Clay 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 Clay
Different jobs that get compared constantly. Clay is a spreadsheet with waterfall enrichment underneath and is unmatched for building and enriching a prospect list from dozens of data providers. Relevance AI has no comparable data waterfall and expects you to bring or buy the data. The common pattern is both: Clay assembles and enriches the list, Relevance AI runs the agents that research, draft, and act on it. Buying Relevance AI expecting Clay's data coverage is the most common mismatch in this category.
Choose Clay if
GTM engineers and data-savvy teams building automated, multi-provider enrichment and personalization pipelines.
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 | Clay | Relevance AI |
|---|---|---|
| Category | Data | GTM Engineering |
| Starting price | Free plan; paid from $149/mo (free plan available) | Free (200 Actions per month); Pro from $19 per month billed annually (free plan available) |
| Pricing model | Credit-based monthly tiers; provider calls and Claygent runs consume credits (only on successful hits for waterfalls). Feature gates (CRM write-back, webhooks) sit at tier boundaries. | 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/month, core table features. | 200 Actions per month, a one-time grant of 1,000 Vendor Credits, one build user, one project |
| Free trial | 14 days (Pro features) | No time-limited trial; the free plan serves that purpose |
| Best for | GTM engineers and data-savvy teams building automated, multi-provider enrichment and personalization pipelines. | 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 | First enriched table in an hour via templates; a production pipeline (sources, then waterfalls, then scoring, then delivery) typically takes 2-4 weeks to harden. | 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 | The steepest in this report, genuinely a skill. Templates, the academy, and a large creator ecosystem (courses, agencies) flatten it substantially. | 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 app, Chrome extension, REST API | Web application, REST API, Model Context Protocol (MCP) server, Slack, Microsoft Teams, Android app, Embeddable tools and agents |
| Compliance | SOC 2 Type II, GDPR program | SOC 2 Type II, GDPR |
| Founded | 2017 | 2020 |
| Headquarters | New York City, US | Sydney, Australia |
| Ownership | Venture-backed (private) | Independent, venture-backed (OnSearch Pty Ltd trading as Relevance AI) |
Strengths and limitations
Clay
Strengths
- Waterfall coverage decisively beats any single data provider.
- Claygent turns open-web research into a scalable, auditable pipeline step.
- Deep native integrations across the modern outbound stack (sequencers, CRMs, signals).
- Template/creator ecosystem compounds, proven workflows are one click away.
Limitations
- Real learning curve, tables, waterfalls, and prompt design reward (effectively require) a technical operator.
- Credit economics are powerful but unforgiving without active management.
- Not a proprietary data source; quality ceilings are its providers'.
- Enterprise governance (SSO, roles, audit) only matures at top tiers.
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
Clay
Credit-based monthly tiers; provider calls and Claygent runs consume credits (only on successful hits for waterfalls). Feature gates (CRM write-back, webhooks) sit at tier boundaries.
- Free$0
- Starter$149
- Explorer$349
- Pro$800
- EnterpriseCustom
Clay's effective price is workflow-dependent: well-designed tables deliver coverage and personalization no single vendor matches at any price, while naive configurations burn credits alarmingly. Teams treating credit design as part of the craft consistently report it as the stack's highest-ROI line item.
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
Clay
MomentumClay is the most consequential product in this report: it moved the center of outbound gravity from databases and sequencers to the orchestration layer between them, and its valuation sprint reflects substance, not froth. The costs are honest, a real learning curve and credit economics that punish sloppiness, but teams that invest in the craft get coverage, research, and personalization nothing else assembles. If your outbound has an engineer, this is their instrument.
Read the full Clay 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 profileClay profile last reviewed 2026-08-22; Relevance AI last reviewed 2026-08-23. Pricing is compiled from public sources and can change without notice. See our methodology.