This topic surveys the functional, commercial and deployment choices that define contemporary AI chatbots and conversational assistants for customer service and personal use as of 2026-01-25. It covers core feature trends — multimodal inputs, retrieval-augmented generation (RAG), real‑time web grounding and citations, multi-agent orchestration, and privacy-enabled private models — and how those map to pricing and monetization strategies. Key platform examples illustrate those tradeoffs: Google Gemini provides multimodal models and cloud APIs (Google AI Studio, Vertex AI) for developers and enterprises; Perplexity AI emphasizes real-time, web-grounded answers with citations and developer APIs; IBM watsonx Assistant focuses on enterprise virtual agents and no-code/developer orchestration; Cohere supplies private, customizable LLMs, embeddings and retrieval for business use cases. No-code builders (Anakin.ai, WebTalkBot) and workspace-integrated assistants (Notion) lower integration friction for document Q&A and automation, while specialized agents (HelloAI for Shopify, Katara for Discord) show vertical monetization tied to conversions and community engagement. Monetization and pricing patterns include usage-based API billing, tiered subscriptions, per-seat or per-bot enterprise licensing, transaction- or conversion-based fees for commerce bots, and premium features (human escalation, extended context, private hosting). Deployment choices — cloud inference, fine‑tuning vs. retrieval pipelines, and on‑device or OS‑level assistants (e.g., Siri redesign trends) — affect cost, latency and data controls. Evaluation priorities are accuracy, grounding/citation quality, latency, cost-per-query and conversion metrics for customer-service bots. Understanding these technical and commercial dimensions helps teams choose between turnkey no-code options, managed enterprise platforms, or custom stacks built on model APIs and platforms.