EzyConn vs Landbot: AI Answers vs Visual Flows

Two different products wearing the same "chatbot" label

On a features page, EzyConn and Landbot look like competitors. In practice they were built to solve different problems. Landbot, founded in Barcelona and long popular with marketing teams, is a visual flow builder: you drag blocks onto a canvas (questions, buttons, conditions, integrations) and the bot walks visitors through the exact tree you designed. It replaced the web form, and it did that job brilliantly.

EzyConn is a generative answering engine: you point it at your website, help center and PDFs, it builds a retrieval index, and from that moment it composes answers to whatever customers actually ask, including the 30-50% of questions that never appear in any flow designer's plan. If you want the mechanics, our guide on training an AI chatbot on your website walks through the full process. The right choice depends almost entirely on whether your traffic needs guiding or answering.

EzyConn vs Landbot: feature matrix

Where Landbot genuinely shines

An honest comparison starts with what the other tool does better. Both platforms are genuinely no-code, so this isn't a question of who needs engineers. Landbot has three real strengths that no generative-first platform matches today.

If your bot's job is "collect name, email, budget and use case, then book a call," Landbot is arguably the best tool on the market for it. See our roundup of the best AI chatbots for lead generation for how flow-first tools stack up on that specific job.

The core difference: what happens off-script

Here is the structural limit of every flow builder. A well-designed Landbot tree covers perhaps 20-40 intents, the questions you predicted. Analysis of real support inboxes consistently shows that 30-50% of inbound questions fall outside any predicted intent list: phrasing variations, edge cases, multi-part questions, typos in another language. When those hit a scripted flow, the visitor gets a "Sorry, I didn't understand that" fallback, taps through menus, or abandons. Landbot's newer AI blocks soften this (you can drop an LLM node into a flow) but the AI only fires where you placed it, and it doesn't automatically know your product documentation.

EzyConn inverts the model. The generative answer is the default path: every question is answered from your indexed content with citations, and structure (buttons, guided prompts) is the exception you add where it helps. In practice this is why generative-first deployments report 60-80% ticket deflection while flow-only bots typically plateau at 25-40%, and the delta is exactly those unpredicted questions. For what a good safety net looks like when the AI itself is unsure, see our guide to AI chatbot fallback strategies.

Maintenance follows the same physics. A 50-node Landbot tree needs a human editor every time pricing, policy or product names change; teams commonly budget 2-4 hours a month just keeping flows accurate. EzyConn re-crawls your site on a schedule, so a help-center edit propagates to the bot without anyone opening a canvas.

Channels, handoff and integrations

On channels, Landbot leads on WhatsApp: official WhatsApp Business API, message templates, opt-in flows and broadcast campaigns make it a genuine WhatsApp marketing suite. If your customers live on WhatsApp, which is common across Spain, LATAM and India, that depth matters. EzyConn treats WhatsApp as one support surface among several (web widget, email, WhatsApp), with a single AI brain and a single conversation history across all of them.

On handoff, the priorities reverse. EzyConn ships a shared team inbox where the AI escalates with a drafted summary of the conversation, detected intent and suggested reply, the pattern we document in our chatbot human-handoff guide. Landbot can route to a human inbox too, but its inbox is lighter and clearly secondary to the flow product. Integration-wise, both connect to Google Sheets, Zapier/Make, webhooks and major CRMs; Landbot's integrations fire at flow nodes, EzyConn's fire on conversation events like escalation or lead capture.

Pricing: flat-rate vs volume tiers

As of mid-2026 (always check current pricing), Landbot offers a free tier that is genuinely useful for testing, with paid plans scaling by chat volume and channel access. Web-only plans start around €40/month, and WhatsApp plans with meaningful volume commonly land in the €200-€400+/month range before per-conversation WhatsApp fees. EzyConn uses flat-rate monthly plans (full details on our pricing page) with no per-resolution or per-chat surcharges, so a viral spike or seasonal rush doesn't change your bill.

The crossover math is simple: below ~100 conversations a month, Landbot's free tier is hard to beat. Between 500 and 5,000 conversations, flat-rate pricing usually wins on cost per conversation. Our AI chatbot pricing guide breaks down the models with worked examples at each volume band.

Who should pick which

Pick Landbot if:

• Your primary use case is lead capture, qualification or surveys: conversational forms are Landbot's home turf, with 50-70% completion rates.
• WhatsApp is your dominant channel and you want broadcasts, templates and opt-in management in one tool.
• You need tightly controlled, compliance-approved wording on every step, with zero generative variation.
• Your question volume is small and predictable enough that 20-40 scripted intents cover it.

Pick EzyConn if:

• Your bot's main job is customer support: answering real questions about your product, policies and pricing at 60-80% deflection.
• You want to be live in 15 minutes by indexing existing content, not spend days designing trees.
• Your content changes often and you don't want a standing flow-maintenance chore.
• You need a real team inbox with AI-summarized handoffs when conversations escalate.

Frequently Asked Questions

Last updated July 2, 2026. Feature and pricing details reflect publicly available information as of mid-2026. Verify current plans on each vendor's site before buying. View more guides.