Is Gumloop or Relevance AI cheaper?

Gumloop starts at $37 per month (Pro, 20,000 credits included) (14 days trial). Relevance AI starts at Free (200 Actions per month); Pro from $19 per month billed annually (free plan available).

Gumloop vs Relevance AI

An independent, review-free comparison compiled by the SaaSTracker editorial team. Both products are profiled in full, and neither can pay for placement here.

The short answer

Editorial assessment

Relevance AI compared with Gumloop

Gumloop is the closer thing to a visual automation canvas: node-based flows, easier to reason about, faster for a linear pipeline like scrape, enrich, write to a sheet. Relevance AI is stronger when the unit of work is an agent that decides what to do next, delegates to other agents, and asks a human before acting. If your process is a diagram, Gumloop is the calmer purchase. If your process is a job description, Relevance AI fits better, and its tracing and approval controls are more mature.

Choose Gumloop if

Operations, revenue operations, and growth people who understand a process well enough to encode it but do not want to write and host code: Gumloop gives them agents with real tool access, a shared knowledge base, and per-run cost visibility, with no seat charge as the rest of the team starts using what they built.

Choose Relevance AI if

Revenue and operations teams that want AI agents doing repeatable research, enrichment, outreach, and triage work under supervision, and that have someone willing to treat agent building as an ongoing job rather than a one-week setup. Strongest where the work is judgment-heavy and unstructured, which is exactly where a rules-based automation tool stalls.

Side by side

13 attributes

Attribute	Gumloop	Relevance AI
Category	GTM Engineering	GTM Engineering
Starting price	$37 per month (Pro, 20,000 credits included) (14 days trial)	Free (200 Actions per month); Pro from $19 per month billed annually (free plan available)
Pricing model	Usage-based. Seats are unlimited on every plan and the meter is credits, where one credit is $0.005. Credits are consumed by model chat and reasoning tokens, tool calls (minimum one credit per successful call), compute time at roughly five credits per session-minute, and an 8 percent orchestration fee on top of those. Workflow runs bill the same way. Bring-your-own-key removes token charges but raises the orchestration fee on agent chats.	Two meters plus a plan fee. Actions are consumed one per tool run, including runs that fail or retry. Vendor Credits cover the underlying LLM and third-party model spend, passed through at wholesale with no markup, and can be bypassed entirely on paid plans by connecting your own OpenAI or Anthropic API keys. Plans set the included Actions, included Vendor Credits, number of build users, and number of projects. This structure took effect on 8 September 2025 and replaced the previous single-credit model; the Business plan was discontinued at the same time.
Free plan	No	200 Actions per month, a one-time grant of 1,000 Vendor Credits, one build user, one project
Free trial	14 days on Pro	No time-limited trial; the free plan serves that purpose
Best for	Operations, revenue operations, and growth people who understand a process well enough to encode it but do not want to write and host code: Gumloop gives them agents with real tool access, a shared knowledge base, and per-run cost visibility, with no seat charge as the rest of the team starts using what they built.	Revenue and operations teams that want AI agents doing repeatable research, enrichment, outreach, and triage work under supervision, and that have someone willing to treat agent building as an ongoing job rather than a one-week setup. Strongest where the work is judgment-heavy and unstructured, which is exactly where a rules-based automation tool stalls.
Setup time	A first useful agent in an afternoon: connect two or three apps, write instructions, test in the canvas, then invite it into a Slack channel. A production process with triggers, error handling, and evaluations is more like a week of part-time work, most of which is spent discovering the edge cases in your own data.	An hour to sign up, clone a marketplace agent, and see it run. A week or two to get a first agent doing real work against real data, most of which is spent writing instructions, connecting integrations, and discovering the edge cases where the agent is confidently wrong.
Learning curve	Low to start and moderate to do well. The canvas and the agent editor are approachable, but the skills that separate a demo from something dependable are prompt discipline, knowing when to force a step into a deterministic workflow instead of trusting the model, and reading the run log to find which node is consuming the credits.	Low to begin, steep in the middle. The visual tool builder is approachable for a non-engineer, but reliable agents require prompt discipline, structured output enforcement, and an appetite for reading traces. Multi-agent Workforces and custom API steps are a genuine technical exercise, not a no-code one.
Platforms	Web application, Slack, Microsoft Teams, Email (per-agent inbox), Hosted agent pages with custom domains, Chrome extension, CLI	Web application, REST API, Model Context Protocol (MCP) server, Slack, Microsoft Teams, Android app, Embeddable tools and agents
Compliance	SOC 2 Type II, GDPR, Zero data retention options with model providers	SOC 2 Type II, GDPR
Founded	2023	2020
Headquarters	San Francisco, California (founded in Vancouver, Canada)	Sydney, Australia
Ownership	Independent, venture-backed (legal entity AgentHub Inc.)	Independent, venture-backed (OnSearch Pty Ltd trading as Relevance AI)

Strengths and limitations

Gumloop

Strengths

Unlimited seats on every plan, so adoption across a team costs nothing extra and the builder is not rationing access.
Genuinely non-technical building surface that still reaches real systems, with roughly 300 connectors and first-class MCP support.
Per-node credit and runtime reporting in the run log, which is better cost instrumentation than most automation platforms offer.
Company Brain with permission-preserving indexing, which is what makes a shared Slack agent safe rather than a data leak waiting to happen.

Limitations

The free plan was discontinued; evaluation is now a 14-day trial on a paid plan, which is a real barrier for a cautious small buyer.
Credit consumption is hard to predict before you build. Users report a workflow going from one or two credits per run to dozens after a small change, and credits do not roll over on Pro.
The 8 percent orchestration fee sits on top of model and tool costs, and rises on bring-your-own-key agent chats, so BYOK is less of a savings lever than it first appears.
Pro concurrency is a wall, not a queue: 5 simultaneous workflow runs, then HTTP 429. Queuing is an Enterprise feature, which pushes any genuinely high-volume webhook use case upmarket.

Relevance AI

Strengths

The agent abstraction is genuinely well built: instructions, scoped tools, knowledge, approval gates, and escalation rules are all first-class rather than bolted on.
Tracing and cost visibility are better than most competitors, showing per-step model calls and per-run cost, which turns debugging from guesswork into reading a log.
The two-meter pricing separates platform cost from model cost honestly, and bring-your-own-keys removes the markup that most agent platforms quietly charge.
Approval gates and escalation are the right primitives for letting an agent touch a CRM or an inbox without an incident.

Limitations

Cost is hard to forecast. Actions are charged per tool run including failures and retries, and a multi-agent Workforce fans out into far more runs than a first-time buyer estimates.
The public pricing page shows only Enterprise as of August 2026; Free, Pro, and Team still exist but you have to dig through documentation to find their terms, which is a deliberate move away from self-serve buyers.
The Pro to Team step is punishing, roughly $19 to $234 per month for under three times the Actions, and there is no longer a Business tier in between since it was retired in September 2025.
Governance essentials are Enterprise-gated: evals, agent performance observability, audit logs, SSO, RBAC, and Salesforce, Snowflake, and Zendesk triggers are all out of reach of a self-serve buyer.

Pricing compared

Gumloop

Usage-based. Seats are unlimited on every plan and the meter is credits, where one credit is $0.005. Credits are consumed by model chat and reasoning tokens, tool calls (minimum one credit per successful call), compute time at roughly five credits per session-minute, and an 8 percent orchestration fee on top of those. Workflow runs bill the same way. Bring-your-own-key removes token charges but raises the orchestration fee on agent chats.

ProFrom $37
EnterpriseCustom

The unlimited-seat model is the genuinely unusual part. A ten-person team pays the same $37 base as one person, which inverts the economics of nearly every competing tool and makes Gumloop cheap for the pattern it is designed around: one builder, many users. The offsetting risk is that the credit meter is opaque until you have run real work through it, and the removal of the free plan means you now discover your actual cost during a 14-day paid trial rather than over a leisurely month on a free tier. For a small business, the honest budgeting approach is to assume the $37 covers exploration and light production only, and to instrument the run log early so you know which agent is eating the month.

Relevance AI

Two meters plus a plan fee. Actions are consumed one per tool run, including runs that fail or retry. Vendor Credits cover the underlying LLM and third-party model spend, passed through at wholesale with no markup, and can be bypassed entirely on paid plans by connecting your own OpenAI or Anthropic API keys. Plans set the included Actions, included Vendor Credits, number of build users, and number of projects. This structure took effect on 8 September 2025 and replaced the previous single-credit model; the Business plan was discontinued at the same time.

Free$0
Pro$19
Team$234
EnterpriseCustom

Priced against what it replaces, the Pro plan is cheap: $19 a month plus metered model spend for work that would otherwise be a contractor's afternoon. The problem is the shape of the curve above it. Team at $234 a month gives under three times the Actions for more than ten times the price, and the governance features that make agents safe to leave running (evals, observability, audit logs, SSO) are not purchasable at any published price. A small business gets real value from the free and Pro tiers for supervised, human-in-the-loop work. A company that wants agents operating unattended on customer data is buying Enterprise, and should assume a five-figure annual commitment and a procurement cycle.

Editorial verdict on each

Gumloop

Gumloop is the most credible no-code agent platform for a go-to-market team that has processes worth encoding and nobody free to write code. The unlimited-seat model is the standout commercial decision: one person builds, the whole company uses it, and the base subscription does not move. Company Brain with permission-aware indexing, skills that give the prompt layer a home, and evaluations that grade output are the parts competitors have not matched. The reasons for caution are commercial rather than technical. The free plan is gone, so evaluation now happens inside a 14-day paid trial; credits are hard to forecast, do not roll over, and carry an orchestration fee on top; and the governance features a compliance review will ask about live entirely on Enterprise. Buy it if the work you want automated genuinely needs judgment, budget for usage rather than a flat subscription, and instrument the run log from day one so you find out which agent is expensive before the invoice does.

Read the full Gumloop profile

Relevance AI

Relevance AI is one of the better-engineered agent platforms available to a small company, and the parts that matter most for production use, tracing with per-step cost, approval gates, escalation rules, a job queue with retries, and an honest two-meter bill, are more mature here than in most of its peers. The friction is commercial rather than technical. Pricing jumps from $19 to $234 per month with under three times the Actions, the Business tier that used to bridge that gap was retired in September 2025, and the governance features that make agents safe to leave running unattended (evals, observability, audit logs, SSO) are all locked behind a quote-only Enterprise plan. The public pricing page now advertises nothing else, which tells you where the company's attention has gone. Buy the Free or Pro plan for supervised, human-in-the-loop work where a person reviews output, and expect real value from it. Do not plan an unattended, customer-data-touching deployment on a self-serve plan, because the tools to prove it is behaving are not sold at that price.

Read the full Relevance AI profile

Gumloop profile last reviewed 2026-08-23; Relevance AI last reviewed 2026-08-23. Pricing is compiled from public sources and can change without notice. See our methodology.