pairwise· By Marius Bughiu · Last updated 2026-06-25
Lindy and Gumloop are both no-code platforms for building AI agents over the SaaS an ops team already runs, both connect to a thousand-plus apps, and both meter on usage credits that bundle the model cost into the bill. They diverge on the build surface and the buyer. Lindy is a chat-first, always-on assistant — you describe an agent in plain language, point it at a trigger (an email lands, a meeting ends), and it acts with judgment mid-flow. Gumloop is a visual drag-and-drop canvas — you wire nodes into a repeatable pipeline (scrape a list → extract fields → enrich → write to a sheet) that a whole team can author and run cheaply. The routing question: do you want an always-on assistant that reasons over your inbox, or a visual canvas for repeatable document and data pipelines that scales across a team?
Where Lindy wins
Always-on judgment agents, built by describing. Lindy’s Agent Builder turns a plain-language description into a running agent in minutes, and the agent fires on events without you in the loop — read this email, decide, then act. It runs primarily on Claude (Sonnet 4.5 is the default model). When the highest-value automation needs judgment mid-flow rather than a fixed if-this-then-that path, Lindy is built for it; Gumloop’s canvas leans toward deterministic pipelines you lay out in advance.
Inbox- and calendar-centric assistant work, including voice. Meeting agents that turn a call recording into a CRM update, inbound triage that drafts a routed reply and books the meeting, candidate nudges against a recruiter’s calendar, phone and voice agents — Lindy is shaped like a personal assistant that lives in your email. Gumloop is shaped like a data pipeline, and that personal-assistant surface is not its center of gravity.
Computer use for the no-API long tail. Autopilot is a cloud browser the agent operates like a human — click, fill, scroll, navigate — for the apps with no API to call. It is included on Pro and Max, and it covers automations Gumloop’s node catalog can’t reach without an integration.
Where Gumloop wins
A visual canvas you can see, version, and hand off. Gumloop builds are a node graph, not a chat transcript. For document-heavy and data-processing flows — scrape, extract, enrich, summarize, write back — an inspectable canvas beats a chat-defined agent: a colleague reads the pipeline, edits one node, and reruns it. Non-builders follow a diagram more easily than a prompt, which is why Gumloop spread inside companies as the place employees build their own automations.
Cheaper credit economics and unlimited seats. Gumloop Pro is $37/month ($29.60 billed annually) with unlimited seats and 20,000+ credits; Lindy’s entry Plus is $49.99/month and caps you at 2 inboxes. Gumloop’s bet is “every employee an agent builder” — roll building out across a whole team without a per-seat penalty, where Lindy’s plans gate you by inbox count and start higher.
Enterprise traction and governance checkboxes. Shopify, Ramp, Gusto, Samsara, Instacart, and Opendoor build on Gumloop; the Enterprise tier adds SCIM/SAML, role-based access control, audit logs, and a virtual private cloud; Benchmark led a $50M Series B in March 2026 (~$70M total raised). For a security review and a multi-team rollout, those are concrete answers rather than a roadmap.
Pricing reality
Both meter usage and fold the model cost into the meter, so compare by shape, not sticker. Gumloop prices credits per node run: every workflow run costs a base 1 credit plus node costs — a standard AI call is 2 credits, an advanced model 20, an enrichment 60 — with overage at $0.005/credit. Free includes 5,000 credits/month; Pro is $37/month for 20,000+ credits and unlimited seats; Enterprise is custom. Lindy prices an opaque usage allowance against a plan: Plus $49.99 (2 inboxes), Pro $99.99 (~3× the Plus allowance, 3 inboxes), Max $199.99 (~7×, 5 inboxes), Enterprise custom.
The crossover is team breadth versus always-on judgment. Spread agent-building across a 10-person ops team and Gumloop’s unlimited seats plus cheaper per-credit math is the lower, more legible bill — Lindy’s inbox caps and higher entry price work against a wide rollout. Run a single always-on inbox assistant that must reason on every message and Lindy’s entry price buys a build path Gumloop’s canvas doesn’t match. On both, the line item buyers under-forecast is the model-and-action tail: a 60-credit enrichment node across a list, or computer-use and voice runs, burn the meter far faster than a “send an email” mental model predicts.
Implementation effort
Lindy’s ramp is chat-then-govern: describe the agent, get it running in minutes, then keep send and write actions in draft-or-notify mode until it earns trust on a sample — autonomy without a gate sends wrong actions at machine speed. Gumloop’s ramp is design-then-budget: assemble the node graph, test on a small batch, and model the per-node credit cost before scaling a flow to thousands of rows, because a 60-credit enrichment node multiplied across a list is the bill nobody forecasts. Neither replaces your system of record — keep the CRM, ATS, or spreadsheet underneath, and give either platform a named owner so agent sprawl doesn’t outrun review.
Verdict
Pick Lindy when the job is an always-on assistant that watches an inbox or calendar and exercises judgment mid-flow — inbound triage, meeting-to-CRM, candidate nudges, voice — when you want to build by describing rather than wiring, and when you’re a solo operator or a lean team that values speed-to-first-agent over seat math. It is the chat-first, judgment-driven, Claude-native pick.
Pick Gumloop when the job is a repeatable document or data pipeline you want visible and versionable, when you want agent-building in the hands of a whole team without paying per seat, and when enterprise governance (SCIM, audit logs, VPC) and cheaper credit economics decide it. It is the visual-canvas, team-scale, lower-cost pick.
If you can’t decide, default to Gumloop: the canvas is more legible to non-builders, unlimited seats remove the per-head penalty, and the credit math is cheaper to scale across an ops team. Switch to Lindy the moment your highest-value automation stops being a fixed pipeline and becomes an always-on assistant that has to read, decide, and act on every inbound on its own.
Pick neither when you want self-hostable open source your engineers own (n8n leads there), when the work is deterministic plumbing across the widest integration catalog (Zapier), or when you need governed agents over permissioned company data with an audit trail and EU residency for a larger org (Dust).
Lindy and Gumloop are both no-code platforms for building AI agents over the SaaS an ops team already runs, both connect to a thousand-plus apps, and both meter on usage credits that bundle the model cost into the bill. They diverge on the build surface and the buyer. Lindy is a chat-first, always-on assistant — you describe an agent in plain language, point it at a trigger (an email lands, a meeting ends), and it acts with judgment mid-flow. Gumloop is a visual drag-and-drop canvas — you wire nodes into a repeatable pipeline (scrape a list → extract fields → enrich → write to a sheet) that a whole team can author and run cheaply. The routing question: do you want an always-on assistant that reasons over your inbox, or a visual canvas for repeatable document and data pipelines that scales across a team?
Where Lindy wins
Always-on judgment agents, built by describing. Lindy’s Agent Builder turns a plain-language description into a running agent in minutes, and the agent fires on events without you in the loop — read this email, decide, then act. It runs primarily on Claude (Sonnet 4.5 is the default model). When the highest-value automation needs judgment mid-flow rather than a fixed if-this-then-that path, Lindy is built for it; Gumloop’s canvas leans toward deterministic pipelines you lay out in advance.
Inbox- and calendar-centric assistant work, including voice. Meeting agents that turn a call recording into a CRM update, inbound triage that drafts a routed reply and books the meeting, candidate nudges against a recruiter’s calendar, phone and voice agents — Lindy is shaped like a personal assistant that lives in your email. Gumloop is shaped like a data pipeline, and that personal-assistant surface is not its center of gravity.
Computer use for the no-API long tail. Autopilot is a cloud browser the agent operates like a human — click, fill, scroll, navigate — for the apps with no API to call. It is included on Pro and Max, and it covers automations Gumloop’s node catalog can’t reach without an integration.
Where Gumloop wins
A visual canvas you can see, version, and hand off. Gumloop builds are a node graph, not a chat transcript. For document-heavy and data-processing flows — scrape, extract, enrich, summarize, write back — an inspectable canvas beats a chat-defined agent: a colleague reads the pipeline, edits one node, and reruns it. Non-builders follow a diagram more easily than a prompt, which is why Gumloop spread inside companies as the place employees build their own automations.
Cheaper credit economics and unlimited seats. Gumloop Pro is $37/month ($29.60 billed annually) with unlimited seats and 20,000+ credits; Lindy’s entry Plus is $49.99/month and caps you at 2 inboxes. Gumloop’s bet is “every employee an agent builder” — roll building out across a whole team without a per-seat penalty, where Lindy’s plans gate you by inbox count and start higher.
Enterprise traction and governance checkboxes. Shopify, Ramp, Gusto, Samsara, Instacart, and Opendoor build on Gumloop; the Enterprise tier adds SCIM/SAML, role-based access control, audit logs, and a virtual private cloud; Benchmark led a $50M Series B in March 2026 (~$70M total raised). For a security review and a multi-team rollout, those are concrete answers rather than a roadmap.
Pricing reality
Both meter usage and fold the model cost into the meter, so compare by shape, not sticker. Gumloop prices credits per node run: every workflow run costs a base 1 credit plus node costs — a standard AI call is 2 credits, an advanced model 20, an enrichment 60 — with overage at $0.005/credit. Free includes 5,000 credits/month; Pro is $37/month for 20,000+ credits and unlimited seats; Enterprise is custom. Lindy prices an opaque usage allowance against a plan: Plus $49.99 (2 inboxes), Pro $99.99 (~3× the Plus allowance, 3 inboxes), Max $199.99 (~7×, 5 inboxes), Enterprise custom.
The crossover is team breadth versus always-on judgment. Spread agent-building across a 10-person ops team and Gumloop’s unlimited seats plus cheaper per-credit math is the lower, more legible bill — Lindy’s inbox caps and higher entry price work against a wide rollout. Run a single always-on inbox assistant that must reason on every message and Lindy’s entry price buys a build path Gumloop’s canvas doesn’t match. On both, the line item buyers under-forecast is the model-and-action tail: a 60-credit enrichment node across a list, or computer-use and voice runs, burn the meter far faster than a “send an email” mental model predicts.
Implementation effort
Lindy’s ramp is chat-then-govern: describe the agent, get it running in minutes, then keep send and write actions in draft-or-notify mode until it earns trust on a sample — autonomy without a gate sends wrong actions at machine speed. Gumloop’s ramp is design-then-budget: assemble the node graph, test on a small batch, and model the per-node credit cost before scaling a flow to thousands of rows, because a 60-credit enrichment node multiplied across a list is the bill nobody forecasts. Neither replaces your system of record — keep the CRM, ATS, or spreadsheet underneath, and give either platform a named owner so agent sprawl doesn’t outrun review.
Verdict
Pick Lindy when the job is an always-on assistant that watches an inbox or calendar and exercises judgment mid-flow — inbound triage, meeting-to-CRM, candidate nudges, voice — when you want to build by describing rather than wiring, and when you’re a solo operator or a lean team that values speed-to-first-agent over seat math. It is the chat-first, judgment-driven, Claude-native pick.
Pick Gumloop when the job is a repeatable document or data pipeline you want visible and versionable, when you want agent-building in the hands of a whole team without paying per seat, and when enterprise governance (SCIM, audit logs, VPC) and cheaper credit economics decide it. It is the visual-canvas, team-scale, lower-cost pick.
If you can’t decide, default to Gumloop: the canvas is more legible to non-builders, unlimited seats remove the per-head penalty, and the credit math is cheaper to scale across an ops team. Switch to Lindy the moment your highest-value automation stops being a fixed pipeline and becomes an always-on assistant that has to read, decide, and act on every inbound on its own.
Pick neither when you want self-hostable open source your engineers own (n8n leads there), when the work is deterministic plumbing across the widest integration catalog (Zapier), or when you need governed agents over permissioned company data with an audit trail and EU residency for a larger org (Dust).