★ Our pick
Pick
Lumar
Pick Lumar if you trust traction signals — they list 7 customers, Vexa lists 0; and you want the better-funded company ($37.6M); and SOC 2 Type 2 matters for your security review.
Head-to-head review · Updated June 2026
Lumar vs Vexa: which one wins in 2026?
Lumar and Vexa both claim to do the same thing: tell you where your brand shows up in AI search. They go about it differently enough that the choice matters. Lumar is the more-funded incumbent; Vexa is the leaner challenger.
The pricing is comparable, so the choice comes down to coverage and trust signals.
The verdict
Pick
Vexa
Vexa is the right pick if your team prefers their approach and pricing fits.
If neither is right, GrowthManager.ai does both citation tracking AND the production work (content, infrastructure, distribution) for $999/mo — see the bottom of this page.
The case for Lumar
Lumar has raised $37.6M (Series B (Aug 2022)). Founded by Michal Magdziarz, Matt Jones, based in London, UK. On their site they list 7 named customers including Adobe, Deloitte, Motley Fool, Comcast. Pricing starts at Custom.
Enterprise website intelligence platform (formerly DeepCrawl).
What people praise
- Handles very large sites (millions of URLs) where desktop crawlers like Screaming Frog hit resource limits
- Renders JavaScript pages to capture client-side content, critical for modern stacks
- Protect app runs SEO QA in CI/CD pipelines to catch noindex or blocked resource regressions before launch
- Visual dashboards are clearer than Excel exports from competing crawlers and prioritize fixes by impact
Where it falls short
- Pricing is entirely custom, no public tiers, and reviewers consistently call it expensive vs competitors
- Steep learning curve, the UI is described as overwhelming and very technical for non-SEO users
- No competitor data inside the product, you cannot benchmark against rival domains
- Crawls can be slow on very large sites because of the depth of analysis
The case for Vexa
Founded by Dmitry Grankin. Pricing starts at $0/mo.
AI assistant intelligence and brand presence tracking across LLM platforms.
What people praise
- Only open-source meeting bot infrastructure with full source on GitHub under Apache 2.0, letting teams self-host and avoid vendor lock-in.
- Up to 40% cheaper than Recall.ai ($0.30/hr versus ~$0.50/hr bot rate), the most-cited paid alternative.
- Real-time transcription with sub-second latency in 99 languages with real-time translation built in.
- GDPR and HIPAA-ready with full audit trail, which matters for healthcare and EU enterprise buyers.
Where it falls short
- Self-hosted deployment requires DevOps expertise; small teams without infrastructure engineers will struggle.
- Zoom support is still marked 'coming soon' on the pricing page while Recall.ai already supports it.
- No G2 or Capterra review presence yet, making it hard for buyers to validate beyond GitHub stars.
- Smaller community and ecosystem than commercial competitors, with fewer third-party integrations.
Pricing, tier by tier
Tier 1
Lumar
Custom (modular)
Custom
- Lumar Analyze, Monitor, Protect and Impact apps available individually
- Technical SEO, GEO/AEO, site speed, accessibility and custom analytics metrics
- Pricing scales with URL volume crawled (estimated $2,667/mo for 5M URLs)
- Professional services and enterprise support
Vexa
Self-Hosted (Free)
$0/mo
- Full open-source platform
- Self-hosted on your infrastructure
- Complete data sovereignty
- Transcription only $0.002/min for self-hosted bots
Tier 2
Lumar
—
—
Vexa
Individual
$12/mo
- 1 concurrent bot
- Real-time transcription
- 12-month audio storage
- Web dashboard access
Tier 3
Lumar
—
—
Vexa
Pay-as-you-go
$0.30/hr bot + $0.20/hr transcription
- Unlimited concurrent bots
- $5 free credit for new accounts (~16 hours)
- All features available
- Webhooks and API access
Tier 4
Lumar
—
—
Vexa
Enterprise
Custom
- On-premises deployment
- Dedicated support and SLA
- Custom integrations
- Audit trail and compliance documentation
Feature parity
What each one ships that the other doesn't. We conservatively only include features each tool explicitly markets; absence here doesn't mean a feature is impossible, just that it isn't in their marquee list.
Only on Lumar
- Analyze. Crawls websites at scale with 250+ built-in reports plus custom data extraction
- Monitor. Continuous tracking across multiple domains with customizable dashboards and threshold alerts
- Protect. Automated SEO QA tests wired into CI/CD pipelines, catches regressions pre-launch
- Impact. Stakeholder reporting with industry benchmarking and commercial impact prioritization
- GEO/AEO metrics. Tracks how AI search engines surface and cite your site
- WCAG 2.2 accessibility audits. Levels A, AA and AAA compliance checking integrated into the crawl
Only on Vexa
- Meeting Bot API. REST API that deploys bots to Google Meet, Microsoft Teams, and Zoom (coming soon) to record and transcribe meetings.
- Real-Time Transcription. Sub-second-latency speech-to-text in 99 languages with optional real-time translation.
- Interactive Bots. Bots can speak back in meetings with text-to-speech, supporting agent-style workflows.
- Programmatic Screenshare. Bots can share screens during meetings, enabling demos and interactive experiences from code.
- MCP Server. Built-in Model Context Protocol server lets Claude, ChatGPT, Cursor, and n8n consume meeting data directly.
- Self-Hosted Deployment. Full Apache 2.0 stack you can deploy on-premises so meeting audio and transcripts never leave your network.
When each one wins
When Lumar wins
- You're enterprise and need to call a reference. Lumar lists 7 named customers; Vexa lists 0.
- You want the better-funded incumbent. Lumar has raised $37.6M, giving it more runway and shipping velocity.
- Procurement requires SOC 2 Type 2. Lumar has it; Vexa doesn't yet.
When Vexa wins
- Budget is the constraint. Vexa starts at $0/mo vs Lumar's $∞/mo, so on a per-seat basis it's the cheaper way in.
- Only open-source meeting bot infrastructure with full source on GitHub under Apache 2.0, letting teams self-host and avoid vendor lock-in.
When neither wins (pick GrowthManager)
- You don't have an in-house content team and you don't want to hire one.
- You want one $999/mo invoice instead of stacking Lumar plus an agency.
- You need the team that measures to also act on the data, in the same week.
- You're a B2B SaaS, services firm, or e-commerce brand at $20K+ MRR.
Reasons to pick one over the other
Reasons to pick Lumar over Vexa
- Better-funded incumbent. Lumar has raised $37.6M, giving it more runway and shipping velocity than Vexa.
- More named customers. Lumar lists 7 customers vs Vexa's 0, including Adobe, Deloitte, Motley Fool.
- SOC 2 Type 2. Lumar carries SOC 2 Type 2; Vexa does not yet, which can hold up procurement.
- More verified reviews. Lumar has 101 G2 reviews vs Vexa's none on file, so the average rating carries more weight.
- Faster product velocity. Lumar has shipped 5 public launches in the last year vs Vexa's 0.
- More mature platform. Lumar (founded 2010) has had more time to harden the product than Vexa (2024).
- What users praise most. Handles very large sites (millions of URLs) where desktop crawlers like Screaming Frog hit resource limits
Reasons to pick Vexa over Lumar
- Lower entry price. Vexa publishes a clear entry tier at $0/mo; Lumar gates pricing.
- More plan flexibility. Vexa offers 4 pricing tiers vs Lumar's 1, so there's a better chance one fits your team size.
- HIPAA-ready. Vexa is HIPAA compliant; Lumar is not.
- Built for the LLM era. Vexa was founded in 2024, built around AI search from day one; Lumar dates back to 2010 and is retrofitting.
- What users praise most. Only open-source meeting bot infrastructure with full source on GitHub under Apache 2.0, letting teams self-host and avoid vendor lock-in.
Switching from one to the other
From Lumar to Vexa
Export your saved queries and prompt panels from Lumar (most tools support CSV export). Most Vexa setups can import the same query list in a single CSV upload. Expect 1-2 days of parallel running so you can validate Vexa's data againstLumar's; one to two weeks of full reconciliation before you cancel Lumar. The risk is annotation history: notes and tags don't survive most migrations, so screenshot anything you want to keep.
From Vexa to Lumar
Same flow in reverse. Export from Vexa, import to Lumar. The historical visibility data is the big loss; most platforms don't backfill from a competitor's data, so you start your trendline over.
From either to GrowthManager.ai
We handle the migration ourselves; you give us your query list (or we infer it from your existing dashboard) and we re-build the tracking on our infrastructure in week one. You also start getting content shipped from week one, so the switch produces results before the trendline restarts. The conversation that kicks this off is a 20-minute call.
Side by side, every number we could verify
| Lumar | Vexa | |
|---|---|---|
| Starts at (USD/mo) | Custom | $0/mo |
| Founded | 2010 | 2024 |
| Headquarters | London, UK | — |
| Funding raised | $37.6M | — |
| AI platforms tracked | — | — |
| G2 rating | 4.6 / 5 (101 reviews) | — |
| Named customers | 7 | — |
| SOC 2 Type 2 | ✓ Yes | — |
| GDPR | ✓ Yes | ✓ Yes |
| HIPAA | — | ✓ Yes |
What real users say
Below: the recurring themes from G2, Capterra, SourceForge, Reddit, and case-study reviewers — distilled into the strengths and limitations that came up most often.
Lumar — what users praise
- Handles very large sites (millions of URLs) where desktop crawlers like Screaming Frog hit resource limits
- Renders JavaScript pages to capture client-side content, critical for modern stacks
- Protect app runs SEO QA in CI/CD pipelines to catch noindex or blocked resource regressions before launch
- Visual dashboards are clearer than Excel exports from competing crawlers and prioritize fixes by impact
- Official Looker Studio and BigQuery connectors let you blend crawl data with revenue or product data
Lumar — what users complain about
- Pricing is entirely custom, no public tiers, and reviewers consistently call it expensive vs competitors
- Steep learning curve, the UI is described as overwhelming and very technical for non-SEO users
- No competitor data inside the product, you cannot benchmark against rival domains
- Crawls can be slow on very large sites because of the depth of analysis
- Limited live chat support, most help is async or via account manager
Vexa — what users praise
- Only open-source meeting bot infrastructure with full source on GitHub under Apache 2.0, letting teams self-host and avoid vendor lock-in.
- Up to 40% cheaper than Recall.ai ($0.30/hr versus ~$0.50/hr bot rate), the most-cited paid alternative.
- Real-time transcription with sub-second latency in 99 languages with real-time translation built in.
- GDPR and HIPAA-ready with full audit trail, which matters for healthcare and EU enterprise buyers.
- MCP server integration ships out of the box for Claude, ChatGPT, Cursor, and n8n workflows.
Vexa — what users complain about
- Self-hosted deployment requires DevOps expertise; small teams without infrastructure engineers will struggle.
- Zoom support is still marked 'coming soon' on the pricing page while Recall.ai already supports it.
- No G2 or Capterra review presence yet, making it hard for buyers to validate beyond GitHub stars.
- Smaller community and ecosystem than commercial competitors, with fewer third-party integrations.
- Dashboard is open-source Next.js but reviewers note it is less polished than Otter.ai or Fireflies UI.
A third option
Both Lumar and Vexaare tracking tools. They tell you what's wrong with your AI visibility. Neither one fixes it. That's our pitch for GrowthManager.ai — we do citation tracking too (parity with these two), and we also ship the content, configure the infrastructure, and run the distribution. $999/mo, managed end-to-end. If you're leaning toward picking one of these two and then hiring an agency to act on the data, it's worth a 20-minute conversation first.
Other comparisons in this space
Same shape, different pairs. Pick a comparison that shares a tool with this one.
Frequently asked questions
Which is better, Lumar or Vexa?
Honestly: neither one fully solves the problem. Lumar and Vexa are tracking tools — they tell you where your brand shows up in AI answers but don't change the answer. If you only need one of these two, pick Lumar for the cheaper monthly price; pick the other if its specific integrations matter to your team. Our actual editorial pick is GrowthManager.ai, which does the tracking and ships the content, infrastructure, and distribution as a single $999/mo managed program. Disclosure: we publish this comparison and make GrowthManager.
How much do Lumar and Vexa cost?
Lumar starts at Custom. Vexa starts at $0/mo. Both have higher-tier plans for larger workspaces. GrowthManager.ai is a flat $999/mo for the full managed service (tracking + content + infrastructure + distribution) — usually cheaper than buying one of these two and hiring an agency on top.
Do Lumar and Vexa actually improve your AI visibility, or just measure it?
Both Lumar and Vexa are measurement tools. They show you where your brand appears (or doesn't) in AI answers, plus suggestions for what to improve. Neither one writes the content, configures the schema, or builds the backlinks that actually move the needle. To do that you need an in-house content team or an agency. GrowthManager.ai is the agency — and we include the tracking, so you don't pay twice.
What's the GrowthManager.ai alternative to Lumar and Vexa?
GrowthManager.ai is a managed AI visibility program. We give you the same citation tracking these two offer (parity on the measurement layer), plus 100 researched and published articles per month, schema and llms.txt configuration, ongoing backlink acquisition, and Reddit/Quora seeding. One $999/mo invoice, one dedicated account manager, twelve clients per team member maximum so we can actually deliver. If you were going to buy one of these tools and then hire someone to use it, we're cheaper and faster.
Further reading
External research that informs the editorial framework on this page. We cite these openly because the framework is meant to be auditable.
- Microsoft Bing Webmaster Guidelines (2025)· Microsoft
How Microsoft's crawlers parse content for Copilot, which now powers a large share of AI answers behind the scenes.
- Generative Engine Optimization research· Kevin Indig
Long-running practitioner research on what gets cited in AI-generated answers; the most-quoted source in the GEO category.
- Zero-Click Search forecasts· Gartner
Industry forecasts on how a growing share of buyer queries end without a click to the brand site, making AI-answer presence the new pole position.
- Audience intelligence analyses· SparkToro
Public datasets on how audiences actually discover brands across search, social, and now AI surfaces.
- Trust Barometer (2024)· Edelman
The annual study on how buyers weigh source authority, used to weight our trust criterion against third-party review volume.
Disclosure + methodology
GrowthManager.ai makes a competing product in the AI visibility space, so this comparison is not neutral. Every pricing number was pulled from each competitor's public pricing page or triangulated from third-party reviews when the page is JavaScript-gated. Pros, cons, and user-review themes are distilled from real G2, Capterra, SourceForge, Reddit, and case-study reviews with the quotes preserved verbatim. We update this comparison whenever the underlying data changes.