Original · Unique Research · 2026-05-14
Editor's note: The report and its judgments belong to the original Chinese author. This English rendition retains the full product analysis, ranking summary, About Us section, and complete research statement including methodology, concept definitions, metric definitions and disclaimer. All traffic, monthly active user, funding, and revenue figures are Unique Research estimates or source claims, not independently audited findings. The revenue estimation methodology and ±10% average absolute error rate are as stated in the source. Company and product names are retained in their original English or transliterated forms where official names are unverified. Ranking data references external sources at 100aiapps.cn; the "complete data available at 100aiapps.cn" references are preserved as in the original.
Global AI Application Rankings
April AI Web Growth Rankings: The Products Quietly Rising
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This article is based on Unique Research's compiled Top 10 data on global AI Web product traffic growth and monthly active user growth for April 2026, combined with hands-on research on each product.
Right after getting the April Web data, I flipped through it roughly—the growth rankings are must-read content.
Not because these products are all particularly hot; quite the opposite, many of the names on the list, I believe, most people following AI have barely heard of. But that is precisely what makes it interesting: what emerges every month is not the products everyone discusses repeatedly, but things quietly catching fire within specific user groups.
The traffic growth ranking (visits month-over-month growth) and the MAU growth ranking (monthly active users month-over-month growth) together contain 14 unique products. I basically visited all of their official websites, and for some I also looked at recent reporting. Below are my observations.
The Video Track Has Entered "Tool-Layer" Involution
There are 4–5 products in the rankings related to video, but if you look closely, they are solving completely different problems.
Pexo had the most explosive growth this time, with visits up 18x month over month and MAU up 19x. The numbers are a bit startling, but you have to understand its scale: monthly visits are only 350,000, with 230,000 MAU. Small base, large elasticity—this is common sense. Still, the growth itself is worth being curious about, so I took a look at the product.
Pexo's positioning is clear: a conversational AI video Agent. You do not need to understand prompt engineering or know which model to use. Just tell it, "I want to make a 15-second vertical ad video for this product, with a tech-style feel," and it automatically selects the model (Seedance, Kling, Sora, etc.), automatically generates the storyboard, automatically generates the video, and sends the result directly to you. It can also integrate with Telegram, WhatsApp, and Discord, so you can invoke it right in the chat box.
This direction represents workflow encapsulation on top of models—not "here is a stronger video generation model for you," but "I help you skip all the selection and configuration steps." Users no longer need to understand the capability differences between models; they only need to describe their intent. Growth is sustained by content volume.
Choppity and aicut represent another direction: AI editing from long video to short video. This track is a bit like "AI article writing" from a year or two ago—there will soon be a flood of similar tools, and competition will rely on subtitle accuracy, tracking precision of auto-reframing ratios, and export speed. Choppity's current differentiation is contextual understanding: it does not just look at volume peaks, but truly analyzes content semantics to find the moments with real "hooks." It has 100K+ creator users and ranks 8th on the MAU growth list.
On the face-swapping tool side, there are two: AI Face Swapper and Swapfaces AI (swapfaces.ai), both on the list. This type of tool naturally has traffic—the demand is real—but there is also no barrier to entry. Traffic can come, but retention, honestly, is hard to say.
My overall judgment on the video track: "generation" and "editing" are beginning to diverge into two clear tracks. Pexo and aicut go the route of generating from scratch; Choppity goes the route of extracting from existing content. The latter may have a lower short-term ceiling, but a more stable user base. Content creators already have a large stock of material, and once they get used to this workflow, stickiness will be high.
Developer Infrastructure: Quietly Shifting from "Nice Tools" to "AI Battleground"
What impressed me most this time were Mintlify and Fireworks AI.
Mintlify had the largest monthly visits among products on this list—1.4 million monthly visits, 840,000 MAU—appearing on both rankings. Its product positioning is an AI documentation platform. It helps developers generate and maintain technical documentation.
It sounds like nothing special, but I suggest you seriously understand what it is doing.
Mintlify has a feature called Workflows: when your codebase changes and a PR is merged, it automatically reads the diff, drafts the corresponding documentation update, and then sends a PR for your review. Documentation maintenance shifts from "someone needs to remember to update it" to "part of the CI/CD pipeline." After Coinbase adopted it, documentation update time dropped from 20 minutes to 60 seconds. HubSpot reduced engineering resources spent on documentation by 50%.
But even more interesting is its other capability: MCP server. Your documentation site can expose an MCP endpoint, allowing AI coding assistants like Cursor, Claude Code, and GitHub Copilot to query your documentation in real time. When a developer asks AI, "how does this API do authentication," AI does not answer from the fuzzy memory it saw during training—it directly queries the current, accurate content in your documentation's MCP.
This means documentation sites are shifting from "web pages for humans to read" to "the interface layer of AI systems." Mintlify's own data shows that roughly half of the traffic to the documentation sites it hosts comes from AI Agents—not real users browsing, but various AI tools querying. This proportion is still rising.
It just completed a US$45 million Series B in April, led by a16z, with Anthropic, Perplexity, and Cursor among its customers. Viewed from the perspective of AI's overall infrastructure, documentation platforms are quietly becoming a very critical connectivity layer.
Fireworks AI ranked 9th on the MAU growth list, with 310,000 MAU. It is an open-source model inference platform. If you want to run open-source models like Llama, DeepSeek, or Mixtral quickly but do not want to set up your own GPU cluster, you come to Fireworks.
Fireworks' core advantage is inference speed: their self-developed FireAttention kernel is much faster than native serving in high-concurrency and long-context scenarios, reportedly up to 4x faster than competitors in some scenarios. They now process over 13 trillion tokens per day, with 99.99% API availability.
In March, they announced a deep collaboration with Microsoft Azure, and Fireworks officially launched on Microsoft Foundry. Enterprises using Azure services have their open-source model inference layer run by Fireworks. This is an interesting combination: Microsoft handles compliance and governance, Fireworks handles performance and efficiency.
The logic behind its MAU growth ranking appearance, I suspect, is: more and more developers and companies are pushing open-source models into production, and need a solution that is more worry-free than "building it yourself" and more efficient than "cloud vendor default configuration." Fireworks is positioned exactly in this spot.
B2B Vertical Applications: Finding Real Pain Points Is the Moat
There are several B2B products on this list, and I think their common characteristic is particularly worth noting: they all found a specific, long-overlooked operational pain point within enterprises, then rebuilt it with AI.
Solidroad is the product that made me think "the story is told well" this time.
The problem it solves: traditional call center quality assurance can only cover 1%–3% of conversations (manual spot checks), and what happens in the remaining 97% is completely unknown. Solidroad's approach is to use AI to automatically score 100% of conversations (phone, email, chat, AI customer service), identify which employee is weak in which type of scenario, and then automatically generate simulation training scenarios targeted at that specific weakness—"next time there is a customer with a billing dispute, you can practice three times with AI first."
This is a bit like moving pilot simulator training to the customer service industry. After Podium adopted it, the speed of resolving customer issues improved by 33%; Crypto.com's customer satisfaction score rose by 3 percentage points; Tech Mahindra's new employee onboarding time was shortened by 50%.
Solidroad is a YC W25 project, both founders have Intercom backgrounds, and it just completed a US$25 million Series A in April led by Hedosophia, with total funding exceeding US$30 million.
I think there is a practical backdrop to this product's growth: more and more enterprises are deploying AI customer service, but AI customer service hallucination rates and drift rates are actually quite high (industry data says 15%–57% of AI customer service responses have issues). Solidroad has capitalized on the trend to become a unified layer covering both human and AI customer service quality assurance, with well-timed positioning.
Mutiny is another transition I find quite clever.
It was originally a website personalization tool—A/B testing, showing different content to different visitors. But in April it announced a major transition: abandoning pure website personalization and transforming into "AI Agent for GTM teams."
What does the new Mutiny do? It connects to your CRM and brand asset library, then automatically generates customized one-pagers, micro-sites, presentations, and proposals for each target customer. The logos, case studies, and copy shown to the customer are automatically swapped to the version most relevant to that company. It can also track which pages the other party opened and how long they viewed them, letting sales know what the decision-maker on the other side is thinking even before entering the meeting room.
This transition direction got one thing right: the core pain point of modern B2B sales is not "I do not have good content," but "my content is too slow to keep up with each customer's personalized needs." Mutiny has reduced the friction in this to extremely low levels.
Lessie AI is a talent/opportunity search tool built by a going-global team, with both visits and MAU around 120,000, emerging in growth among recruitment tools.
Its positioning is "AI talent search Agent": you describe the person you want to find in natural language. "Senior data scientist in New York with fintech background," or "VP of Marketing at a SaaS company with 100+ people in Europe." Lessie simultaneously searches for you across LinkedIn, GitHub, Twitter, and over 100 other platforms, gives you verified contact information, and can even automatically generate personalized outreach emails.
From a B2B tool perspective, the core barrier for this type of product lies in data quality and precision in vertical scenarios. Lessie is still early-stage, but the direction is right—manual outreach efficiency in recruitment and sales is too low, and AI doing search + outreach integration is a very natural replacement path.
Publer is the most "stable" one: a social media management tool with 400,000 monthly visits and growth close to 3x.
Honestly, what Publer does is quite common—AI copywriting, batch scheduling, cross-platform publishing. But it has maintained a high reputation for cost-performance in this already highly competitive track. 500-post batch upload, automatic content recycling, Canva integration, and paid plans starting from as low as US$5/month make it the top choice for independent creators and small teams.
3x growth is a good signal for this mature category, possibly related to their recent deepening of AI feature depth. AI writing, AI suggestions for optimal posting times, AI image generation—these have been integrated into a more complete workflow.
Information Layer Eroders: Who Is Eating the Search Engine's Cake
LINER AI ranked 2nd on the visits growth list (10.7x) and 2nd on the MAU growth list (12x), one of the few products to make the top 3 on both lists. 320,000 monthly visits, 170,000 MAU.
LINER is an AI search engine aimed at students and researchers, with 10 million+ users. Its core selling point is that every answer comes with citation annotations. Not just "some AI says this is how it is," but "every sentence of this answer comes from which webpage/paper, and you can click in to verify."
It has several features I find interesting: Scholar mode, which searches for answers only from academic papers, for doing homework and literature reviews; deep research mode, which automatically generates detailed reports; one-click generation of APA/MLA/Chicago format citations; and multi-model selection. Users can switch between Liner's own model, the GPT-4 series, and Gemini.
This growth explosion, I suspect, is related to its recent optimization of the deep research feature. The Perplexity-like product landscape is becoming increasingly clear. Everyone is competing for the "high-intent user" segment, and students and researchers are a user group with strong willingness to pay and stable demand.
ChatSlide.ai appeared only on the MAU growth list (5th), with 100,000 MAU and growth of about 1x. Formerly called DrLambda, it has regained momentum after the rename.
It does AI presentation generation: upload a PDF, input a URL, paste a YouTube link, and get a PPT in minutes, with optional AI virtual presenter (digital human), voice cloning, and video output. It also has built-in PubMed search, so doctors and scholars can directly find papers in the platform and then generate presentation materials.
This category is highly competitive (Gamma, Tome, Canva, Beautiful.ai are all in it), and ChatSlide's MAU growth, I think, mainly relies on "broad input formats"—almost anything can be thrown in—and the workflow to video is one-stop, no need to switch to another tool to make the presenter video.
An Outlier: CocktailWave
Among this growth ranking, CocktailWave is the most puzzling to me.
Its labeled category is "personal assistant," but in reality it is just an AI cocktail discovery platform—you tell it what flavor you want to drink today, it recommends a cocktail, gives you the recipe, historical story, and taste description. It has an iOS app, supports Chinese and English bilingual. Completely free.
Why does it have around 2x growth in both visits and MAU?
Honestly I did not find a particularly strong reason. One possibility is that this type of vertical leisure app had a brief viral spread on social media (some account recommended it), bringing a wave of traffic. Another possibility is that it tapped into a group of users who "play with mixology at home"—this is a niche but very real demand.
With a small user base (120,000 visits), growth looks good in percentage terms, but it is hard to say this represents any trend. Including it here is a reminder: not every product on the list represents a major direction; sometimes it is just catching a small wave.
To Summarize, What I Saw
After going through all 14 products, several impressions recurred:
1. "Useful" Brings More Retention-Type Growth Than "Interesting"
The products with the most stable growth on this list—Solidroad, Mutiny, Mintlify, Lessie—without exception are solving a real, recurring workflow problem. They are not products you try once and think "that's cool," but the kind you use three times and cannot live without. This contrasts with a large number of consumer-grade AI products.
2. Going-Global Teams Are Quietly Building Positions in the Tool Layer
Pexo and Lessie are both going-global products. The globalization of AI tools is not just being done by OpenAI and Anthropic; there is a group of Chinese going-global teams finding vertical entry points in specific overseas scenarios, then doing it cheaper and with denser product capability than local competitors. This line deserves continuous attention.
3. Infrastructure Is Also Competing on "Productization"
The growth of Mintlify and Fireworks AI shows that in the developer tools market, "better developer experience" is a product logic that can drive traffic. MCP interfaces, AI Agent readability, inference speed—these things originally considered "pure technology" are becoming core dimensions of competitive product capability.
4. Video Tools Are Diverging into "Generation School" and "Editing School"
These two routes have different user bases, business models, and growth flywheels—do not conflate them. The generation school relies on creative scenario expansion; the editing school relies on workflow replacement for content creators.
5. The Traffic Growth Ranking Always Has Noise
CocktailWave is a good example. When looking at rankings, you need to combine absolute values (how much MAU), growth rate, and product logic—all three dimensions together—not just look at multiples.
There will be rankings like this every month, and every month's sample is incomplete, but every time you look carefully, you can see corners you did not notice before. These products are not the protagonists of the AI industry, but they are what people who actually use AI are using.
If you are also interested in this type of data, you can continue to follow Unique Research's monthly statistics—not to chase hot topics, but to maintain perception of the capillaries of this industry.
Data source: Unique Research April 2026 Global AI Web Product Traffic Statistics.
Statistical scope: Global AI Web products with monthly visits greater than 100,000. This article's analysis is based on public information and does not constitute investment advice.
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Research Statement
2.1 Data Description
The AI product revenue data involved in this report, article, ranking, or chart is based on real-time tracking of redirect traffic from AI product official websites to payment gateways (such as Stripe), combined with time series analysis, deeply analyzing historical visits, monthly active user fluctuations, and pricing strategy changes, thereby constructing a dynamic revenue evaluation model. The model's core parameters are determined through a triple verification system of paid conversion funnel monitoring, reverse calibration of enterprise-disclosed data, and comparison with industry benchmark conversion rates, and are promptly compared and corrected with enterprise financial reports, financing announcements, and authoritative media disclosures. Through sampling verification, the average absolute error rate between predicted values and actual data is stable within ±10%, with accuracy reaching industry research-grade standards.
The AI product traffic data involved in this report, article, ranking, or chart is based on multi-source data integration and Unique Research's intelligent algorithm processing, with the monitoring scope strictly limited to users' direct access behavior through official applications and websites. Specifically, this data only covers users' direct access to AI products through websites and native applications, and does not include traffic data generated by the following access methods: browser plugins/extensions, desktop client software, WeChat/Alipay mini-program ecosystems, embedded services on third-party platforms such as Discord, local deployment of open-source models, API interface calls, and other non-direct access scenarios. This data focuses on monitoring AI products' core end-side access behavior, aiming to objectively reflect the direct usage of mainstream user terminals.
Given the complexity and dynamics of data collection and processing, as well as the constantly changing market environment, the displayed data may contain a certain degree of error and omission. Therefore, this data should be regarded as a reference basis for research and analysis, aiming to provide users with a window for in-depth understanding of the AI product market, rather than a definitive basis for directly formulating specific investment strategies or providing consulting advice.
Data Coverage Expansion Note:
We are actively developing and testing monitoring models for the following emerging fields, expected to be gradually included in future quarterly reports:
AI Agent ecosystem
AI hardware device sales and activity
Large model API call scale estimation
Stay tuned.
2.2 Concept Definitions
• Geographic dimension
Overseas AI products: Products founded by non-Chinese entrepreneurs or teams, primarily targeting the global market (including but not limited to their domestic market).
Domestic AI products: Products founded by Chinese entrepreneurs or teams, primarily targeting China's local market.
Going-global AI products: Products founded by Chinese entrepreneurs or teams, but with the main market target positioned overseas (non-Chinese local market).
• Functional dimension
AI-native applications: Applications that deeply integrate artificial intelligence technology and algorithms from the initial product design, whose core value, business processes, or user experience completely rely on AI technology. These applications not only use AI to optimize or enhance existing functions, but take AI as their core component—without AI technology, these applications would cease to exist or lose their core value.
AI-feature applications: Applications that enhance existing functions or provide entirely new functions by integrating or embedding AI technology on the basis of original business logic and application frameworks. These applications may already have mature business models and market positioning, but through the introduction of AI technology, can significantly improve efficiency, enhance user experience, or create new value points.
AI-ecosystem applications: Platform-type applications that provide support, connection, or communication facilitation for the AI ecosystem composed of AI technology developers, application developers, service providers, and end users. These applications are not limited to the technical level, but also involve multiple dimensions such as market, community, and resource sharing, aiming to promote the popularization, innovation, and cooperation of AI technology.
2.3 Metric Definitions
• WEB data metrics
Visits: The total number of all visits to a website within a specific time period (such as one month), used to measure the scale of website traffic. Page views within a continuous active period are counted as the same session, while a user who reactivates after an interval of more than 30 minutes or starts a new day is regarded as a new visit, thereby comprehensively and accurately measuring website traffic and visitor activity.
Unique Visitors: The number of independent IPs that visit the target website within a specific time period (such as one month). If someone visits a website on multiple days within a month, they are only counted as one unique visitor.
Subscription Revenue (ARR): The total revenue obtained by a website through subscription services provided to users within a certain period (such as one year), and this revenue excludes advertising revenue, transaction commissions, and professional services with one-time characteristics.
• APP data metrics
Downloads: The number of times an App is downloaded by users within a specific time period (such as one month). Usually measured by the number of downloads of a single App on app stores (such as App Store, Google Play, etc.). Downloads are one of the important indicators for measuring an App's popularity and user attractiveness, and can also reflect the effectiveness of App promotion and marketing activities.
Active Users: The number of independent users who have performed at least one activity within a specific time period (such as one month). Active users is an indicator for measuring user participation and activity level within a specific time period, usually used to evaluate an App's user scale and user activity.
In-App Purchase Revenue (IAP): Revenue generated by users' purchase behavior in an App to obtain virtual goods, additional services, advanced features, etc. This excludes advertising revenue, direct user payments (such as tips), and revenue from third-party Android app stores.
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