# Botpress Developer-first chatbot and conversational AI agent builder ## About Botpress Botpress is a visual agent builder with drag-and-drop flows, an autonomous LLM engine, and multi-channel deployment to web and messaging apps. It targets developers building customer-facing chatbots. Sistava takes a different approach: instead of building conversational bots, you hire AI employees that handle real business operations with team coordination, task boards, file management, and durable execution. ## Platform details - Pricing: Free (pay-as-you-go), then $89/mo (Plus), $495/mo (Team). AI tokens billed separately. - Founded: 2017, Quebec City, Canada - Funding: $40M raised ($25M Series B in 2025) - Last reviewed: 2026-03-22 ## Official website - [Visit Botpress](https://botpress.com) ## What does Botpress actually do? Botpress is an end-to-end AI agent platform focused on chat experiences across web, WhatsApp, Slack, and other messaging surfaces. The platform combines a visual flow builder, language model orchestration, vector storage for retrieval-augmented answers, and a runtime that handles user sessions, escalations, and human handoff. The product spans both no-code builders and developer extensions. Non-technical teams build through the studio interface; engineers can drop into the SDK to write custom actions, integrate proprietary systems, or compose multi-agent flows. WhatsApp support and whitelabel webchat are bundled into the Plus plan. Where Botpress sits in the category is squarely on the chat side of conversational AI. It is not a voice-first platform like Bland or Retell, and it is not a workforce-style product like Sistava that treats an agent as an employee working across many channels. For teams whose customer surface is chat-shaped, Botpress is one of the most mature dedicated options. ## How much does Botpress cost? Botpress charges $89 per month for Plus and $495 per month for Team, both on top of AI Spend, with a permanent Free tier limited to 100 conversations per month, 1 seat, and 3 AI agents. The pricing changed in May to charge per conversation, defined as any exchange with at least two end-user messages, rather than per incoming message. Plus includes unlimited AI agents, WhatsApp, and whitelabel webchat. Team adds higher seat counts and shared workspace features. Both plans pass through the underlying LLM cost as AI Spend rather than marking it up, which is unusual in the chatbot category and helps high-volume teams keep the unit economics transparent. Compared to workforce platforms like Sistava that bundle model cost into a credit allowance, Botpress separates the platform fee from the AI usage, which makes forecasting either easier or harder depending on how predictable conversation volume is. For chat-only deployments with steady traffic, the split is clean; for variable workloads with mixed channels, a credit-based platform tends to flatten the bill more. ## When does Botpress beat the alternatives? Botpress wins when the buyer wants a serious chat-only deployment with custom branding, WhatsApp integration, and the ability to handle real volume on one platform. The combination of visual builder for fast iteration, SDK for custom logic, and pass-through AI Spend is hard to beat when chat is the entire problem. Multi-channel chat is the second strength. WhatsApp, webchat, Slack, and other surfaces are first-class in the platform, and the same agent definition can be exposed across all of them. Teams running customer support, lead qualification, or self-service that lives in messaging apps get a coherent product surface. Botpress loses when the workflow involves voice calls, email composition, or open-ended research outside of chat. Workforce platforms like Sistava treat those as the same job assigned to an employee; Botpress treats them as out of scope. For chat-first teams the focus is a strength, for cross-channel teams it is a constraint. ## Where does Botpress fall short? Botpress is chat-shaped, and any work that does not fit a chat session sits outside the platform. There is no native voice agent, no email composer, and no surface for running open-ended research tasks. Teams who want one product to handle customer conversations, sales outreach, and back-office work need to add other tools. The second gap is the AI Spend separation. Pass-through pricing is honest, but it puts the burden of model cost forecasting on the buyer. Heavy retrieval, long contexts, or expensive models can push the AI Spend above the platform fee quickly, and teams without strong usage instrumentation end up surprised by the LLM bill more than the Botpress bill. Third, the visual builder, while mature, still requires real design work for non-trivial flows. Tree-shaped chat experiences scale to thousands of nodes for complex deployments. Workforce platforms like Sistava push that complexity inside the agent and surface a simpler task interface, which is a different trade-off than what Botpress offers. ## How does Botpress handle the chat experience end to end? Botpress treats chat as a first-class product surface rather than a side feature. The platform handles user sessions, conversation state, fallback to humans, knowledge-base lookup, and channel-specific quirks like WhatsApp template messages, all inside one runtime. The studio interface lets a non-developer iterate on the flow without redeploying. Retrieval is built in. Documents uploaded to the knowledge base are vectorized and made available to the agent for grounded answers, which is the table-stakes pattern for customer-support and self-service chat. The Free tier even includes a 100MB vector storage budget, which is enough to validate an early deployment. Workforce platforms like Sistava draw the line elsewhere: chat is one of several channels an employee uses, and the unit of work is a task rather than a conversation. Buyers choosing between them are really choosing between conversation-shaped work (Botpress) and employee-shaped work (Sistava). Both fit different problems. ## Comparison - [Compare Botpress with Sistava](/en/compare/agent-builders/botpress) — See the two platforms side by side when you are ready to evaluate them.