Here’s something odd: Bluesky has around 40 million total users. Threads has somewhere between 275 and 400 million monthly active users. That’s a 7-10x difference. Yet publishers and businesses keep saying their smaller Bluesky audience delivers better results than their massive Threads following.
This isn’t about community magic or mysterious vibes. It’s about architecture, business model alignment, and what happens when you build for engagement quality instead of engagement quantity. If you’re making platform decisions based on follower counts, you’re measuring the wrong thing. Understanding these metrics is crucial when evaluating centralised versus decentralised social platforms for your organisation.
Let’s look at the numbers, the mechanics, and what this means for where you should actually be building an audience.
What is the actual user base size difference between Threads and Bluesky in 2025?
The scale gap is real and big. Threads hit 115 million daily active users by June 2025. Monthly active users are reported at 275 million, 350 million, or 400 million depending on which Meta disclosure you read and when they said it. Bluesky reached 40 million total users in October 2025, with roughly 4.1 million daily actives.
Threads had a massive head start. Instagram integration meant 150 million downloads in just 6 days and 100 million users within 5 days of launch in summer 2023. Bluesky grew the hard way—organically, from invite-only beta to where it is now.
But here’s the thing. 70% of Threads daily users also use Facebook. Another 51% use Instagram. That’s not just ecosystem advantage—it’s attention being split across multiple apps. Users treat Threads as one app among several Meta properties, not the place they go.
Both platforms are US-dominant, with roughly 42% of Bluesky users from the US, followed by Brazil at 11% and the UK at 7.5%. Demographically, the user bases aren’t that different. The difference is in what those users actually do.
How does Bluesky’s engagement quality differ from Threads despite having fewer users?
You need to separate engagement quality from engagement quantity. Quality is time spent per post, link click-through rates, reply depth, and whether people actually do something with your content. Quantity is just raw likes and follows.
Bluesky users spend 10 minutes and 35 seconds per visit, comparable to Facebook’s 10 minutes 57 seconds and X’s 12 minutes 1 second. They visit about 8 pages per session, with a bounce rate of 37.8%. That’s depth, not drive-by likes.
When people post identical content to both platforms, Twitter/X got more likes, but Bluesky sparked better conversations. Bluesky posts average 21 interactions while Twitter/X gets around 328, but that’s not the story. The story is that people click on shared links more often on Bluesky and conversations go deeper.
Publishers see this clearly. Referral traffic tells the real story about whether your audience actually cares about what you’re posting. We’ll get to the specific numbers in a moment, but the pattern is consistent: smaller Bluesky audiences deliver better business outcomes than larger Threads audiences.
During major events—the World Series, election night 2025—Bluesky sees 1.5-2x engagement increases. That’s real-time gathering place behaviour. People go there when something’s happening because they expect the conversation to be there.
Why do Threads users barely click on links compared to other social platforms?
Adam Mosseri runs Threads. He stated it plainly: the platform “doesn’t place much value on” links because “people don’t like and comment on links much.” That’s policy, not observation.
Here’s the data. With 115 million daily active users, Threads generated 28.4 million outbound referrals in June 2025. That’s 0.25 clicks per user—less than one click per four daily users.
Chartbeat analytics reveal that Threads accounts for less than 0.1% of publisher referral traffic. For context, Facebook drives 2-3% of publisher traffic. Google Discover drives 13-14%. Threads, with its massive user base, barely registers.
This is circular logic in action. The algorithm deprioritises link posts. Low visibility leads to low clicks. Low click data justifies continued suppression. Social media consultant Lia Haberman put it like this: “People just got trained not to look for them, not to include them, not to think about them.”
Meta’s business model explains this. Ad-supported platforms make money by keeping you on the platform. Every external link is a user potentially leaving. The algorithm reflects business priorities, not user preferences.
Compare this to Bluesky’s chronological feed, which treats links the same as any other post. No suppression. No deprioritisation. Just time order. The platform doesn’t have an ad model creating friction with external links.
What makes Bluesky a high-signal community platform versus Threads?
High-signal means substantive conversations with low noise. Understanding how federation affects community culture helps explain this difference. The AT Protocol’s design allows what Bluesky COO Rose Wang calls “cities within our state”—diverse communities with distinct norms and cultures.
Custom feeds are how it works. Users and developers create feeds that filter for specific topics or interests. Want a feed just for developer discussions? Academic research? Journalism? You can create it or subscribe to someone else’s. This enables niche communities to form around shared interests rather than everyone getting one algorithmic timeline.
User-driven moderation means communities set their own standards. You choose which moderation rules to follow, which communities to participate in. Culture forms from the bottom up rather than through top-down policy.
The result is what people call a “high-signal environment”. Users opt in for conversation. Discussions have depth. Subcultures form fast. People say they’re “meeting someone online again” and “learning something”—behaviours that come from idea formation, not just content consumption.
Threads has a centralised algorithmic feed that mixes all content types. It’s optimised for engagement quantity—clicks, time on site—rather than quality conversations. The “competitive, performance-focused” feel versus Bluesky’s “conversational, calm” environment reflects different architectural choices and business models.
How does Bluesky’s chronological feed affect engagement patterns?
Chronological feeds make timing matter. Your post appears in strict time order, most recent first. No algorithmic curation deciding who sees what when.
Bluesky users engage most actively between 1 PM and 3 PM, with interaction rates 4-5 times higher than early morning or late night. Posts between 11 PM and 6 AM get the lowest engagement because most users are offline. Content posted when your audience is offline gets buried before they see it.
RecurPost analysed more than 2 million posts and found consistent patterns. Strong engagement runs from 9 AM to 6 PM, when users interact 2-3 times more frequently than slower periods. Geography matters: US users peak 1-3 PM Eastern, Brazil 12-2 PM BRT, UK 1-3 PM GMT with Friday at 6 PM showing impressive reach.
The trade-off is predictability versus reach. Algorithmic feeds might surface your post hours later or give older content viral reach. Chronological feeds are straightforward—post when your audience is active, maintain regular presence, and you’ll stay visible.
But you gain transparency. Users know why they see content. It’s recent and they follow you. No opaque algorithmic decisions. That builds trust that algorithmic curation can’t match.
Why does Bluesky drive better referral traffic despite having fewer users?
The comparison is straightforward. Threads has 115 million daily users generating 28.4 million referrals—that’s 0.25 clicks per user. Bluesky doesn’t publish equivalent numbers, but publishers report better outcomes. The difference is structural.
No link deprioritisation means your posts with links get the same visibility as posts without links. The chronological feed doesn’t penalise external content. Followers are more likely to click links before diving into work, and morning readers often return later to share comments.
Business model alignment matters. Bluesky doesn’t have an ad-supported model. There’s no conflict between keeping users on-platform and letting them follow external links. The platform doesn’t make money by maximising time on site.
Audience intent is the other piece. Bluesky’s high-signal environment attracts users who want substantive content. Nearly 4 in 10 Bluesky users rely on the app for news. That’s an audience actively seeking information that requires off-platform reading.
For publishers, the calculation is simple. A thousand engaged users who click and convert outperform ten thousand passive scrollers. When your business model depends on driving traffic to your site, per-user engagement quality matters more than total reach.
What is the difference in community culture between Bluesky and Threads?
The “lifeless” descriptor for Threads comes up often enough to matter. Algorithmic curation prioritises engagement quantity over quality. Link suppression reduces substantive content sharing. Attention gets diluted across Meta’s properties.
Bluesky becomes a destination during major events. “If you wanted to see where the World Series conversation was happening, it was on Bluesky”. During major events, engagement surges significantly. That’s gathering place behaviour—people go where they expect the conversation to be.
Federation’s cultural impact comes from enabling diverse communities with their own norms. Custom feeds create the “cities within our state” model. Communities set standards matching their values. You choose which communities and standards to participate in.
Users describe Bluesky as “quieter and more thoughtful”, with intentional replies and discussions that don’t disappear instantly. Threads conversations have shorter lifespans with constant attention shifts as the algorithm surfaces new content.
Rose Wang notes, “There’s still a yearning for people to gather … and to feel that connection and bond.” Many users are leaving platforms where one person can “change the culture overnight”. They want community stability that doesn’t depend on a single company’s whims.
How should you evaluate platform engagement quality versus quantity?
Stop measuring follower counts and raw likes. Those are vanity metrics. Measure what matters: time spent per post, link click-through rate, reply depth, referral traffic, and conversions.
Wang puts it clearly: “If you’re looking for the top-line numbers of followers and views … it is hard to compete, but what matters is a strong connection with a smaller group of people because that passion is actually more important.”
Choose Bluesky for meaningful interaction, ownership of your audience relationship, long-term community building, and when you need to drive referral traffic. The platform suits tech, finance, and digital privacy industries seeking niche, engaged audiences.
Choose Threads for exposure, algorithm-powered visibility, and reaching existing Instagram audiences. It works for mainstream brands that can repurpose Instagram content and participate in trending discussions without expecting traffic back to their site.
Many people use both strategically—Threads for exposure, Bluesky for genuine engagement. That’s fine, but test it properly. Post identical content to both platforms. Measure engagement quality metrics, not vanity metrics. Track CTR, time spent, and conversions. See which platform delivers actual business outcomes.
If you’re building tools or integrations, API capabilities supporting engagement become essential for understanding what’s technically feasible. API maturity, data portability, and moderation control are important considerations. AT Protocol’s decentralised architecture prevents single-point-of-failure risks and algorithmic manipulation. You won’t wake up to find the API access you depend on got shut down overnight.
Business model alignment is the final consideration. For a comprehensive analysis of these trade-offs, our strategic framework for platform selection provides decision criteria tailored to technical organisations. If your strategy involves content marketing and driving traffic to your own properties, platforms with ad models that suppress external links work against you. You’re fighting the algorithm’s business priorities.
FAQ
Is Bluesky really better for engagement than Threads even though it’s smaller?
Yes, if you measure the right things. Threads has 7-10x more users, but Bluesky drives higher time spent per post, better link click-through rates, and stronger publisher referral traffic per user. For business outcomes like traffic and conversions, Bluesky’s smaller but more engaged audience often outperforms Threads’ larger passive user base.
Why does everyone say Threads feels lifeless compared to Bluesky?
The algorithmic feed deprioritises links. Attention gets split across Meta’s properties. There’s a lack of community-driven spaces. Bluesky’s chronological feed, custom community feeds, and user-driven moderation create distinct cultures with authentic conversations. During cultural moments like elections or sporting events, Bluesky sees engagement surges versus Threads’ passive consumption patterns.
What’s the deal with Threads not sending much traffic despite having millions of users?
Adam Mosseri said the platform “doesn’t place much value on” links. With 115 million daily users generating only 28.4 million referrals, that’s 0.25 clicks per user. Threads accounts for less than 0.1% of publisher referral traffic according to Chartbeat. The circular logic works like this: algorithmic suppression reduces link visibility, low visibility means low clicks, and low click data justifies continued suppression.
Can I use both platforms effectively or should I choose one?
You can use both strategically—Threads for exposure, Bluesky for genuine engagement. Use Bluesky for driving referral traffic, building high-signal professional communities, and engaging technical audiences. Use Threads for broad brand awareness leveraging Instagram’s 2 billion users and Meta’s ad infrastructure. Test identical content on both and measure engagement quality metrics, not vanity metrics, to determine actual ROI.
How does Bluesky’s AT Protocol affect engagement compared to centralised platforms?
AT Protocol’s decentralised architecture lets users choose feeds, moderation standards, and data hosting. This creates “cities within our state” with distinct community cultures. User choice drives authentic engagement—people participate in communities matching their values. Centralised platforms use one-size-fits-all algorithmic curation optimising for engagement quantity rather than quality. Decentralisation also prevents single-point-of-failure risks and algorithmic manipulation.
What engagement metrics should developers track when building presence on these platforms?
Track link click-through rate, time spent per post, reply depth showing substantive discussion versus surface reactions, referral traffic and conversions (off-platform actions driven by posts), and engagement rate normalised by follower count. Avoid vanity metrics like follower count and raw like counts that don’t correlate with business outcomes.
Why are publishers switching to Bluesky from Threads?
Publishers prioritise referral traffic and conversions over follower counts. Threads accounts for less than 0.1% of publisher referral traffic despite its massive audience. Bluesky’s lack of link deprioritisation, high-intent audience, and community culture valuing in-depth content create better outcomes. The Boston Globe and other publishers report that Threads trails significantly versus Bluesky in traffic and conversions. When your business model depends on driving traffic to your site, a platform generating 0.25 clicks per daily user underperforms one with higher per-user engagement.
Does Bluesky’s chronological feed mean my posts disappear faster?
Posts scroll out faster than algorithmic feeds might surface old content. But you gain predictability—posting during peak windows (1-3 PM EST for US audiences) maximises visibility. Consistent posting maintains presence. Algorithmic feeds offer less control; posts might surface hours later or not at all based on opaque criteria. Users know why they see content (recency and follows) versus mysterious algorithmic decisions. That transparency builds trust.
How do custom feeds on Bluesky improve engagement quality?
Custom feeds act as specialised communities with distinct characteristics. Users create feeds filtering for specific topics, enabling niche community formation around shared interests. This creates high-signal environments where conversations match user intent. Threads’ centralised algorithmic feed mixes all content types, diluting signal with noise. Custom feeds also let users choose moderation standards matching their values, fostering authentic community culture rather than platform-imposed norms.
What’s the impact of Meta’s cross-platform integration on Threads engagement?
Instagram integration enabled rapid user acquisition (150 million downloads in 6 days) but creates attention dilution. Users treat Threads as one of several Meta apps rather than a primary platform. This explains why users divide engagement across multiple Meta properties. Bluesky users demonstrate higher platform commitment and concentrated engagement.
How does link deprioritisation create a self-fulfilling prophecy on Threads?
Algorithmic suppression creates the low engagement that justifies continued suppression. Publishers testing identical content across platforms see higher CTR on Bluesky where links receive equal treatment, proving the issue is policy-driven, not user preference.
What role does decentralisation play in building trust on Bluesky?
AT Protocol gives users control over feeds, moderation, and data hosting, reducing single-point-of-failure risks and algorithmic manipulation concerns. Users trust they won’t face sudden algorithm changes destroying their reach or arbitrary moderation without recourse. The “cities within our state” model allows communities to self-govern with transparent rules versus centralised platform policies applied inconsistently. This architectural transparency and user sovereignty align with open-source principles and data ownership values.
