SendPulse builds Telegram chatbots. Entergram runs Telegram conversations.

What SendPulse is built for

SendPulse is an all-in-one marketing platform. Email campaigns, SMS, push, a light CRM and chatbots for Telegram, WhatsApp, Instagram, Facebook and Viber all sit in one account, which is genuinely convenient for a small marketing team that would otherwise buy four tools.

Its Telegram chatbot builder is the strong part: no-code visual flows, subscription widgets for your site, group and channel automation that can check whether a user belongs to a channel, block or unblock them, and post into a group. The free plan allows 3 chatbots and 10,000 messages a month, and paid plans start around $9.85 per month, scaling with subscriber count and bot count.

For lead capture, onboarding sequences, drip content and audience broadcast to people who opted in, this is a good product at a very low price.

Where the chatbot model stops

Everything SendPulse does on Telegram depends on someone subscribing to your bot first. That is not a limitation SendPulse chose; it is Telegram's rule. A bot cannot send the first message to a user, cannot see anything a group said before it was added, and in groups runs with privacy mode on by default, receiving only commands and direct replies unless promoted to administrator.

The practical consequence is about coverage rather than features. If your business runs on Telegram, most of your real conversation volume is people messaging your team's personal accounts: a prospect DMing a rep, a customer raising a problem in a shared project group, a partner forwarding an introduction. None of that involves your bot, so none of it appears in SendPulse at any price.

This is also why the comparison is less competitive than it looks. SendPulse is not a worse CRM than Entergram; it is a marketing tool being asked a CRM question by the search results.

• Only reaches people who subscribed to your bot
• No first outbound message, so no cold follow-up or reactivation
• No chat history from before the bot was connected
• DMs to your team's personal accounts are entirely invisible

The subscriber-count pricing trap

SendPulse prices by how many subscribers your bots have and how many bots you run. For marketing this is fair: a bigger audience is worth more. For support and sales it inverts the logic, because your cost rises with audience size while your capacity to answer stays fixed at the number of people on the team.

Communities, marketplaces and anything with a large inbound audience feel this quickly. Entergram charges per seat with unlimited chats and contacts, so the bill tracks the size of your team rather than the size of your audience. See the pricing page for current rates.

It is also worth noting that SendPulse announced pricing changes taking effect from 2 March 2026, so verify current rates directly before committing.

What an operations tool adds

Entergram connects the account, so the inbox contains DMs, groups, channels, folders and full history. On top of that sits the layer a team needs: assignment, internal comments, tickets raised from individual messages, priorities and due dates, SLA targets, and a Kanban board for working the queue.

It also handles the thing SendPulse's model cannot: broadcasting from real accounts. You import a contact list, imported columns become custom CRM fields usable as filters and merge tags, contacts are resolved by username or phone number through live Telegram search, and the send is spread across several connected accounts, each on its own proxy, with Telegram-aware pacing. Replies land in the shared inbox against the CRM record, so a campaign becomes tracked conversations rather than a one-way blast.

Automation: visual flows vs. an open protocol

SendPulse's no-code builder is a real advantage and Entergram has no visual canvas. If drag-and-drop flows are what you want, buy SendPulse and do not let a comparison page talk you out of it.

Entergram's bet is programmability: a REST API, Make.com, reminders, broadcast, and an MCP server spanning every account on a seat. A model that reads the actual conversation replaces a lot of branching logic, because it triages by meaning rather than keyword. AI inbox triage over MCP shows the pattern.