Cargo 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 assessmentRelevance AI compared with Cargo
Cargo is purpose-built for revenue orchestration: routing, waterfall enrichment, and pushing scored records into a CRM, with GTM data plumbing as the whole point. Relevance AI is a general agent platform that happens to be sold heavily into GTM. A team whose problem is specifically lead routing and enrichment logic will get there faster on Cargo. A team that also wants support triage, meeting agents, and internal research from the same platform will prefer Relevance AI, at the cost of building more of the GTM logic themselves.
Choose Cargo if
Technical revenue operators and GTM engineers at funded startups and mid-market software companies who need enrichment, scoring, routing, and agent workflows to run as one governed system, and who want that logic versioned in code rather than trapped in a visual canvas.
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 | Cargo | Relevance AI |
|---|---|---|
| Category | GTM Engineering | GTM Engineering |
| Starting price | Free plan with 100 credits per month; paid plans from about $165 per month (free plan available) | Free (200 Actions per month); Pro from $19 per month billed annually (free plan available) |
| Pricing model | Usage-based credits on a subscription plan, with no per-seat charge and no feature gating between tiers. Credits are consumed by integration tasks (priced per integration), orchestration steps (roughly 1 credit per 100 steps), and storage upserts (roughly 1 credit per 1,000 upserts). Enrichment and LLM provider costs are separate, since you connect your own accounts. Prices are quoted as from figures because per-integration credit consumption varies. | 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 | 100 credits per month, community support, all features included, no SSO | 200 Actions per month, a one-time grant of 1,000 Vendor Credits, one build user, one project |
| Free trial | Free plan with 100 credits and no payment method required, plus a 14-day satisfaction guarantee on paid plans | No time-limited trial; the free plan serves that purpose |
| Best for | Technical revenue operators and GTM engineers at funded startups and mid-market software companies who need enrichment, scoring, routing, and agent workflows to run as one governed system, and who want that logic versioned in code rather than trapped in a visual canvas. | 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 working Play in a day for someone comfortable with APIs: connect a CRM, define a company model, add one enrichment step, and trigger on record change. A production deployment covering enrichment, scoring, routing, and CRM writeback more realistically takes two to four weeks, most of it spent agreeing on the data model rather than on the tool. | 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 | Moderate to steep, and honestly so. The primitives are few but they assume familiarity with data modeling, idempotency, retries, and rate limits. The visual builder lowers the entry cost but not the conceptual one; teams without a technical operator tend to stall after the first workflow. | 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, cargo-ai command line interface, TypeScript CDK, Hosted Vite apps deployed alongside a workspace | Web application, REST API, Model Context Protocol (MCP) server, Slack, Microsoft Teams, Android app, Embeddable tools and agents |
| Compliance | GDPR, SOC 2 | SOC 2 Type II, GDPR |
| Founded | 2023 | 2020 |
| Headquarters | San Francisco, California, with a team in Paris | Sydney, Australia |
| Ownership | Independent, venture-backed | Independent, venture-backed (OnSearch Pty Ltd trading as Relevance AI) |
Strengths and limitations
Cargo
Strengths
- Revenue logic can be versioned, reviewed, and deployed like software, which is a real answer to the problem of critical scoring rules living inside one person's canvas.
- Agents are steps inside workflows sharing one data model, so multi-agent handoffs stay structured instead of degrading into text passed between prompts.
- Bring-your-own credentials for enrichment and LLM providers means no data resale markup and no vendor lock-in on the data layer.
- No per-seat pricing and no feature gating between tiers, so a small technical team gets the full platform at the entry price.
Limitations
- The credit meter has three dimensions (integration tasks, orchestration steps, storage upserts), so spend is genuinely hard to forecast before a month of real usage.
- No native sequencing: there is no email or LinkedIn sequence builder, so outbound execution always requires a second tool and a handoff step.
- The built-in enrichment provider catalog is smaller than Clay's, which matters if your waterfall depends on a long tail of niche data vendors.
- The step from about 2,500 credits to about 17,000 credits is a jump from roughly $250 to roughly $1,190 a month with nothing in between.
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
Cargo
Usage-based credits on a subscription plan, with no per-seat charge and no feature gating between tiers. Credits are consumed by integration tasks (priced per integration), orchestration steps (roughly 1 credit per 100 steps), and storage upserts (roughly 1 credit per 1,000 upserts). Enrichment and LLM provider costs are separate, since you connect your own accounts. Prices are quoted as from figures because per-integration credit consumption varies.
- Free$0
- StarterFrom $165
- ProfessionalFrom $250
- EnterpriseFrom $1,190
- Premium EnterpriseFrom $3,000
Priced against the alternative of a data engineer maintaining glue scripts, Cargo is inexpensive; priced against the tools a five-person sales team actually buys, it is not an impulse purchase. The seat-free model is genuinely favorable for agencies and for teams where many people benefit from workflows one person builds, and the absence of feature gating means the Starter plan is the whole product rather than a demo. The weak spot is predictability: with credits consumed by integration calls, orchestration steps, and storage writes at once, the first two months are an estimation exercise, and the leap to the Enterprise tier arrives faster than most buyers expect once always-on plays are running.
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
Cargo
Cargo is the most convincing attempt yet to treat go-to-market logic as software rather than as a canvas somebody maintains. The primitives are well chosen, agents sit inside workflows instead of beside them, run traces and typed tools make the thing operable, and the seat-free, feature-complete pricing is a genuine kindness in a category full of gated tiers. The costs are equally clear. There is no sequencing, the enrichment catalog is narrower than Clay's, credits are metered along three axes that resist forecasting, and the jump from the $250 tier to the $1,190 tier arrives quickly once plays run continuously. The deciding question is not budget but staffing: with an engineer who wants revenue logic in version control, this is a strong buy at a price a funded small company can absorb; without one, most of what makes Cargo different is out of reach and a spreadsheet-shaped competitor will get further faster.
Read the full Cargo profileRelevance 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 profileCargo 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.