Paperchat vs Salesforce Agentforce, which is better? (2026)

Paperchat

Free plan

Paperchat is a no-code platform for building AI chatbots trained on your own data, designed so any team can launch one without an enterprise stack. You connect your files, your website URL, a sitemap, or plain text, and Paperchat turns that into a chatbot that answers from your content using retrieval. Where Agentforce assumes you already run Salesforce and bills by consumption, Paperchat needs no CRM, no ecosystem, and no per-conversation meter. The same trained bot runs on a website widget and natively across WhatsApp, Facebook Messenger, Instagram, and Telegram, and real-time human handover lets a staff member step into any live chat. Lead capture, conversation history, and multi-tenant team workspaces are included rather than tied to platform licenses. Paperchat starts on a genuinely free plan, deploys in minutes, and its paid plans scale gently with predictable pricing, which makes it a practical fit for startups, agencies, and support teams that want a capable AI chatbot without Salesforce, without complex governance setup, and without consumption-based billing surprises.

Salesforce Agentforce

Usage-based

Salesforce Agentforce is Salesforce's autonomous AI agent platform, embedded directly in the Salesforce CRM. It is built to handle service, sales, marketing, and commerce tasks as agents that can reason over your unified Salesforce data and take real actions through the platform, from updating records to triggering workflows. Because it sits inside Salesforce, it has native access to CRM data and the governance, security, and audit controls that large enterprises require, and its autonomous depth lets agents resolve multi-step tasks rather than just answer questions. Pricing is consumption-based through Flex Credits, with conversations often quoted around two dollars each, on top of the Salesforce licensing it requires. That model is powerful for enterprises already standardized on Salesforce, but it brings real complexity: setup, data modeling, and governance configuration take effort, costs can be hard to predict at scale, and it is not a self-serve option for small teams that simply want a chatbot trained on their content.