Sniffsub is a Reddit analytics tool that groups subreddits into custom collections, tracks their growth and engagement, and uses NLP clustering to surface recurring themes and optimal posting windows. Built by a solo indie developer for founders, marketers and e-commerce sellers doing market research.
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Sniffsub is a subreddit analytics tool that treats Reddit as a research corpus rather than a posting destination. Its official positioning is direct — the homepage headline reads "Explore Your Audience On Reddit," with a subhead about finding highly relevant conversations for your brand, product or project. In practice that means you point the tool at communities instead of keywords-in-the-abstract: you assemble a set of subreddits that plausibly contain your buyers, and the product then reports how those communities are growing, when their members are active, and which topics keep resurfacing inside them.
The product is built and maintained by a solo indie developer. The About page names him as Xiang and describes the origin plainly: he left a full-time engineering role at a large technology company in 2022 to work independently, cycled through projects that did not find traction, and concluded that the recurring mistake was building things disconnected from what people actually asked for. Sniffsub is his answer to that — the stated mission is to help indie developers, small startups, designers and e-commerce sellers discover real needs from real conversations. That provenance matters more than it might for a larger vendor, and this page returns to it in the sections on limitations and buyer fit, because it shapes what you should and should not expect from the service.
Positioned against the broader category, Sniffsub sits at the narrow end. Enterprise social listening suites cover many networks, offer contractual support and sell to procurement committees. Sniffsub covers one network, is priced for individuals and small teams, and is transparent about being a focused tool. The trade-off is legible: you get Reddit-specific depth — subreddit growth tables, community-level theme extraction, posting-time analysis — without the breadth, the account management or the compliance paperwork that larger platforms bundle in.
The homepage organises the product into three verbs, and they map cleanly onto how the tool is actually used.
The first step is assembling what the product calls collections. The official description is to "build your custom portfolio of communities" and to "group subreddits by niche, industry, or interest to create a focused lens." This is the conceptual heart of the tool. Reddit's difficulty is not that data is unavailable but that it is scattered across hundreds of thousands of communities of wildly different sizes and cultures. A collection is a saved answer to the question "which twenty communities actually matter for what I sell," and every subsequent metric is computed against that saved set rather than against Reddit at large.
Collection count is the primary axis on which the paid plans differ. The Basic plan allows up to five collections; the Pro plan removes the cap. That design tells you something about intended usage: five collections is enough for one product with a few adjacent segments, while unlimited collections suits an agency or a team tracking several clients or several product lines in parallel.
The second verb covers tracking. The official framing is to "track growth and engagement in real-time" and to see "which communities are spiking and where the conversation is heading." The public trending page shows the shape of this output without requiring an account: it lists communities sorted by growth, giving for each one a total member count, an absolute figure for new members, and a growth percentage. On the day this page was researched the listing included r/GiftIdeas at 553,551 members with 8.1 percent growth, r/TopCharacterTropes at 636,810 members with 7.7 percent growth, and r/interesting at 1,450,878 members with 4.2 percent growth. You can filter the listing by community size band, which is useful because growth percentages are not comparable across scales — a small community can post a large percentage from a handful of joins, while a percentage point on a community of over a million members represents a substantial absolute movement.
A companion view aggregates Reddit content into broad categories with post volumes attached. At the time of research the page displayed fifty-one named categories with a control to load more, led by Technology at 89,790,493 posts, Games at 79,030,039 posts and Lifestyle at 73,759,393 posts. These are orientation numbers rather than decision numbers — they tell you the relative weight of broad content areas, not which specific community deserves your attention.
The third verb is where the AI claim sits. The official phrasing is "AI-extracted themes and trends," followed by the frank explanation that "we read thousands of posts so you don't have to, surfacing the topics that matter." The methodology page is unusually specific for a product of this size, and it names four distinct analytical approaches rather than hiding behind a generic AI label.
Theme extraction uses NLP and clustering algorithms to identify recurring themes, with models analysing post titles, keywords and contextual relationships. Engagement scoring rates posts on upvote ratios, comment velocity, and relative performance measured against the subreddit's own averages — the relative element is the important part, since a post with two hundred upvotes means something very different in a community of ten thousand than in one of two million. Timing analysis computes optimal posting windows from thousands of posts while accounting for timezone and day-of-week patterns. Similarity detection identifies related subreddits through user overlap, shared vocabulary and topic similarity, which is the mechanism behind discovering communities you did not already know to look for.
Alongside the analytical layer, the product offers keyword-driven subreddit search and a semantic search capability. Semantic search appears even on the free tier, though in a limited form. The distinction matters in practice: keyword search finds communities whose names or descriptions contain your term, while semantic search is intended to find communities that discuss your problem space without necessarily using your vocabulary — which is precisely the gap that trips up first-time Reddit researchers who search for their own product category and find nothing.
For research workflows that end outside the browser, the product exposes a subreddit list export. The official limit is up to 10,000 subreddits in a single export, delivered as CSV, with each row carrying the subreddit name, description, subscriber count, creation date, post activity and NSFW status. Filters for keyword, subscriber count, NSFW status and creation date narrow the set before you export it. This is a genuinely useful primitive for anyone building their own analysis on top — with one caveat about data freshness covered in the limitations section below.
The Pro plan lists Metrics API Access and Collection API Access among its features. The public site does not publish API documentation, rate limits or authentication details, so the practical scope of that access cannot be verified from the outside. Anyone whose purchase decision depends on the API should confirm its specifics with the developer before subscribing rather than inferring capability from a feature-list line item.
Several tools are published outside the paywall and outside the login. A best-time-to-post analyser accepts a subreddit and returns weekly activity in UTC, marked explicitly as free. There is also an LLM token price calculator, which has nothing to do with Reddit and reads as a traffic-acquisition asset — a common and unremarkable pattern for an indie product, though worth recognising for what it is.
The official FAQ places this first under its Getting Started grouping, and the described workflow is to locate the subreddits where your target audience already gathers, then search within them for the pain points your idea claims to address. This is Reddit market research in its most literal form, and the value of the sequence is its order: you look for evidence of demand before you write code, rather than shipping and then hunting for someone who cares. Sniffsub's contribution is narrowing the search space — instead of guessing at community names, you use keyword and semantic search to assemble a candidate set, then read what those communities complain about.
The product's Market Research grouping describes searching for competitor names alongside phrases such as "alternative to" or "too expensive," with the tool automating sentiment tracking across the results. This is one of the most reliable uses of Reddit as a data source, because dissatisfied users of an established product describe their problems in unusually concrete terms — they name the specific workflow that breaks, the specific limit they hit, the specific price point that pushed them to look elsewhere. That specificity is what makes the output usable for roadmap decisions rather than merely directional.
The official FAQ states that the tool uses AI to comprehend the context of thousands of posts and to filter for discussions carrying high purchase intent — people asking for recommendations, seeking alternatives, or looking for solutions to a specific problem. The companion Shopify application makes this framing even more direct, describing Reddit conversations as a lead source that can be swept with a single search of a product keyword or a store domain. Treat the output as a queue of conversations worth reading rather than as a list of qualified leads; the product surfaces threads, and judging intent and deciding whether a reply is appropriate remains a human task.
Posting-time analysis serves a narrow but real need. Reddit's ranking rewards early velocity, so a post that lands when a community is asleep can be effectively invisible regardless of quality. The tool computes windows from thousands of posts with timezone and day-of-week patterns factored in. The practical caveat is that timing optimisation raises the ceiling on a good post; it does not rescue a poor one, and no amount of scheduling precision substitutes for writing something a community wants to read.
The official FAQ describes this explicitly: Reddit threads rank well in search engines, so a well-answered question becomes what the site calls a permanent entry point that keeps delivering visitors months or years after it was posted. The research task here is finding questions that both recur in the community and have search demand behind them — which is where theme extraction earns its keep, since a theme that appears repeatedly across a thirty-day window is by definition a question people keep asking.
The FAQ names this use case and points at communities organised around app testing, suggesting monitoring for phrases such as "need an app for." It is a modest application, but a real one for a pre-launch product with no user base and no mailing list.
The FAQ addresses promotion for indie games and gives advice that runs against the instinct most first-time marketers have: contribute value inside niche communities and use the tool to monitor trending topics rather than promoting blindly. This is correct and worth restating. Reddit communities are moderated by volunteers with low tolerance for extraction, and a poorly judged promotional post costs more than the impressions it buys. Any research tool for Reddit is best understood as a way to become better informed before participating, not as a way to participate at scale.
Both the trending listing and the best-time-to-post analyser are publicly accessible without an account. Run your own communities through them first. This is the cheapest possible way to judge whether the tool's data matches what you already know about spaces you understand well, and it costs nothing but a few minutes.
Use keyword and semantic search to find subreddits related to your problem space. Cast wider than feels comfortable at this stage; the similarity detection feature exists precisely because the obvious communities are usually not the only relevant ones, and adjacency discovered through user overlap frequently outperforms the communities you would have guessed.
Large is not the same as useful. A community of two million with a strict no-self-promotion rule and a firehose of daily posts may be a worse target than a focused community of forty thousand where your topic is the entire point. Use the growth figures to distinguish communities gaining momentum from those merely large and static, and use the size filter so you are comparing like with like.
Save your pruned set as a collection. This is the step that turns ad-hoc searching into repeatable measurement, since subsequent theme and engagement analysis runs against a stable set you can revisit over time. Remember the Basic plan's cap of five collections when deciding how granular to make them.
Theme extraction points you at what matters; it does not replace reading. Clustering identifies that a topic recurs, but the reason it recurs — the specific frustration, the specific workaround people have invented — lives in the post text and the comments underneath it. Use the themes as an index into the corpus, then open the threads that the index flags.
Once you have something worth posting, run the target subreddit through the best time to post on Reddit analysis and schedule accordingly. Treat the recommendation as a prior rather than a rule, and verify it against your own results over several posts.
If your analysis needs to leave the product — combining Reddit community data with your own customer records, for example — the CSV export handles bulk extraction with the fields listed earlier. Note the freshness caveat below before you build anything time-sensitive on it.
Compare growth within a size band, not across the whole list. Percentage growth is scale-dependent, and the trending page's size filter exists for exactly this reason. Comparing a 30 percent gain in a five-thousand-member community against a 4 percent gain in a two-million-member community is comparing two different phenomena.
Trust relative engagement over absolute engagement. The product's own scoring uses performance relative to subreddit averages, and that is the right instinct to adopt manually as well. A hundred upvotes is an achievement in some communities and a rounding error in others.
Verify the free tier against a community you know well. Before paying, run the tool against a subreddit you already read daily. If its themes match your own sense of what that community discusses, the extraction is working for your domain. If they do not, no amount of tier upgrading will fix a mismatch between the clustering and your subject matter.
Do not treat theme extraction as sentiment analysis. Clustering tells you what is discussed, not how people feel about it. A theme labelled around your competitor's name could be enthusiasm or fury, and the only way to know is to read.
Set a thirty-day mental window. The default analysis window is thirty days. Anything you conclude is a statement about the last month, not about the community's enduring character, and seasonal effects can dominate a single window.
Budget for the reading, not just the subscription. The tool's honest pitch is that it reads thousands of posts so you do not have to. The corollary is that you still have to read the ones it surfaces, and that time is the real cost of the workflow.
Take the seven-day refund window seriously as a test period. The refund terms are inconsistent between pages, as discussed below, so treat the first week as your genuine evaluation period and resolve any doubts inside it.
Indie developers and solo founders are the explicitly stated audience, named on the About page alongside small startups, designers and e-commerce sellers. The fit is good: pre-launch validation is exactly the problem the founder describes having had himself, and the entry price is low enough to test against a single product idea.
Small marketing teams and agencies running Reddit as one channel among several. Unlimited collections on the Pro tier is the feature that matters here, since tracking several clients or product lines in parallel exceeds the Basic cap quickly.
E-commerce sellers are directly targeted through the companion Shopify application, which searches posts, comments and shared links by product keyword or store domain and offers hour-by-weekday posting heatmaps. If you already run a Shopify store, that surface is worth evaluating first — it is free, though as noted below it carries no user reviews.
Content and SEO practitioners chasing the long-tail traffic pattern the FAQ describes, where a well-answered Reddit thread ranks and keeps delivering visitors.
Who it is not for. Organisations that need multi-network coverage, contractual uptime commitments, a named support contact or vendor security review will not find them here — the site publishes no SLA, no security documentation and no company registration details. Anyone whose compliance process requires a documented legal entity and governing law should read the section below carefully, because the terms of service leave the governing-law provision blank.
Sniffsub is a browser-based web application. The public marketing pages — the trending listing, the category breakdown, the methodology page, the best-time-to-post analyser and the export tool — are reachable without an account, while the working application under the app path requires a login.
Authentication and account handling are conventional, and the privacy policy names the third-party processors involved: Google Analytics and PostHog for analytics, Stripe for payments. The policy states that data may be stored and processed in the United States and references the California Consumer Privacy Act of 2018 in setting out access, correction, deletion and portability rights. It does not specify a data retention period.
Beyond the web application there is one additional distribution surface: a Shopify application listed as "SniffSub: Reddit Brand Monitor," published by a developer account named Olaka App with sniffsub.com given as the developer website. It launched on 18 February 2025 and is listed as free. No native mobile applications are published for either iOS or Android.
The pricing page lists three tiers, and the figures below are quoted from it directly.
Free — $0 per month. The free tier is explicitly a limited version rather than a trial: limited subreddit metrics, limited themes, limited topics and limited semantic search. The specific numeric limits are not published. Its real function is evaluation, and it is sufficient for that.
Basic — $15 per month, or $180 billed annually. Positioned for starting users. It unlocks full access to topics, full access to themes, full access to metrics, up to five collections, and early access to new features.
Pro — $83 per month, or $990 billed annually. Positioned for power users and scaling teams. It adds unlimited collections, unlimited AI functions, Metrics API access and Collection API access.
Two observations on the arithmetic are worth making, because the pricing page does not make them for you. First, the annual options are not discounts in the usual sense: $180 across twelve months is exactly $15 per month, and $990 across twelve months is $82.50 per month against a $83 monthly rate. Paying annually here buys almost nothing beyond commitment, which is unusual — most SaaS annual plans carry a ten to twenty percent reduction. Second, the gap between Basic and Pro is wide, better than five and a half times, with no intermediate tier. If your need is unlimited collections but not API access, there is no plan shaped for you.
The pricing page also states that you can cancel at any time, offers a seven-day money-back guarantee, and says there are no hidden fees. The terms of service complicate that last point, as the next section explains.
Because Sniffsub occupies a narrow niche, the honest comparison set spans several different product shapes rather than a list of direct clones.
Enterprise social listening platforms — the established multi-network monitoring suites — cover Reddit as one channel among many, alongside contractual support, security review documentation and account management. They are the right choice when Reddit is part of a broader brand-monitoring mandate and when procurement requires a vendor that can answer a security questionnaire. They cost substantially more and are not sold at indie-developer price points.
Reddit's own native tools. Reddit itself provides subreddit search, and community moderators often publish their own statistics. This costs nothing and is the correct baseline for anyone with only a handful of communities to track. Sniffsub's advantage appears at scale — comparing dozens of communities systematically over time is where manual checking becomes untenable.
Direct data access via the Reddit API. Teams with engineering capacity can build their own pipeline. This offers total control and no per-seat fee, at the cost of build and maintenance time, and subject to the platform's access terms, which for commercial use are contractual rather than self-service.
General-purpose analysis on exported data. The CSV export makes a hybrid approach viable — take the bulk community list, do your own analysis in a spreadsheet or notebook, and use the product only for the parts that are hard to replicate.
For Reddit audience research specifically, the decision usually reduces to two questions: is Reddit a primary channel or an occasional one, and do you need a vendor relationship or just a tool? Sniffsub answers well when Reddit is primary and a tool is sufficient.
This section is longer than usual because the constraints here are concrete and several of them are only visible when you compare pages against each other.
The refund terms contradict themselves. The pricing page advertises a seven-day money-back guarantee, and the terms of service confirm in one place that you can request a refund within seven days by email, with approved refunds returned to the original payment method. Elsewhere in the same terms document, the subscription clause states that payments will be charged periodically, that you can cancel at any time, and that "Payments are non-refundable, except as required by law." The site does not reconcile the two statements. Both are quoted here rather than harmonised, because harmonising them would mean choosing one on your behalf. Anyone relying on the refund should get written confirmation before subscribing.
No legal entity or governing law is disclosed. The terms refer to Sniffsub as "the Company" but name no registered entity, no jurisdiction and no company address, and the governing-law clause is left incomplete. The footer copyright reads "© 2026 SniffSub. All rights reserved." For an individual buyer at $15 per month this is unremarkable; for an organisation with a vendor onboarding process it is likely to be a blocker, and it is better discovered now than during procurement review.
Data source and platform authorisation are not disclosed. The site does not state how it obtains Reddit data, whether it holds commercial API authorisation, or what its relationship to Reddit is. There is no non-affiliation statement anywhere in the terms or privacy policy. This page does not assert that the product is or is not compliant — the verifiable fact is simply that the official pages make no statement on the subject, and buyers who need certainty should ask directly.
Refresh rates differ by feature and the difference is easy to miss. The methodology page states an appealing cadence: new posts indexed daily, trending content updated hourly, theme models retrained weekly. The export page states something different about its own dataset — that the database is refreshed monthly. Both statements are official and both are presumably accurate about their respective components, but reading only the first would leave you expecting fresher exports than you get. If your workflow depends on a newly created community appearing in an export, plan for month-scale latency.
Coverage scale is unverifiable. The methodology page's aggregate statistics render as placeholder values — "0+" and "0M+" — rather than real figures, and no page discloses how many subreddits the product indexes in total. You therefore cannot confirm that a community you care about is covered without checking it individually. Checking individually is easy, via the free tools, and is the recommended mitigation.
There is essentially no independent evaluation of this product. This is the single most important caveat for a prospective buyer, and it is stated plainly rather than papered over. No listing exists on the major B2B software review platforms. No coverage by established technology publications was found. No user discussion was located in developer or founder communities. The companion Shopify application carries a rating of 0.0 from 0 reviews, and two independent Shopify tracking services corroborate this — one reports "Not enough data" for its review history, the other records an average rating of 0 and a total of 0 reviews. On Product Hunt the product remains listed in beta, launched on 5 February 2025 with a feature launch on 14 February, and has fifteen followers and no reviews. A zero-review count is not a bad review; it is an absence of evidence, and with a sample size of zero no quality conclusion of any kind can be drawn from public sources. The practical consequence is that you must evaluate the product yourself using the free tier and the seven-day window, because there is no peer experience to lean on.
It is a single-maintainer product. The About page presents a solo operation. This is not a defect — small tools built by one attentive person are frequently better than committee-built alternatives — but it does mean bus-factor risk is real, response times depend on one person's availability, and there is no support organisation behind the email address. The blog offers a rough proxy for activity: two posts, the most recent dated 10 January 2026 and the other 12 December 2025.
Scraping the product is prohibited. The terms forbid systematically retrieving data from the service without permission, automated use of the system, and access through automated or non-human means. If you want programmatic data, the supported route is the Pro tier's API rather than scraping the web interface.
Predictive metrics are unexplained. The Shopify application is described as offering predicted upvotes and comment rates. No model description, error range or backtest methodology is published for these predictions on any official page. Treat them as directional heuristics.
One small quality signal. The pricing page's Pro column opens its feature list with "Everything in Pro," where the conventional and presumably intended wording is "Everything in Basic." It is a trivial copy error with no functional consequence, but it is consistent with the picture of an actively-developed product maintained by one person rather than a polished enterprise offering.
There is a permanently free tier priced at $0 per month, and it is a limited version rather than a time-boxed trial. The official feature list describes it as offering limited subreddit metrics, limited themes, limited topics and limited semantic search, though the specific numeric thresholds are not published. Separately, several utilities sit entirely outside the account system: the trending subreddit listing, the Reddit category breakdown and the best-time-to-post analyser can all be used without signing up at all.
Basic is $15 per month or $180 billed annually; Pro is $83 per month or $990 billed annually. Annual billing offers essentially no discount — $180 over twelve months is exactly $15 per month, and $990 works out to $82.50 against a $83 monthly rate. Unless you have a reason to prefer a single annual charge, monthly billing costs almost the same and preserves flexibility.
Basic unlocks full access to topics, themes and metrics, allows up to five collections, and includes early access to new features. Pro adds unlimited collections, unlimited AI functions, Metrics API access and Collection API access. The collection cap is the clearest practical dividing line: if you are researching one product with a few adjacent segments, five is likely sufficient; if you are running several clients or product lines in parallel, it will not be.
The pricing page advertises a seven-day money-back guarantee and the terms of service describe a seven-day refund process handled by email, with approved refunds returned to the original payment method. However, the same terms document also states in its subscription clause that payments are non-refundable except as required by law. These two statements are not reconciled on the site. Given the ambiguity, confirm the refund terms in writing with the developer before subscribing, and treat the first week as your real evaluation window.
It depends which feature you are using, and the site gives two different answers. The methodology page states that new posts are indexed daily, trending content is updated hourly, and theme models are retrained weekly, with a default analysis window of thirty days. The bulk export page states that its database is refreshed monthly. So the in-product analytics are considerably fresher than the exportable subreddit dataset — worth knowing before you build a time-sensitive workflow on exports.
The export tool produces CSV files containing up to 10,000 subreddits per export. Each row includes the subreddit name, description, subscriber count, creation date, post activity and NSFW status. You can filter by keyword, subscriber count, NSFW status and creation date before exporting. Note that the terms of service prohibit systematically retrieving data from the service by automated means, so the export tool and the Pro tier's API are the supported routes for getting data out.
The methodology page describes four techniques. Themes come from NLP and clustering algorithms analysing post titles, keywords and contextual relationships. Engagement scoring rates posts on upvote ratios, comment velocity and performance relative to each subreddit's own averages. Posting windows are computed from thousands of posts with timezone and day-of-week patterns considered. Related-community detection works through user overlap, shared vocabulary and topic similarity. What the page does not publish is which models are used or how accuracy was validated, so testing the output against a community you know well is the practical way to judge quality.
Effectively none, and this is worth stating plainly. No listings were found on the major B2B software review platforms, no coverage by established technology publications was located, and no user discussion was found in developer or founder communities. The companion Shopify application shows a rating of 0.0 from 0 reviews, which two independent Shopify tracking services confirm. On Product Hunt the product is still marked beta with fifteen followers and no reviews. With a review sample size of zero, no quality judgement can be drawn either way from public evidence — you will need to evaluate it yourself via the free tier.
The About page identifies a solo indie developer named Xiang, who left a full-time engineering role at a large technology company in 2022. The terms of service do not name a registered legal entity, jurisdiction or company address, and the governing-law provision is left blank. For an individual paying $15 a month this is normal for the indie software category. For an organisation with vendor review requirements it is likely to be disqualifying, so check your own procurement rules before investing time in an evaluation.
There is a companion Shopify application called "SniffSub: Reddit Brand Monitor," published by a developer account named Olaka App with sniffsub.com listed as the developer website. It launched on 18 February 2025, is free, and searches Reddit posts, comments and shared links by product keyword or store domain, with filtering by subreddit and time range and hour-by-weekday posting heatmaps. It currently has no user reviews. There are no native iOS or Android applications; the main product is a browser-based web application.
