reserve planning.
Per-task cost, payback period, and ROI math for AI agents from a CFO's perspective. The numbers that survive board scrutiny in 2026.
ToT, GoT, and self-consistency are CoT successors. The 2026 head-to-head comparison and where each pays its compute cost back.
How chatbot architectures evolved from intent-classifier-plus-rules to fully agentic LLM pipelines, with 2026 production patterns.
What happens when the model has no good answer is the most-undervalued bot design decision. The 2026 fallback patterns that work.
Brand voice in chatbots is engineered through prompts, evaluators, and red-teaming. The 2026 patterns for getting the personality right.
Embedding models are not interchangeable. The 2026 comparison of OpenAI, BGE, Voyage, Cohere, and the dimensions that matter for production RAG.
How you chunk decides what your RAG retrieves. The 2026 chunking strategies — recursive, semantic, late, contextual — benchmarked side-by-side.
The three AI IDEs that dominate developer workflows in 2026 — benchmarked on agentic capability, codebase awareness, and developer productivity.
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