Daniel sent us this one — and it's basically the problem every self-hosted podcaster runs into about six months after they've escaped the platforms. You build your own pipeline on R2, you're saving a fortune, you own your feed, nobody can deplatform you. But you're flying completely blind on listener data. No geographic breakdown, no per-episode trends, nothing you could show a sponsor. And every analytics solution out there either wants to track your listeners like they're lab rats, or it just doesn't work with object storage at all. The question is: can you get lightweight, privacy-respecting, sponsor-ready numbers without building your own database from scratch? Or should you just switch to a storage provider that bakes this in?
This is one of those problems where the architectural mismatch is the whole story. Object storage — R2, S3, Backblaze B2 — serves files directly to listeners. The request hits Cloudflare's edge or Amazon's edge, not your server. Standard web analytics tools like Matomo or Plausible rely on JavaScript running in a browser or server-side request logging. Podcast apps don't run JavaScript. They don't render HTML. They issue an HTTP GET for an audio file and that's it. Your analytics dashboard sees precisely nothing.
The raw materials you're working with are... an IP address, a user-agent string that's probably lying, and a timestamp. That's the whole buffet.
Even the user-agent is borderline useless. Podcast apps often identify as generic HTTP clients. Apple Podcasts on iOS will sometimes just say "AppleCoreMedia" followed by a version number that tells you nothing about the device. Android apps are all over the place. You can't reliably distinguish "iPhone fourteen Pro running iOS nineteen" from "some Python script someone wrote to scrape episodes." Which means demographic data from user-agent parsing is a fantasy unless you're doing something much more invasive.
The fantasy is "I'll just look at my CDN dashboard and see how many people listened." What's the reality?
The reality is that a single actual human listener generates somewhere between one point two and one point eight HTTP requests per episode listen. That's from Podtrac's twenty twenty-five transparency report, and it's averaged across the industry. But that's the average. In practice, podcast apps aggressively cache audio. If someone downloads episode fifty once and listens to it ten times over two weeks, that's one request. Meanwhile, bots, pre-fetchers, and health-check scanners can generate three to five requests per episode without a human ever being involved. Your CDN dashboard shows raw request counts. It's not just inaccurate — it's misleading in both directions simultaneously.
You think you have more listeners than you do because of bots, but you're also undercounting actual listens because of caching. The number is wrong in ways that don't even cancel out.
And this matters now more than it did even two years ago, because podcast ad spend is projected to hit about four billion dollars this year. Even indie shows with a few thousand downloads per episode are starting to attract sponsors, and sponsors want numbers. They don't necessarily need Nielsen-grade audited data, but they need something they can look at and say "okay, this methodology is consistent and I can compare episode to episode.
Let's map the landscape. As I see it, there are three paths. Path one: proxy your requests through an analytics service that sits between the listener and your storage. Path two: use whatever analytics your storage provider gives you natively. Path three: switch to a provider that ships podcast-specific analytics out of the box. Let's start with proxies, because that's where most of the tools live.
Services like OP3, which stands for Open Podcast Analytics, and Podtrac, and historically Chartable before Spotify absorbed it — they all work on the same principle. You change the enclosure URL in your RSS feed so instead of pointing directly at your R2 bucket, it points at their endpoint. The listener's podcast app requests the file from the analytics service. The analytics service logs the request, then redirects the app to the actual file on your storage. The app follows the redirect, downloads the file, and the listener never knows the difference.
Walk me through the actual HTTP dance. I tap play.
Your podcast app sends a GET request to something like op three dot dev slash e slash your hyphen episode dot mp three. OP3 receives that request, logs the IP address, the user-agent, the timestamp, and any query parameters. It sets a session cookie if it's doing unique listener estimation. Then it returns a three oh two redirect to your actual R2 URL — something like your hyphen bucket dot r2 dot cloudflarestorage dot com slash episode dot mp three. The app follows the redirect and downloads the file from R2. OP3 never sees the actual download. It only sees the initial redirect request.
The difference between a three oh two and a three oh seven matters here?
It does, and this is where things get fiddly. A three oh two is a temporary redirect. Browsers and some HTTP clients will cache the redirect and go straight to the final URL on subsequent requests, which means your analytics service never sees repeat listens. A three oh seven is also temporary but the spec says the client must not change the request method and should re-validate on each request. Most podcast apps treat three oh seven as "ask the analytics server every time." But some older podcast clients don't handle three oh seven correctly and will fail entirely. So proxy services tend to use three oh two and accept that they'll miss some repeat requests, or they use unique per-request URLs with expiry timestamps to force re-validation.
Which breaks CDN caching entirely.
If every request hits the analytics proxy first and gets a unique URL, your CDN can't serve a cached copy. Every download comes straight from your origin storage. For a show with five thousand downloads per episode, that can increase your R2 egress costs by three to five times. R2 charges thirty-six cents per terabyte for egress, and the first ten terabytes per month are free, so for most indie shows the dollar amount is small — we're talking maybe a dollar eighty per terabyte instead of thirty-six cents. But it adds up as you grow, and it feels wasteful to burn bandwidth just to count downloads.
The proxy approach works, but it costs you in bandwidth and latency, and you're introducing a third-party dependency between your listener and your content. What about the privacy side? What are these services actually logging?
OP3 is the most privacy-forward of the bunch. It's fully open-source under the MIT license, and it processes over fifty million requests per month across all the shows using it. Their stated policy is that they strip IP addresses after twenty-four hours and never store them long-term. They log country-level geography derived from IP at the edge, episode slug, timestamp, and that's about it. No device fingerprinting, no cross-episode tracking beyond what's needed for unique listener estimation. And because it's open-source, you can verify what they claim.
Podtrac offers more demographic data, but it comes with strings. They use a tracking pixel in show notes — which only works if the listener's app renders HTML show notes, and many don't. They also require you to prefix your episode URLs with their redirect service. Their privacy policy is less transparent than OP3's, and they're a commercial entity that monetizes aggregate data. If your objection to invasive analytics is on principle, Podtrac is going to feel uncomfortable.
Chartable is now just Spotify wearing a trench coat.
Chartable was acquired by Spotify and is now fully integrated into their ecosystem. The data flows back to Spotify. If you're self-hosting specifically to avoid platform lock-in, routing your analytics through a Spotify-owned service defeats the purpose. You're giving them your listener data while trying to stay independent of them.
OP3 is the least bad proxy option from a privacy standpoint. But you mentioned unique listener estimation. How do you do that without cookies or device IDs?
This is the fundamental tension. The industry uses a few approaches, none of them perfect. One is IP plus user-agent fingerprinting over a rolling time window — say, twenty-four hours. If the same IP and user-agent request the same episode multiple times within twenty-four hours, count it as one unique listener. This breaks down with shared IPs, like everyone on a university campus or behind a corporate NAT, and it overcounts when someone switches from Wi-Fi to cellular. Another approach is setting a first-party cookie on the redirect domain, but podcast apps don't always respect cookies from redirect responses. A third approach is what OP3 does, which is a combination of IP, user-agent, and a hashed version of the listener's IP plus the date, rolled up daily. It's not precise, but it's consistent.
Consistent methodology matters more to sponsors than precision anyway.
That's one of the key misconceptions. Most podcast sponsors don't need unique listener counts down to the individual. They care about trend lines, relative episode performance, and order-of-magnitude accuracy. If your methodology says episode fifty got about three thousand downloads and episode fifty-one got about thirty-two hundred, that's useful even if the absolute numbers are off by twenty percent. The consistency is what lets sponsors see growth and engagement patterns.
We've established that the proxy approach works but costs you in bandwidth and privacy purity. Let's look at the alternative: what if your storage provider just gave you the data you need?
This is where the "switch providers" option comes in, and it's worth looking at even if you're happy with R2. R2's built-in analytics show total bytes served and total request counts, but they're aggregated at the bucket level. You can see "this month your bucket served two terabytes across four hundred thousand requests," but you can't break that down by episode. You can enable R2 Object Metadata to get per-object request counts, but that's still just raw download numbers — no geography, no unique listener estimation, no ability to distinguish a bot from a human.
It's better than nothing, but not by much. What about S3 server access logs?
If you're using R2 with an S3-compatible client — and most people are — you can enable access logging, which writes a log entry for every GET request to a separate log bucket. Each entry includes the IP address, user-agent, timestamp, request path, and HTTP status code. You can then pipe those logs into something like Axiom or ClickHouse or even just parse them with a Cloudflare Worker. This gives you raw material to build per-episode download counts and geo-lookup. But now you're storing raw IP addresses, which is a privacy liability. And you're building a log processing pipeline, which starts to feel like the "building your own database" thing the prompt specifically wants to avoid.
The prompt says: I don't want to reinvent the wheel and build my own analytics tracking database. Access logs plus ClickHouse is... that's building a database.
It's a small database, but it's a database. You have to manage retention, you have to handle geo-lookup, you have to build queries. For a podcast with two hundred episodes and a few thousand listeners, this is over-engineered.
Let's talk about the "switch providers" path. Backblaze B2 plus BunnyCDN keeps coming up.
This is the "it just works" option for podcasters who want analytics without engineering. Backblaze B2 is competitive with R2 on storage pricing — slightly different model but comparable for audio files. BunnyCDN charges a dollar per terabyte for egress and includes per-file analytics in their control panel: request counts, bandwidth usage, geographic distribution, cache hit ratios. No additional setup. You upload your files to B2, configure BunnyCDN as your pull zone, point your RSS feed at the BunnyCDN URLs, and you get analytics out of the box.
Geographic distribution meaning country-level?
Country and sometimes city-level, depending on the resolution of their geo-IP database. It's not perfect — geo-IP is always approximate — but it's good enough for "forty percent of our listeners are in the US, fifteen percent in Germany, ten percent in Australia." That's exactly the kind of data sponsors want to see.
The tradeoff being migration effort and slightly higher egress costs.
R2's egress is free for the first ten terabytes per month, which is enormous for a podcast. BunnyCDN charges from the first byte. For a show doing ten thousand downloads per episode at an average file size of fifty megabytes, that's about five hundred gigabytes per episode, so five dollars in BunnyCDN egress versus free on R2. It's not nothing, but it's also not ruinous. And you're paying for the analytics convenience.
There's also Fastly's object storage. I've been watching them.
Fastly is the dark horse here. Their object storage — they acquired it from Glitch, actually — offers real-time log streaming to your own endpoint. You can configure it to strip IPs before they leave Fastly's edge, so you get geo-data without ever storing PII. The cost is higher than R2 — about one cent per gigabyte for storage versus R2's zero point three six cents — but the analytics capabilities are first-class. For a podcaster who wants detailed data without building infrastructure, it's compelling.
We're still talking about migrating two hundred episodes of audio files, updating the RSS feed, making sure nothing breaks. That's a weekend project at minimum.
If you mess up the RSS feed, every subscriber's app will fail to fetch the next episode. Migration risk is real.
Here's where my mind keeps going. The prompt says "light touch," "don't want to build a database," "privacy-respecting," "robust enough for sponsors." And we've been dancing around a solution that checks all those boxes without switching providers or running a proxy service. A Cloudflare Worker sitting in front of the R2 bucket.
This is the approach I'd recommend for anyone already on R2 who wants analytics without building infrastructure. Here's the concrete architecture. You deploy a Cloudflare Worker on a route that matches your episode URLs — something like your domain dot com slash episodes slash asterisk dot mp three. Every request for an audio file hits the Worker first. The Worker extracts the country code from the request's CF dash IPCountry header, which Cloudflare provides for free on every request without you ever seeing the raw IP. It extracts the episode slug from the URL path. It extracts the cache status from the CF dash CacheStatus header. Then it logs a single event to Cloudflare Analytics Engine: timestamp, country code, episode slug, cache status. That's it. No IP address, no user-agent, no device fingerprint.
Then the Worker just... serves the file?
The Worker fetches the file from R2 using the Cloudflare cache API, sets a Cache-Control header with a long max-age — say, six hundred four thousand eight hundred seconds, which is a week — and returns it to the listener. The file gets cached at Cloudflare's edge. Subsequent requests for the same episode hit the cache and never touch R2 or the Worker's logging path again, unless the cache expires.
You're not breaking caching. The proxy problem we talked about earlier — where every request hits the analytics endpoint — doesn't apply here because the Worker is the CDN edge. The cache sits in front of the Worker.
And the cost is absurdly low. Cloudflare Analytics Engine charges five cents per million events ingested, with the first hundred thousand events free per month. For a show with ten thousand downloads per month, you're paying... let me do the math. Ten thousand events at five cents per million. That's zero point zero five cents. You'll never hit the billing threshold. The Worker itself runs on Cloudflare's free tier for up to a hundred thousand requests per day. You are paying essentially nothing beyond what you already pay for R2.
The code is what, twenty lines?
About twenty lines of JavaScript. You listen for a fetch event, check if the URL matches your episode pattern, extract the metadata from the request headers, call the Analytics Engine writeDataPoint method, then fetch the file from R2 and return it with cache headers. No database to provision, no logs to rotate, no geo-IP database to maintain.
You get country-level geography, per-episode request counts, and cache hit ratios. What don't you get?
You don't get unique listener estimation. You can approximate it by looking at unique combinations of country and episode over a time window in your Analytics Engine queries, but it's not the same as the IP-plus-user-agent fingerprinting that OP3 does. You also don't get any demographic data — but the prompt explicitly doesn't want that. And you don't get bot filtering out of the box, though you can add simple bot detection in the Worker by checking the user-agent against a list of known bot patterns and skipping the analytics logging for those requests.
For sponsor-ready numbers, you might combine this with a calibration step. Run OP3 on one episode, compare the numbers, and establish a multiplier.
That's exactly the approach. Run OP3 as a one-off audit for a single episode. OP3 will give you its best estimate of unique listeners, including its bot filtering and deduplication. Compare that to your Worker's raw request count for the same episode. If OP3 says a thousand unique listeners and your Worker logged fourteen hundred requests, your calibration multiplier is roughly zero point seven. Apply that to your Worker data going forward, and you've got numbers that are consistent, privacy-respecting, and defensible to a sponsor.
"Our methodology logs country-level request counts at the edge, with bot filtering, calibrated against OP3's open-source unique listener estimation." That's a sentence a sponsor will accept.
It's honest. You're not claiming precision you don't have. You're saying "here's what we measure, here's how we adjust it, here's why it's consistent.
Let me run through the privacy checklist, because I think this is where a lot of self-hosters get nervous and over-engineer. One: never store raw IPs. The Worker approach gets country from Cloudflare's header and never touches the IP. Two: use country-level geolocation only, not city. Three: don't set cookies, don't track across episodes. Four: be transparent — put a note in your show notes saying "we log country and episode for aggregate analytics, no personal data, here's how to opt out.
Point five: actually provide an opt-out. You can do this with a separate RSS feed URL that points directly to R2, bypassing the Worker entirely. Anyone who wants zero analytics can subscribe to that feed. Almost nobody will, but the fact that you offer it builds trust.
The "no analytics" RSS feed is one of those ideas that costs almost nothing to implement and signals something important. It says: we're collecting data because it helps us make the show better and talk to sponsors, not because we feel entitled to it.
This is where I think the philosophical stance in the prompt is worth taking seriously. The prompt says "I object on principle to invasive analytics technologies." That's not a technical constraint. That's a values statement. And the good news is, in twenty twenty-six, you don't have to choose between having data and having principles. The tooling exists to do both.
Unless you want dynamic ad insertion.
That's the elephant in the room.
Because DAI requires knowing things about the listener at request time — at minimum, their rough location for geo-targeted ads, and ideally some kind of session context for frequency capping. The industry is moving toward DAI as the standard for podcast advertising. If you're self-hosting with a lightweight analytics pipeline, you're locked out of that entirely.
DAI requires an ad server that makes decisions at request time based on listener data. The major platforms — Spotify, Apple Podcasts, YouTube — are building this into their infrastructure. For self-hosted shows, the options are either to use a third-party DAI service like AdsWizz or Triton Digital, which reintroduces the third-party dependency and the privacy concerns, or to accept that you're in the host-read sponsorship market, not the programmatic ad market.
Which is fine for most indie shows. Host-read ads pay better per episode anyway, and the relationship with the sponsor is direct. You don't need DAI for that. You need trend data and a media kit.
The media kit is where the Worker analytics plus OP3 calibration really shines. You can produce a one-page PDF that shows monthly downloads, top five episodes, geographic breakdown, and growth trend. That's what sponsors actually look at. They're not plugging into an API.
Alright, let me try to synthesize this into something actionable. If you're on R2 and you want analytics without building a database, deploy a Cloudflare Worker that logs country, episode slug, and cache status to Analytics Engine. It's twenty lines of code, costs essentially nothing, and gives you per-episode request counts with geographic distribution. For calibration, run OP3 on one episode per quarter to establish your bot-and-caching multiplier. For your media kit, apply that multiplier to your Worker data. Be transparent about your methodology. Offer a no-analytics RSS feed.
If you're willing to switch providers, Backblaze B2 plus BunnyCDN is the best "it just works" option. You get analytics in the control panel on day one, no code required. The tradeoff is slightly higher egress costs and a migration weekend.
There's also a middle path that I don't think gets enough attention. R2 plus a simple server-side analytics tool like Umami or Plausible, but configured to receive data from a Worker rather than from browser JavaScript. Umami in particular has a simple HTTP API — you can POST an event with a URL and a referrer and a country code, and it handles the dashboard. It's slightly more infrastructure than the pure Analytics Engine approach, but you get a nice UI without building anything.
Umami is open-source, self-hostable, and explicitly privacy-focused. It doesn't use cookies by default. It's designed for exactly this kind of lightweight, non-invasive analytics. The downside is you're now running a small server — a five-dollar VPS or a Cloudflare Tunnel to a Raspberry Pi in your closet. It's not zero maintenance.
It's also not "building your own analytics tracking database." It's installing an open-source tool and pointing a Worker at it. That's afternoon work, not weekend work.
And for podcasters who want a dashboard they can share with sponsors without giving them access to Cloudflare's console, Umami is a better fit than raw Analytics Engine queries.
Let me circle back to something you mentioned earlier about Apple's Private Click Measurement for podcasts. What's the status of that?
It's been discussed in the context of iOS twenty, expected later this year. The idea is that Apple would provide aggregate download counts to podcasters without exposing individual listener behavior — similar to their Private Click Measurement for web ads. It would work by having Apple Podcasts report episode-level download data to a privacy-preserving aggregation service, with differential privacy noise added. The podcaster gets "this episode was downloaded approximately X times in the US, Y times in Germany" without Apple ever being able to tie a download to a specific Apple ID.
Which would solve the problem for anyone whose audience is mostly on Apple Podcasts. But it's Apple-controlled, Apple-only, and not here yet.
If you're self-hosting partly to avoid platform dependency, relying on Apple for your analytics is... You've escaped Spotify's walled garden only to walk into Apple's.
The garden has nicer landscaping, but it's still a garden.
It's still a garden with walls. And Apple could change the terms or the granularity or the availability at any time. The Worker approach is yours. It runs on infrastructure you control, logging only what you decide to log, and it'll keep working as long as Cloudflare keeps running Workers, which is a bet I'm comfortable making.
Alright, I want to address one more thing from the prompt before we wrap. The prompt mentions "audited analytics" as a benchmark for robustness. What does audited actually mean in podcasting?
In the podcast world, "audited" usually means IAB-certified measurement. The Interactive Advertising Bureau has a set of standards for what counts as a valid download — minimum duration of file downloaded, filtering of bots and pre-fetches, deduplication of repeat requests within a window. Services like Podtrac and Chartable have gone through IAB certification. OP3 has not, though their methodology is transparent enough that you can evaluate it yourself.
When the prompt says "I know our current analytics aren't as robust as audited analytics," the gap is mostly about bot filtering and deduplication methodology, not about fundamental data quality. A well-configured Worker with bot filtering and an OP3 calibration pass gets you most of the way there.
For an indie show talking to sponsors directly, IAB certification is nice to have but rarely required. Sponsors who work with indie podcasts are evaluating the host, the audience fit, and the trend data. They're not running your feed through an IAB compliance checker.
To put a bow on this: the analytics dilemma for self-hosted podcasters is real, but it's solvable without compromising on privacy or building a database. The Cloudflare Worker plus Analytics Engine approach is the sweet spot for most R2 users. If you want even less engineering, switch to Backblaze B2 plus BunnyCDN. If you need a prettier dashboard, add Umami. And if you need unique listener estimation for a media kit, calibrate with OP3 quarterly.
The one thing I'd add: document your methodology. Whatever approach you choose, write down exactly what you log, how you filter, and how you estimate. That documentation is worth more to a sponsor than any dashboard screenshot, because it shows you've thought about this seriously.
Share what you build. The self-hosted podcasting community is still small, and there aren't that many reference architectures for privacy-respecting analytics. If you deploy a Worker that does this, blog about it, post the code, let other people adapt it. The more patterns we have in the open, the less every new podcaster has to figure this out from scratch.
Now: Hilbert's daily fun fact.
Hilbert: In sixteen ninety-three, the population of São Tomé and Príncipe was approximately fourteen thousand people — roughly the same number as the total recorded births in London that year, a statistical coincidence that early probability theorists cited when debating whether demographic patterns across unrelated populations revealed hidden universal laws.
...right.
Here's the open question I keep coming back to. As podcasting consolidates around dynamic ad insertion, are self-hosted shows going to be locked out of the ad revenue that actually scales? Or will privacy-preserving attribution — whether it's Apple's Private Click Measurement or something open-source we haven't seen yet — give independents a way to participate without becoming platforms themselves?
I think the answer depends on whether the ad market values precision or reach. If programmatic buyers demand per-impression tracking, self-hosted shows are in trouble. But if the market settles on aggregate trend data and host-read sponsorships as the premium tier — which is what's been happening — then the lightweight analytics approach is not just adequate, it's optimal. You get the data you need without the liability you don't.
If this episode saved you from building your own analytics database or convinced you that twenty lines of Worker code beats a weekend of log parsing, share it with a fellow podcaster who's still flying blind. This has been My Weird Prompts. Thanks to our producer Hilbert Flumingtop. Find us at myweirdprompts dot com or wherever you get your podcasts. We're back next week.
