Gumloop vs Relevance AI: Which is best for you?
Relevance AI builds AI agents for GTM teams. Gumloop is a platform for building, deploying, and governing AI agents across your entire organization.
“Gumloop turned our sales and marketing teams into builders, and saved us thousands of hours of manual work.”
What Gumloop does that Relevance AI can't
Govern agents (even non-Gumloop ones)
Live, hosted, and interactive artifacts
Create triggers in plain language
Skills that run code and improve themselves
White-labeled Slack agents for every team
Stop agents before they touch sensitive data
Gumloop vs. Relevance AI
Both Gumloop and Relevance AI have agents. Here's how they differ when deployed to your whole organization.
Gumloop |  Relevance AI | |
|---|---|---|
Platform and ease of use | ||
| Deterministic workflows | ||
| Multi-agent orchestration | ||
| White-labeled Slack agents | ||
| Multiple distinct Slack agents per workspace | ||
| Email your agents | ||
| Host agents on web pages | ||
AI capabilities | ||
| Agents write and run their own code | ||
| Agents produce hosted, shareable files (artifacts) | ||
| Agents create apps with live data | ||
| Skills with executable code and self-improvement | ||
| Multiple model providers (Anthropic, OpenAI, Google, DeepSeek, etc.) | ||
| Bring your own key (BYOK) for models | ||
| Continuous web monitoring | ||
| Conversational analytics agent | ||
| Phone and meeting agents | ||
Pricing | ||
| Major LLM models included in subscription | ||
| Premium enrichment tools included (Apollo, Exa, Firecrawl, Semrush, etc.) | ||
| Usage scales without per-seat fees | ||
Security, compliance, and governance | ||
| SOC 2 Type II | ||
| SSO/SAML | ||
| Role-based access control (RBAC) | ||
| SCIM user provisioning | ||
| Audit logs | ||
| AI proxy routing | ||
| VPC / private deployment | ||
| Single-tenant isolation | ||
| Govern agents on and off the platform | ||
| Runtime tool-call policies (block/flag before execution) | ||
| Custom roles with per-user restrictions and caps | ||
| Per-step audit of every agent action and tool call | ||
| Zero Data Retention (ZDR) / incognito mode | ||
| In-platform real-time activity dashboard | ||
| Org-wide AI model/provider allow/deny lists | ||
| Org-wide fallback model defaults | ||
| Automated PII redaction | ||
| On-site enablement (on enterprise) |
Agents that create and host live, shareable apps
Ask Relevance AI for a dashboard and it writes the code, then hands you a checklist. It's on you to set up hosting, configure credentials, and deploy. Gumloop agents build the artifact and host it. Get a live, shareable page, dashboard, or app with credentialed access to your real data, in one prompt.
A full policy engine
Relevance AI's RBAC controls who can see, edit, and run agents. Gumloop adds the layer enterprises actually ask about: control over what agents are allowed to do. Block sensitive tool calls before they run, set org-wide model allow/deny lists and fallbacks, restrict users with custom roles, and flip on incognito mode so data is never stored at all.
Control every agent and deploy in your own cloud
Both Gumloop and Relevance AI support SOC 2 Type II, SSO, RBAC, and audit logs. But Gumloop also monitors and controls data use across every AI agent in your organization (even ones built outside Gumloop) and can be deployed in your own VPC. Relevance AI governs only its own agents and runs multi-tenant only, with single-tenant still on its roadmap.
Gumloop vs. Relevance AI
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