QX Labs vs Clay: Data Enrichment and GTM at Scale
Clay is a best-in-class GTM enrichment tool. QX Labs runs enrichment at scale too, plus agents, flows, and grounded knowledge in one workspace. How to choose.
The short answer: Clay is a best-in-class GTM data-enrichment tool, a spreadsheet-style workspace that runs "waterfall" enrichment across 150+ data providers and AI research columns to build and enrich prospect lists. QX Labs is a broader AI agent and automation platform that offers the same spreadsheet-at-scale model in its Grids, but that same workspace also runs Flows, conversational Agents, grounded Knowledge, and 1,000+ app actions. Enrichment is one job among many, not the whole product. If pure outbound enrichment is the whole job, Clay is purpose-built and excellent at it. If you want enrichment plus the rest of your AI work in one connected workspace, QX is the better fit.
This is a bottom-of-funnel comparison, so we'll be direct about where QX wins. Clay is a genuinely outstanding product, especially for outbound teams, and we'll be accurate about what it's great at and when it's the better choice.
Key takeaways
- Clay is the enrichment specialist. Its signature is waterfall enrichment: it tries data providers in sequence until one returns a verified email, phone, or firmographic, which pushes match rates well above any single source. For building and enriching prospect lists, it's one of the best tools available.
- QX is a full agent + automation platform. Grids give you the same run-work-across-thousands-of-rows model, but the same workspace also runs Agents, Flows, and Knowledge Vaults, so enrichment is one job among many, not the whole product.
- Scope is the real difference. Clay's depth is concentrated on GTM data. QX trades some of that provider-by-provider enrichment specialism for breadth: research, scoring, outreach, document work, internal-knowledge answers, and triggered automations across your stack.
- Both run "at scale." Clay runs enrichment down a table; QX Grids run an agent, integration, or logic down a column across thousands of rows in parallel, with traceable per-cell results.
- Pick by the shape of the job. Choose Clay if the job is pure outbound enrichment and prospecting. Choose QX if you want enrichment in the same place as the rest of your AI work, grounded in your own data, talked to from Slack or email.
What is Clay, really?
Clay is a GTM data-enrichment and prospecting platform built around a familiar spreadsheet metaphor. You start with a table of records (companies or people) and add enrichment columns that fill in data for every row. Its defining feature is waterfall enrichment: instead of trusting one data vendor, Clay tries providers in an ordered sequence ("what is this person's work email?"), stops at the first valid match, and only bills for the successful lookup. Because it connects 150+ third-party providers (names like Prospeo, Dropcontact, Hunter, People Data Labs, Apollo, and Lusha), coverage climbs far above any single source; Clay reports email find rates in the 80–95% range versus 50–60% for single-tool lookups.
Clay also includes Claygent, an AI research agent that pulls unstructured detail from public sources (job listings, press releases, company pages) via natural-language prompts, plus a basic sequencer and integrations to outbound tools like Instantly, Smartlead, and Lemlist. It's deeply popular with outbound and growth teams for exactly this reason: it turns list-building and enrichment into a fast, programmable, high-coverage workflow.
Clay is the specialist here. Its provider coverage and waterfall logic are best-in-class, and if your job is to build, enrich, and verify prospect lists for outbound, it's hard to beat.
What is QX Labs?
QX Labs is an AI agent and automation platform. The premise: your next hire is an AI agent. Where a chatbot only answers and rigid automation only follows rules, QX understands context, uses your real tools, and completes the work.
It's built from a few primitives that compose:
- Grids: a spreadsheet-on-steroids where every column can run an agent, an integration, or logic across hundreds or thousands of rows in parallel. This is the direct analogue to Clay's model: configure a column once (often by describing it in plain English), then run it down the whole list. Columns can do web research, people/contact lookups (e.g. Apollo, RocketReach), CRM reads/writes, scoring against a rubric, email drafting, PDF extraction, or query your private Knowledge. You see estimated credit costs and can validate on a sample before running the full set.
- Agents: autonomous AI co-workers you brief in plain English. They read context, decide which connected tools to use, take action, and report back, building institutional memory so they get more useful over time. You can talk to them from Slack, Microsoft Teams, WhatsApp, or email, not only in a web app.
- Flows: multi-step workflows that mix deterministic nodes (read a file, write a record, send an email, call an API) with agentic nodes (summarise, classify, decide, draft), triggered by an event, a schedule, or on demand, with guardrails and human-in-the-loop approvals.
- Knowledge Vaults: your internal docs, indexed and continuously synced, so agents and grids answer grounded in your real data, with citations.
- 1,000+ integrations: including a deep long tail of research and enrichment APIs, so agents act with the same reach your team has.
The key point for this comparison: enrichment in QX happens inside a Grid, and that same workspace is where you also build agents, run flows, and ground answers in your own knowledge. Enrichment is one column type, not the entire product.
QX Labs vs Clay: feature comparison
| Capability | Clay | QX Labs |
|---|---|---|
| Enrichment depth | Best-in-class: waterfall across 150+ data providers; very high match rates | Strong: contact/firmographic enrichment via connected providers + web research, inside a Grid |
| Run at scale | Enrichment down a table, thousands of rows | Grids run agents/integrations/logic across thousands of rows in parallel |
| Beyond enrichment | Focused on GTM list-building, enrichment, basic sequencing | Research, scoring, outreach, document extraction, CRM hygiene, and internal-knowledge answers |
| Agents | Claygent (AI research agent for columns) | Full Agents: briefed in plain English, take action across your stack, build memory |
| Flows / automation | Triggers and CRM/outbound integrations | Flows: event/scheduled/on-demand pipelines mixing agentic + deterministic steps, with guardrails |
| Grounding in internal knowledge | Not the focus (external data marketplace) | Knowledge Vaults: indexed, synced, cited answers from your own docs |
| Where you operate it | Web app | Web app, Slack, Teams, WhatsApp, email, API |
| Pricing shape | Dual credits: Data Credits (the data) + Actions (platform work); tiered plans | Usage-based credits, workspace-wide, no per-seat charge; all features on every plan |
Clay figures as publicly listed at time of writing; check current details on each vendor's site.
How does pricing compare?
The two price differently because they're solving differently shaped problems.
Clay uses a dual-credit system introduced in its March 2026 overhaul: Data Credits pay for the data itself (an email, a phone number, a company detail sourced from one of its 150+ partners), while Actions pay for the platform work (routing the request, calling the provider, running the workflow). If an enrichment returns no result, you aren't charged. Self-serve plans run Free (100 Data Credits, 500 Actions, unlimited seats), Launch from $185/month (2,500 Data Credits, 15,000 Actions), and Growth from $495/month (6,000 Data Credits, 40,000 Actions), with custom Enterprise pricing above that. This structure is well-suited to enrichment, where the cost of third-party data is the main variable.
QX uses a single pool of usage-based credits that powers everything: chatting, building, and running agents, grids, and flows. Credits are workspace-wide and shared across the org, so you don't pay per seat, and estimated costs are shown before you run a grid or flow. All features are on every plan (Agents, Grids, Flows, Knowledge, and integrations on both Free and Pro), with tiers differing only by credit allowance and seat count. Because pricing can change, the safest source of truth is the pricing page.
The practical read: Clay's model optimises the economics of buying enrichment data at volume. QX's single-credit model optimises for running many kinds of AI work from one balance, without per-seat fees as you roll it out across a team.
Where QX Labs wins
Breadth in one workspace. Enrichment is one job. With QX, the same place that enriches a list also scores it against your ideal-customer rubric, drafts personalised outreach per row, extracts fields from uploaded PDFs, and answers questions from your internal Knowledge, without exporting to a second tool.
Agents you can talk to. Beyond a research column, QX Agents are co-workers you brief once and delegate to from Slack, Teams, WhatsApp, or email. They take the follow-up action, not just return a cell value, and they accumulate institutional memory the more you use them.
Triggered, guardrailed automation. A Flow can watch for a new CRM record, enrich and score it, branch on the result ("score ≥ 7 → fast-track, otherwise → nurture"), and require human approval before any external email goes out. Agentic where judgement helps, deterministic everywhere else.
Grounded, cited answers. Knowledge Vaults let agents and grids answer from your documents and policies with citations. Useful well beyond GTM, across support, ops, and research.
Where Clay may fit better
Be honest about this: if pure outbound enrichment and prospecting is the whole job, Clay is often the cleaner pick. Its waterfall logic and 150+ provider marketplace are purpose-built for squeezing the highest possible match rate out of a list, and its dual-credit model is tuned for buying that data efficiently at volume. Outbound and growth teams that live in list-building, verification, and sequencing get a deep, polished, specialist tool, plus a large community sharing tables and recipes.
If maximum provider coverage on contact data is your single deciding factor, Clay's specialism is real. QX covers contact enrichment well via connected providers and web research, but it's a broad platform, not an enrichment marketplace. For that one job, weigh depth against the value of having everything in one place.
Which should you pick?
Pick Clay if pure outbound enrichment is the whole job: you want the deepest provider coverage, highest match rates, and a workflow built end-to-end around list-building, verification, and sequencing.
Pick QX Labs if you want enrichment plus the rest of your AI work in one connected workspace: the same Grids that enrich and score at scale, Agents you delegate to from Slack or email, Flows that automate triggered pipelines with guardrails, and Knowledge that grounds answers in your own data, all on one usage-based plan with no per-seat charge.
The honest test: if enrichment is the only thing you need, buy the specialist. If enrichment is one of several AI jobs your team has, buy the platform that does all of them in one place.
See it for yourself
The fastest way to decide is to watch a Grid enrich, research, and score a real list, then see the same workspace run an agent and a flow on top of it. Book a demo and we'll run one of your workflows live, or start free (every feature is on the free plan) and try it on your own data.
Explore the pieces: Grids, Agents, Flows, Knowledge, and the 1,000+ integrations.
See what AI agents can do for your team
Deploy agents that can act across your data and 1,000+ apps.