Gumloop Pricing in 2026: Pro Plan, Credits, and Lindy and Relevance AI Compared

Gumloop's pricing page lists two plans. Pro is the self-serve plan, sold from $37/month after a 14-day free trial; Enterprise is quoted by sales. The page shows no free plan: the structured data on the page lists a $0 offer, but its description is "Free 14-day trial available", and Gumloop's credits doc says the trial requires a card, is one-time per customer, and rolls into a paid Pro subscription when it ends unless you cancel first.

What Pro includes beyond credits. 35+ models, bring-your-own API keys, Company Brain (a company knowledge base agents can search), GitHub skill sync, shared credentials, email support, agent-scoped connector policies, MCP server proxying, and the Gumloop MCP and CLI. Enterprise adds custom model proxy support, dedicated Slack support, an optional embedded Gumloop expert, an optional virtual private cloud, org-wide connector policies, AI spend insights, role-based access control, SCIM/SAML, an admin dashboard, audit logs, custom data retention, data exports and workflow queuing.

The "starts at" wording. $37 is the entry price for 20,000 monthly credits. Spending beyond that is pay-as-you-go at the credit list price, not a jump to a bigger tier, because there is no tier between Pro and Enterprise on the pricing page.

Gumloop's docs call agent credit costs variable: the same agent might use 2 credits for a quick question and 200 for a deep research task. A run's total is the sum of four lines, and the chat header shows the running cost live, with a per-type breakdown one click away.

Model tokens are passed through at list price. Divide the model cost by $0.005: a $0.03 model call is 6 credits. Longer conversations cost more per message because every message carries the earlier context, and one message can trigger several model steps (pick a tool, read the result, answer), each billed separately.

Tool calls cost at least 1 credit. Reading and writing to apps like Slack, Google Sheets and Gmail costs exactly that 1 credit. Tools that enrich or fetch outside data add their own charge on top, listed on each connector's catalog page. Failed calls are not charged.

Compute only counts while the agent works. Waiting for your reply is free. The Code Sandbox and the agent browser have no per-call charge; their running time is billed as compute.

The orchestration fee applies to everything. Gumloop's own example: a chat that spends 400 credits on chat and reasoning, 10 on compute and 15 on tool calls pays a 34-credit fee (8% of 425, rounded up), for 459 credits in total, which is $2.30 at the list price.

Bring your own key changes the split, not the meter. With your own model API key (Anthropic, OpenAI and so on), token credits drop to zero but the orchestration fee rises from 8% to 16%, calculated on the full list cost the task would have had without your key. With your own key for MCP tool calls, the tool charge falls to a minimum of 1 credit per call.

What $37 buys at list price. 20,000 credits at $0.005 is $100 of list-priced usage; the 12,600 bonus credits are where the gap comes from. At Gumloop's own 459-credit example, Pro covers about 43 such chats a month before overage.

Credits do not roll over. The 20,000 credits are a monthly grant that resets each billing period; you get the full amount again and do not bank what you did not use. Only Enterprise plans roll unused credits over.

Running out mid-month. Agents stop and you are prompted to turn on pay-as-you-go, which is off by default. Once enabled, usage bills at $0.005 per credit. Pro overage is always capped: the default ceiling is 1,000,000 overage credits per billing period ($5,000), you can set a lower cap on the Usage and Limits page, and agents stop when the cap is reached.

Concurrency is part of the price. Pro allows 25 agent interactions running at once across the organization. At the limit, Pro rejects new runs: the API returns HTTP 429, Slack users see an error, and a scheduled or event trigger that fires at the limit is skipped. Enterprise queues them instead. Plan for this if one agent fans out into many subagents (Gumloop caps those at 10 per task).

Cancelling. An admin opens Manage Subscription on the Usage and Limits page, which goes to a Stripe billing portal. The plan stays active to the end of the paid period and credits remain usable until then; cancelling during the trial stops the card from being charged.

Where the bill grows. Three habits drive Gumloop spend more than the plan price: frontier models on long chats (token credits), agents that poll on a short schedule (compute and tool credits on every run, even when nothing changed), and paid data connectors whose own charge is added to the 1-credit base. The X section below puts numbers on the second one.

All three sell a credit-style meter on top of a monthly plan, but the unit means something different on each. Prices below are copied from each vendor's own pricing page or pricing doc, in US dollars, as each one shows them.

Lindy. Lindy's pricing page sells one Team plan with a credit slider, from 3,000 credits for $29.99/month up to 1,750,000 credits for $9,999.50/month, and every active user in the workspace is billed, from $29.99/month for 3,000 credits each, with all seats feeding one shared pool. Lindy sizes work in credits: everyday asks use 2 to 250, deep work 250 to 1,000, and big builds 1,000 to 2,500. When the pool runs low, Lindy pauses credit-using actions until the next cycle instead of billing overage; admins can top up at $10 per 1,000 credits. Lindy's docs describe the plans as Plus, Pro and Max with current prices in Settings, so the slider on the pricing page is the figure to trust.

Relevance AI. The public pricing page now shows only an Enterprise card with a Talk to sales button. Self-serve prices are in Relevance AI's pricing doc: Pro is $19/month billed annually or $29 billed monthly, with 2,500 Actions a month, $20 of Vendor Credits a month (the feature table states it as 10,000 Vendor Credits), 2 build users and 1 project; Team is $234/month annually or $349 monthly, with 7,000 Actions, $70 of Vendor Credits, 5 build users, 45 end users and rollover. An Action is counted each time an agent runs a tool. Vendor Credits cover model and tool usage passed through at wholesale with no markup, roll over while you stay subscribed, and are not used when you bring your own keys. The Free plan is retired for new signups and existing accounts.

Reading the units side by side. Gumloop meters everything, model tokens included, in one currency. Relevance AI splits the bill: Actions price the agent's tool steps, and Vendor Credits price the models underneath, which is why a tool-heavy agent can run out of Actions while Vendor Credits pile up. Lindy prices finished work, so a single credit does not map to a token or a tool call.

A few agents in chat, light use. Lindy's $50 of free credits (no card, 7 days) and Gumloop's 14-day trial (card required) are enough to measure a real week. Relevance AI has no free start, so test on Pro monthly at $29 before committing to the annual $19 rate.

Tool-heavy agents (many connector calls, short answers). On Gumloop a plain connector call is 1 credit ($0.005). On Relevance AI each tool run is an Action: 2,500 are included on Pro, and extra Actions are $80 per 1,000 ($0.08 each). Count your agent's tool steps per run before choosing.

Model-heavy agents (long research, big context). The token cost is passed through on all three in some form. Gumloop and Relevance AI both let you bring your own model keys; on Gumloop that doubles the orchestration fee to 16%, while on Relevance AI it removes the Vendor Credits charge.

A team that shares one budget. Lindy and Relevance AI both pool usage across the workspace or organization; Gumloop's Pro also shares one pool of credits across teammates and teams. Lindy bills per active seat, Gumloop lists unlimited seats on Pro, and Relevance AI caps build users per plan (2 on Pro, 5 on Team).

Agents that run on a schedule all day. This is where the meters diverge most, and it is the usual shape of an X monitoring agent. The next two sections measure it.

We read 919 posts from X: the three official accounts' timelines and four keyword searches (one per product, plus one combining the three names with cost words). After removing the official accounts' own posts and 72 copy-pasted duplicates, 444 posts named at least one of the products, 354 of them from the unfiltered brand searches.

Hardly anyone on X talks about the price. Only 11.0% of the 354 brand-search posts used any cost word (credits, price, plan, cost, expensive, subscription or a dollar sign): 13.4% for Gumloop, 10.7% for Lindy and 6.1% for Relevance AI. Across all 444 posts, 14 placed a dollar figure right after a product's name, 7 of those were salaries or funding amounts in thousands or millions, and none quoted a current list price ($37 for Gumloop, $29.99 for Lindy, $19, $29, $234 or $349 for Relevance AI). Price research for these tools has to start at the vendor's page, which is why this page leads with it.

Relevance AI lives in tool lists. 66.7% of posts naming it were lists of four or more AI tools, and 58.6% also named n8n. Gumloop is discussed more on its own, but a third of its posts still name n8n (33.1%), and it has the highest share of MCP mentions (10.2%). Lindy's conversation is the most self-contained: 12.9% tool lists and no MCP mentions in the sample.

The official accounts rarely post about price either. @gumloop's last 100 posts spanned 514 days (45 reposts) and 4 of 55 original posts used a cost word; @getlindy's 100 spanned 380 days with 6 of 65. @RelevanceAI_ returned only 40 posts across 321 days, and its newest was 230 days old at collection, so its X account is not where its pricing changes are announced.

A common agent on all three platforms watches X: brand mentions, a competitor's launches, replies that need an answer. There are three ways to wire it, and they cost very different amounts because two meters run at once, the agent platform's and the X data source's.

Option 1, Gumloop's own X connector. Gumloop lists an X (Twitter) MCP connector that signs in with your X account (no X developer account needed) and can search posts, read timelines, look up users and post. Its connector page says each action uses credits, "typically 5 per item for read operations, 10 for posting", and does not define an item. If an item is one post, reading 3,000 posts a month is 15,000 credits, $75 at the $0.005 list price and 75% of Pro's monthly credits, before the 1-credit call base and the 8% fee.

Option 2, a custom MCP server. Gumloop connects any remote MCP server over HTTPS (streamable HTTP or SSE): Customize, Connectors, Add Custom MCP, paste the URL, then choose API Key / Credentials and paste a key, which Gumloop sends as Authorization: Bearer <key>. Use https://mcp.twitterapi.io/mcp with your TwitterAPI.io key, then add the connector to your agent. Lindy (Integrations, Add, MCP, with the key under Advanced as a bearer token) and Relevance AI (agent Tools tab, Add MCP, Connect my own) take the same URL. The server's 12 tools search posts and read profiles, timelines, replies, followers and trends; each call is billed like the endpoint behind it, per tweet or profile returned. Gumloop adds at least 1 credit per successful tool call plus the model tokens to read the results.

Option 3, a script plus a webhook trigger. Gumloop agents can have a webhook trigger: a unique URL that starts the agent with the POSTed JSON as its input, accepts up to 100 requests per minute, and replies 200 at once while the agent runs in the background. The script below checks X on a schedule and POSTs only when there are new posts, so the agent never runs on an empty check. Lindy and Relevance AI have their own trigger options (Relevance AI's plans include building your own triggers); check their trigger docs for an inbound webhook before reusing the script there.

Why Option 3 matters for scheduled agents. Gumloop's scheduled triggers can fire every minute, and every run pays at least 1 compute credit and 1 credit for the X tool call before any model tokens or the 8% fee. A month of minute-by-minute checks is 43,200 runs:

The X data bill. TwitterAPI.io charges per result returned: tweets are $0.15 per 1,000 (15 credits per tweet, with a 15-credit minimum when a request returns nothing), profiles $0.18 per 1,000. X's own pay-per-usage rate card on docs.x.com/x-api/getting-started/pricing charges $0.005 per post read and $0.010 per user read, which is $5.00 per 1,000 posts, about 33 times more per 1,000 tweets, and caps pay-per-usage at 3 million post reads a month. Reads need no X account.

Sign-up gives up to $1 of free credits (at most 100,000 credits, no card, one per person or network, expiring after 31 days) for new accounts that pass the automatic bot check; that covers about 6,600 tweets. Purchased credits never expire, and paid plans are listed on the pricing page.

If the agent should post, not just read. Writes run through an authenticated session: one /twitter/user_login_v2 login ($0.10, with a proxy you supply) returns login_cookies, and each post, like, retweet or follow through /twitter/create_tweet_v2 and its siblings is 60 credits ($0.0006), billed per HTTP 200 call. The call acts as that account, so X can rate-limit, lock or suspend it; keep a human pace, and expect to log in again when the session expires.

Question. When people on X talk about Gumloop, Lindy and Relevance AI, how often is it about cost, which other tools come up, and how often is MCP mentioned? And what did the data cost on each X rate card?

Accounts and queries. The official accounts @gumloop, @getlindy and @RelevanceAI_ (each confirmed with /twitter/user/info) were read with /twitter/user/last_tweets. Four searches ran on /twitter/tweet/advanced_search with queryType Latest: gumloop -is:retweet; ("lindy ai" OR "lindy.ai" OR @getlindy) -is:retweet; ("relevance ai" OR relevanceai OR @RelevanceAI_) -is:retweet; and the three names combined with cost words.

Window and sample. The most recent matching posts at collection time, collected in 2026: 919 tweets in 52 requests. Posts were deduplicated by id and by copy-pasted text, the official accounts' own posts were excluded from the search analysis, and each post was assigned to the products named in it. Topic shares use only the three unfiltered brand searches, so the cost-word search cannot inflate them. Brand searches covered 118 days for Gumloop, 76 for Lindy and 117 for Relevance AI.

What it cannot show. Keyword rules miss sarcasm, other languages and prices written as words; "lindy" and "relevance ai" were matched as phrases, which drops some genuine mentions. A recent-posts sample reflects the current conversation, not a year. It says nothing about what customers actually spend, and the per-run costs above use published minimums, not measured runs.

Privacy. Aggregates only. Search results come from private individuals, so raw pages were not kept in the repository and no individual is named; only the three organization accounts are.