The verdict: buy the plumbing, not the demo
Most lists of the best MCP tools for startups rank by horsepower, so they hand a seed-stage team the same answer they'd hand a Fortune 500: Salesforce. That's the wrong instinct. A startup doesn't need the most powerful MCP server — it needs the one a founder can wire into Claude this afternoon, that exposes the right data for its function, and that a generalist can run without a specialist. We scored 14 sales, marketing, and product tools against 47 requirements across eight categories, then weighted the result for a sub-$10M-ARR startup rather than an enterprise.
- HubSpot ranks #1 for startups (8.4/10) — not because it's the most capable, but because it pairs strong MCP and native AI with the startup fit Salesforce lacks.
- Salesforce is the most mature tool in the set (9.0 maturity) but ranks #2, dragged down by the weakest startup fit of any vendor evaluated (5.0/10). Power, but heavy.
- Attio is the standout AI-native challenger — #4 overall, and #3 in Sales, despite competing in just one of three functions. On native AI and startup fit it keeps pace with tools many times its size.
- Marketing is the laggard function. Averaged across vendors, marketing MCP/AI maturity scores 4.5/10 — well behind product (6.1) and sales (5.3). The best marketing tool in the set barely clears 6.8.
- The AI is ahead of the plumbing. Native generative-AI features average 7.0/10 across vendors, but actual MCP server maturity averages only 5.8. Most of these tools can think before an agent can fully operate them.
The startup MCP landscape, on one canvas
Before the function-by-function detail, here's the whole field in one picture. Every vendor is plotted on the two dimensions this report cares about most:
- MCP/AI maturity (horizontal) — a composite of MCP server maturity, generative and agentic AI, and data architecture. How ready the tool is for an agent to actually operate it.
- Startup fit (vertical) — how quickly a small, generalist team gets to value: setup time, pricing accessibility, and whether it runs without a specialist.
The quadrants tell the story. Top-right is where a startup wants to buy — mature and accessible — and only HubSpot and Attio reach it. Salesforce and Intercom sit far right (powerful) but lower (heavier). Most of the field clusters top-left: easy to adopt, still building out the agent surface. Bottom-left is where promising tools haven't yet earned a place in an agent-operated stack.
Almost everything here is startup-accessible. Maturity is what separates the field — and only HubSpot and Attio reach the top-right.
The underlying numbers behind every dot — including each vendor's requirement-gap count — are in the maturity, startup fit & completeness table further down.
Why this evaluation is built differently
Three choices make this report diverge from the vendor-authored lists that dominate this search.
It's vendor-neutral. Nearly every “best MCP tools” or “best AI GTM stack” article ranking today is published by a company that sells one of the tools and conveniently ranks itself first. This evaluation is published by Proofmap, which doesn't sell a sales, marketing, or product tool — so there's no thumb on the scale.
It scores three functions in one rubric, on purpose. We evaluated sales, marketing, and product tools together against the same eight categories. That means a sales tool scores near-zero on the Product and Marketing function areas — and that's intended signal, not noise. It lets us compare a tool's function fit and its cross-cutting MCP/AI maturity on the same canvas.
In the overall ranking, each vendor is scored only on its primary function (the other two are treated as not-applicable), so a focused tool isn't penalized for not being a suite.
It's weighted for a startup, not an enterprise. The raw rubric rewards enterprise-grade maturity, which is why an unweighted score crowns Salesforce. We re-weighted for the sub-$10M-ARR buyer — startup fit carries the most weight (27.5%), security the least (7.5%):
Re-weighted for the startup buyer — startup fit carries the most, security the least.
The result is a ranking that answers “what should a startup actually adopt,” not “what has the deepest feature tree.”
Where the data comes from
The methodology is a partnership. Proofmap defined the 47 requirements — calibrated for what a sub-$10M-ARR team actually needs from an agent-operable GTM stack. Olive provided the vendor research and scoring data, which we complemented with our own research and analysis of each vendor's MCP server and AI capabilities.
Unbiased Vendor Research
Scores are built on Olive's independent vendor research and real vendor responses — structured around the 47 requirements Proofmap defined for this evaluation, and complemented by Proofmap's own research into each vendor's MCP and AI surface. Not pay-to-play rankings, not sponsored placements, not reviews.
Coverage note: Semrush and Ahrefs are not in this evaluation. That's deliberate — the reasoning is a finding in its own right, covered in where are Semrush and Ahrefs? Read the marketing-function results as a thinner field than sales or product.
Is MCP a durable bet?
Yes — and the strongest signal isn't the adoption numbers, it's who is now co-governing the standard. In December 2025, Anthropic donated MCP to the Agentic AI Foundation, a vendor-neutral directed fund under the Linux Foundation co-founded by Anthropic, Block, and OpenAI, with AWS, Google, Microsoft, Bloomberg, and Cloudflare among its platinum members.
When direct competitors agree to steward the same infrastructure through a neutral body, a protocol has stopped being one vendor's bet and become shared plumbing — the pattern that made TCP/IP and Kubernetes durable rather than proprietary.
The adoption underneath that move is real. One year after its November 2024 release:
- Over 97 million monthly SDK downloads across Python and TypeScript.
- More than 10,000 published MCP servers.
- First-class support in Claude, ChatGPT, Gemini, Microsoft Copilot, Cursor, and VS Code.
It is also spreading into the fabric of the web itself. Microsoft's NLWeb protocol — announced at Build 2025 and led by R.V. Guha, creator of RSS and Schema.org — makes every NLWeb site automatically an MCP server, which Microsoft frames as playing a role for the agentic web similar to the one HTML played for the browsable web.
For a startup, the practical takeaway: MCP maturity is a durable buying criterion, not a fad you're chasing. A vendor's investment in a real MCP server is a bet on the standard the rest of the industry has now formally agreed to build on.
The startup-adjusted ranking
| Rank | Vendor | Startup-Adjusted Score | Function |
|---|---|---|---|
| 1 | HubSpot | 8.4 | Sales |
| 2 | Salesforce | 8.0 | Sales |
| 3 | Intercom | 7.8 | Product |
| 4 | Attio | 7.5 | Sales |
| 5 | Mixpanel | 7.5 | Product |
| 6 | Amplitude | 7.2 | Product |
| 7 | PostHog | 7.1 | Product |
| 8 | Klaviyo | 6.8 | Marketing |
| 9 | Apollo | 6.2 | Sales |
| 10 | Webflow | 6.0 | Marketing |
| 11 | Gong | 6.0 | Sales |
| 12 | Clay | 5.5 | Sales |
| 13 | Google Analytics | 5.2 | Marketing |
| 14 | Beehiiv | 4.8 | Marketing |
Three incumbents hold the top of the table — HubSpot, Salesforce, and Intercom have shipped genuinely complete MCP and native-AI capability, and the “bolted-on” critique that defined 2024 doesn't hold against them anymore.
But the more useful story sits just below: a tight band of AI-native tools — Attio, Mixpanel, Amplitude, PostHog — clustered between 7.1 and 7.5, separated from the leaders mostly by breadth and enterprise polish, not by whether an agent can use them.
The challenger band is where the startup-relevant decisions actually live.
The MCP race has a surprising leader — and it's the wrong race anyway
The first counterintuitive finding: the incumbents won the MCP race. On raw cross-cutting maturity, Salesforce (9.0) and Intercom (8.6) lead, with HubSpot close behind. The challengers didn't leapfrog them. If your only question were “who has the deepest, most production-ready MCP and native AI,” the answer is the same set of names that have dominated enterprise software for a decade.
But “most mature” is the wrong target for a startup, and the data shows why. Maturity and startup fit pull in opposite directions at the top of the table: Salesforce posts the highest maturity in the set and the lowest startup fit (5.0). You're buying capability you'll spend months operationalizing — exactly the trade a resource-constrained team should avoid.
The second finding is more actionable: the AI is ahead of the plumbing. Native generative-AI features average 7.0 across these vendors, while actual MCP server maturity averages 5.8. Most of these products have shipped in-app AI faster than they've exposed clean, agent-operable MCP servers.
For a founder, that gap is the whole ballgame — an impressive in-app copilot you can't reach from your own agent is worth far less than a plain, complete MCP server you can.
| Category | Average Score |
|---|---|
| Generative & Agentic AI | 7.0 |
| Startup Fit | 7.0 |
| Data Architecture | 6.7 |
| Product (function) | 6.1 |
| MCP Server Maturity | 5.8 |
| Security | 5.6 |
| Sales (function) | 5.3 |
| Marketing (function) | 4.5 |
Native AI (7.0, teal) outruns MCP server maturity (5.8, teal) across the board — and marketing (4.5, clay) trails every other function.
Per-function winners (the decision you're actually making)
No startup chooses between Salesforce and PostHog — they choose the best sales tool, the best product tool, the best marketing tool. Here's the call in each.
Sales
HubSpot for most startups; Attio if you want the AI-native bet.
Winner: HubSpot. The default startup CRM earns it here — top sales-function score (10.00) plus genuine startup fit (8.3), with native AI grounded in first-party CRM data.
The AI-native pick: Attio. It reaches #3 in Sales and #4 overall on the strength of its native AI (8.33) and startup fit (8.3) despite competing only in sales — the clearest example in this report of an AI-native challenger keeping pace with the suites.
Specialists worth knowing: Gong (conversation intelligence) and Apollo (outbound) both score well on sales function but trail on cross-cutting maturity; Clay's enrichment power isn't yet matched by its MCP exposure.
Product
The tightest, strongest field in the report.
Product analytics is the most consistently mature function. Intercom leads on cross-cutting strength (native AI 9.44, MCP maturity 8.89).
Mixpanel and PostHog both pair a strong product-function score (9.00 and 8.00) with top-tier startup fit (8.3) — PostHog is the AI-native, open-source pick for technical founders; Mixpanel the broadest. Amplitude is strong but the heaviest of the four. Any of these is a defensible choice; the differentiator is less “can an agent use it” and more “which fits your team.”
Marketing
The whole field trails — pick on function, not on AI maturity.
The weakest function, and not by a little. Klaviyo is the strongest available (lifecycle/email), followed by Webflow (web/CMS). Google Analytics and Beehiiv trail.
Even the leader here (6.8) would rank mid-pack in sales or product. This isn't a coverage artifact alone — see the dedicated finding below.
The finding nobody's writing about: marketing is the MCP laggard
Across all 14 vendors, marketing MCP/AI maturity averages 4.5/10 — the lowest of any function, behind product (6.1) and sales (5.3). The best marketing tool in the set (Klaviyo, 6.8) would not crack the top half of the sales or product rankings.
Why does the function that lives or dies on content and data lag on agent-accessible AI? Two reasons show up in the data:
- Structural: marketing tools have rich creation surfaces but thin exposure — they let you make things in-app faster than they let an external agent read campaign data or publish through it.
- Field composition: the canonical marketing-data tools — the SEO platforms — sit outside the set. That's not an oversight; it's a finding of its own, covered in the next section.
But the core pattern holds even accounting for both: marketing AI is long on generation and short on the trustworthy, structured inputs an agent needs to generate anything worth shipping.
Where are Semrush and Ahrefs? The web-data call-out
Both SEO incumbents ship MCP servers, and both belong in any marketing conversation. We left them out anyway — because the reason they don't fit this rubric is a finding in its own right.
Much of what a startup historically bought an SEO suite for — rankings, traffic reporting, site diagnostics — lives in data the startup already owns or can reach directly. Google Search Console and Google Analytics now expose that data over MCP, and the AI assistant on top (Claude, ChatGPT) supplies the analysis and reporting layer the suites used to charge for. Point an agent at your own first-party sources and you've reproduced a meaningful slice of the SEO-suite workflow without the subscription.
Notice what makes that possible: the underlying data is web data — public, or first-party data you already own. That's the opposite of the CRM situation. You can't route around HubSpot or Attio to reach your own pipeline; proprietary internal data makes the vendor's MCP server the only door. Tools built on data anyone's agent can reach face a structurally different MCP era than tools that are the sole custodian of yours.
That doesn't zero out the incumbents. Semrush's and Ahrefs' proprietary crawl indexes — backlink graphs, keyword-volume estimates, competitor visibility — remain data an agent can't assemble from first-party sources. But it changes the buying question from “which SEO suite” to “what does the suite add over an agent pointed at the sources I already own.” That's the bar any SEO platform now has to clear.
The same logic runs the other way for the rest of the marketing field: what makes a marketing tool durable in an agent-operated stack is the data only it can provide. Which is where the trust gap opens.
Read, write, generate: where the trust gap opens
It helps to sort what these tools let an AI agent actually do into three tiers:
- Read — pull context (a deal, a funnel, a campaign metric). Safe, and increasingly well-supported.
- Write — take actions (update a record, schedule a send, create a cohort). Higher-stakes, supported by the more mature tools, ideally gated by human confirmation.
- Generate — produce customer-facing output: a sales email, a case study, a landing page, a product claim.
The first two tiers are where MCP maturity is racing ahead. The third is where a quieter problem sits. When an agent generates something a customer will see, the value depends entirely on whether what it's drawing from is true.
And none of the 14 tools here verify the customer proof an agent would cite. A CRM can hand an agent a deal record; it can't tell the agent whether the quote it's about to put in a sales email is real, attributed, and approved for use. That's the missing layer — not access to data, but assurance that the proof an agent generates from is on the record. It's the same provenance gap we mapped across an entirely different category in our generative engine optimization software evaluation — and the reason verified proof is becoming the currency AI systems trade on.
This is the layer Proofmap sits underneath the rest of the stack to provide: a verified Proofbase of on-record customer proof that an agent can draw from when it generates anything customer-facing, so the output is grounded in proof of record rather than a model's best guess. MCP solved access. The next gap is trust in what gets generated — and it's the reason the marketing function, the one most dependent on trustworthy source material, is also the one furthest behind.
Vendor profiles
Grouped by function, ordered by startup-adjusted score. Each profile: where it ranks, what it's best for, its strength, and the watch-out.
HubSpot — #1 overall (8.4)
Best for: Most startups that want one CRM that's strong across the AI stack.
Strength: Top sales-function score (10.00) with real startup fit (8.33) and native AI grounded in first-party CRM data. Only two requirement gaps in the whole rubric — the most complete tool evaluated.
Watch-out: MCP server maturity (7.22) trails its native AI (8.33); the agent experience is good, not yet best-in-class.
Salesforce — #2 overall (8.0)
Best for: A startup that's certain it's scaling into enterprise sales motions fast.
Strength: The most mature tool in the set — native AI 9.44, MCP server 8.89, and only two requirement gaps.
Watch-out: The lowest startup fit of any vendor here (5.00) — power you'll pay for in setup time and cost.
Attio — #4 overall (7.5)
Best for: The AI-native bet — modern GTM teams that want a CRM built around structured data and automation, not retrofitted.
Strength: Native AI (8.33) and startup fit (8.33) that rival far larger suites; #3 in Sales and #4 overall while competing in a single function.
Watch-out: Single-function by design; you're buying a CRM, not a stack.
Apollo — #9 overall (6.2)
Best for: Outbound-led founders.
Strength: Strong sales function (8.33) with solid native AI (7.22) and data architecture (7.50) underneath the outbound motion.
Watch-out: Weak MCP server maturity (2.78) — the data's there, agent access isn't. 20 requirement gaps.
Gong — #11 overall (6.0)
Best for: Founder-led sales with call recording.
Strength: Strong sales function (8.33) built on conversation intelligence.
Watch-out: Low MCP maturity (4.44) and the joint-lowest startup fit in the set (5.00).
Clay — #12 overall (5.5)
Best for: AI-driven prospecting and enrichment.
Strength: Genuinely AI-native in concept, with a sales-function score (5.83) that reflects real enrichment power.
Watch-out: Among the lowest MCP server maturity in the set (2.78) — the conversational layer over its data isn't there yet.
Intercom — #3 overall (7.8)
Best for: Product-plus-support teams.
Strength: Cross-cutting strength that tops the product field — native AI 9.44 and MCP server 8.89.
Watch-out: Thinner on marketing (5.00) and security (6.00) relative to its other scores.
Mixpanel — #5 overall (7.5)
Best for: The best all-round product analytics pick for a startup.
Strength: Product function 9.00 plus top-tier startup fit (8.33).
Watch-out: Mid MCP maturity (6.11) — solid, not leading.
Amplitude — #6 overall (7.2)
Best for: Deeper analytics needs.
Strength: Product function 9.00 with strong data architecture (7.50) and MCP server maturity (7.22).
Watch-out: The heaviest of the four product tools (startup fit 6.67) and 13 requirement gaps.
PostHog — #7 overall (7.1)
Best for: Technical founders — open-source, AI-native, beloved by exactly this ICP.
Strength: Product function 8.00 with top-tier startup fit (8.33); depth over polish.
Watch-out: Native AI (6.11) and MCP server (6.67) are good but not yet leading.
Klaviyo — #8 overall (6.8)
Best for: Lifecycle and email — the #1 marketing tool in this evaluation.
Strength: The strongest marketing tool here, with top-tier startup fit (8.33) and solid data architecture (7.50).
Watch-out: Weak sales/cross-sell exposure (2.50); it leads a trailing field.
Webflow — #10 overall (6.0)
Best for: Managing and updating site content through an agent.
Strength: Decent MCP exposure for a web tool (6.11) and the second-best marketing-function score (5.83).
Watch-out: Narrow to web/CMS; low on everything outside it.
Google Analytics — #13 overall (5.2)
Best for: One thing startups will genuinely use: asking an agent “what happened to traffic last week” instead of logging in.
Strength: Ubiquitous, free, and strong on security (7.00) — a low-cost read-only context source for any agent stack.
Watch-out: Read-only context; 20 requirement gaps; little an agent can act on.
Beehiiv — #14 overall (4.8)
Best for: The AI-native newsletter pick for founder-led distribution.
Strength: Promising native AI (6.11) for a young product, and a genuine fit for founder-led content motions.
Watch-out: The lowest MCP server maturity in the set (1.67) — almost no agent surface yet. 25 requirement gaps.
Maturity, startup fit & completeness
The source data behind the quadrant at the top of this report. Maturity is the mean of MCP Server Maturity, Generative & Agentic AI, and Data Architecture; gaps are the requirements (of 47) a tool does not meet.
| Vendor | MCP/AI Maturity | Startup Fit | Requirement Gaps |
|---|---|---|---|
| HubSpot | 7.7 | 8.3 | 2 |
| Salesforce | 9.0 | 5.0 | 2 |
| Intercom | 8.6 | 6.7 | 6 |
| Attio | 7.3 | 8.3 | 8 |
| Mixpanel | 6.5 | 8.3 | 8 |
| Amplitude | 7.1 | 6.7 | 13 |
| PostHog | 6.3 | 8.3 | 14 |
| Klaviyo | 6.9 | 8.3 | 13 |
| Apollo | 5.8 | 6.7 | 20 |
| Webflow | 5.7 | 6.7 | 12 |
| Gong | 5.6 | 5.0 | 18 |
| Clay | 5.0 | 6.7 | 18 |
| Google Analytics | 5.0 | 6.7 | 20 |
| Beehiiv | 4.3 | 6.7 | 25 |
Recommendations by startup scenario
You want one CRM that does the most across your AI stack → HubSpot. Most complete (2 gaps of 47), strong fit (8.3), #1 overall at 8.4.
You're betting on AI-native and want a modern CRM built for agents → Attio. The clearest structural-advantage pick in the report.
You're a technical, product-led team → PostHog (open-source, AI-native) or Mixpanel (broadest), with Intercom if support is core.
You're scaling into enterprise sales motions fast → Salesforce, eyes open about the setup cost and the startup-fit trade (5.0, lowest in the set).
You're standing up lifecycle/email or founder-led distribution → Klaviyo for lifecycle, Beehiiv for newsletter — but weight your choice on function, since the marketing field's AI maturity trails.
Take the data with you
We've packaged this entire evaluation — the full 14-vendor dataset, the eight-category scores, the key findings, and ready-made prompts — into a structured prompt file you can paste straight into Claude or ChatGPT to pressure-test a shortlist against your own stack. Use the download banner on this page, or grab the raw prompt file directly — free, no signup.
Frequently asked questions
What are the best MCP tools for startups?
For a sub-$10M-ARR startup, HubSpot ranks first overall (8.4/10), followed by Salesforce and Intercom. Attio is the standout AI-native pick, and Mixpanel, PostHog, and Amplitude lead in product. The right answer is function-specific: choose the strongest tool for sales, marketing, or product rather than one overall winner.
What is an MCP server, and why does it matter for a GTM stack?
An MCP (Model Context Protocol) server lets an AI agent securely read and act on a tool's data — pulling a deal, a funnel, or a campaign metric, or updating records. For a go-to-market stack, it's the difference between disconnected apps and a set of tools your own AI agent can actually operate.
Does HubSpot have an MCP server, and how does it compare to Salesforce?
Yes — HubSpot ships a native MCP server and ranks #1 for startups (8.4). Salesforce is more mature overall (9.0 maturity) but posts the lowest startup fit in the set (5.0). For most startups, HubSpot's balance of capability and accessibility wins; Salesforce suits teams scaling fast into enterprise sales motions.
Should startups pick tools based on native AI features or MCP maturity?
MCP maturity. Across these 14 tools, native AI features average 7.0/10 while actual MCP server maturity averages only 5.8 — the AI is ahead of the plumbing. An in-app copilot you can't reach from your own agent is worth less than a complete, operable MCP server. Buy the plumbing, not the demo.
Why aren't Semrush and Ahrefs in this report?
Partly scope, partly thesis. Both ship MCP servers — but much of what a startup buys an SEO suite for now lives in data it can reach directly: Google Search Console and Google Analytics expose their data over MCP, and the AI assistant on top supplies the analysis and reporting layer. Their proprietary crawl indexes (backlinks, keyword volumes) still differentiate them — that's the bar any SEO suite now has to clear. See the web-data call-out in this report.
Methodology & data notes
- Scope: 14 vendors across sales, marketing, and product, evaluated together in one rubric.
- Requirements: 47 across 8 categories; 27 designated must-haves. The three function categories (Sales / Marketing / Product) score a vendor only in its primary function for the overall ranking; cross-cutting categories score all vendors.
- Weighting (startup-tuned): Startup Fit 27.5% · Primary Function 20% · MCP Server Maturity 17.5% · Generative & Agentic AI 17.5% · Data Architecture 10% · Security 7.5%.
- Maturity axis in the quadrant = composite of MCP Server Maturity, Generative & Agentic AI, and Data Architecture.
- Coverage limitation: Semrush and Ahrefs were scoped out — see the web-data call-out. Marketing-function results sit on a thinner field than sales or product.
- Source: vendor-capability research, June 2026. Scores reflect researched capabilities, not buyer-team weightings.
- Provenance: scores are a deterministic rubric applied to third-party vendor-capability research (evaluation framework by Proofmap; vendor data via Olive, complemented by Proofmap's own research and analysis) — a point-in-time snapshot as of June 2026, refreshed quarterly.
- Independence: published by Proofmap, which does not sell a sales, marketing, or product tool and is not a scored vendor in this report.
Scores are a structured starting point for evaluation, not a substitute for hands-on validation against your own requirements.
Vendor comparison: full scores
Every vendor against all eight categories. The function-area near-zeros are by design — a tool is scored only in its own function for the overall ranking, but the raw cross-function scores are shown here for completeness.
| Vendor | Sales | Marketing | Product | MCP Server | Generative AI | Data Arch | Security | Startup Fit |
|---|---|---|---|---|---|---|---|---|
| HubSpot | 10.00 | 8.33 | 8.00 | 7.22 | 8.33 | 7.50 | 9.00 | 8.33 |
| Salesforce | 9.17 | 8.33 | 9.00 | 8.89 | 9.44 | 8.75 | 9.00 | 5.00 |
| Intercom | 7.50 | 5.00 | 8.00 | 8.89 | 9.44 | 7.50 | 6.00 | 6.67 |
| Attio | 7.50 | 3.33 | 4.00 | 7.22 | 8.33 | 6.25 | 5.00 | 8.33 |
| Mixpanel | 4.17 | 5.00 | 9.00 | 6.11 | 7.22 | 6.25 | 6.00 | 8.33 |
| Amplitude | 2.50 | 5.00 | 9.00 | 7.22 | 6.67 | 7.50 | 5.00 | 6.67 |
| PostHog | 2.50 | 3.33 | 8.00 | 6.67 | 6.11 | 6.25 | 5.00 | 8.33 |
| Klaviyo | 2.50 | 5.00 | 8.00 | 6.67 | 6.67 | 7.50 | 5.00 | 8.33 |
| Apollo | 8.33 | 2.50 | 1.00 | 2.78 | 7.22 | 7.50 | 3.00 | 6.67 |
| Webflow | 3.33 | 5.83 | 4.00 | 6.11 | 6.11 | 5.00 | 5.00 | 6.67 |
| Gong | 8.33 | 0.83 | 6.00 | 4.44 | 6.11 | 6.25 | 6.00 | 5.00 |
| Clay | 5.83 | 3.33 | 4.00 | 2.78 | 6.11 | 6.25 | 4.00 | 6.67 |
| Google Analytics | 0.83 | 3.33 | 6.00 | 4.44 | 4.44 | 6.25 | 7.00 | 6.67 |
| Beehiiv | 1.67 | 4.17 | 2.00 | 1.67 | 6.11 | 5.00 | 3.00 | 6.67 |
The full grid — where each tool is strong, and where the near-zeros are by design (a tool is scored in its own function).
Scores reflect June 2026 vendor capability on a 0–10 scale. Evaluation framework by Proofmap. Vendor research and scoring data via Olive, complemented by Proofmap research and analysis.
