eBay status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of eBay reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by eBay users through our website.

• Website Down (63%)
• Sign in (22%)
• Errors (15%)

Live Outage Map

The most recent eBay outage reports came from the following cities:

City	Problem Type	Report Time
Preston	Website Down	7 hours ago
Preston	Website Down	19 hours ago
Manchester	Website Down	1 day ago
Manchester	Website Down	1 day ago
Manchester	Website Down	1 day ago
Manchester	Website Down	2 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

eBay Issues Reports

Latest outage, problems and issue reports in social media:

• 
  🦊 (@whatthefox___) reported 7 minutes ago

  @kaetoceros Only people on eBay are paying that much and the sales are slowing down. People at my locals were selling them for 150

• 
  Ripper (@ripper0x) reported 9 minutes ago

  @anglio @eBay this is terrible

• 
  Zack Morris (@Palladin0) reported 10 minutes ago

  @packaddictsnw Never had an issue w eBay standard shipping once. Most likely buyer being shady

• 
  T-Bob Hebert (@TBob53) reported 10 minutes ago

  I remember getting this issue in a giant haul from eBay when I was younger

• 
  virgo_☀️♊️🌚 (@virgorising_gem) reported 11 minutes ago

  @WALKEMDOWNABE From the moment he couldn’t fix that bald patch , I knew eBay he’s isn’t have any money lol

• 
  Mr Hankey (@hivanchi) reported 13 minutes ago

  @eBay your platform is hurting buyers(im also a seller) The return process is broken. Theres no way to talk to people in the US, either by message or call. Some of your reps read the same script over and over again. Ask you for useless information that leads nowhere.

• 
  Psygnosevo (@ytevo79) reported 14 minutes ago

  Ive had an £80 offer from ebay for the first game (pictured) down from £100, but the manual has a tear mark so that puts me off. But I might pop back into that shop and get the sequel first as its a good price if its good condition.. Tis a shame only now Im interested in these!

• 
  Grey (@LeeGrey1105) reported 18 minutes ago

  @MaddiesMorgue SAME I NEVER THOUGHT ID HAVE HER, now I just have to try to hunt her boots and skirt down on ebay 😭

• 
  Derek Huntley (@BigDerek_KU) reported 18 minutes ago

  @eBay fix your app. I’m tired of getting live notifications when I have them turned off. Beyond annoying!

• 
  KamperKitty (@kamperkittyfn) reported 21 minutes ago

  why are they forcing AI down our throats? first google now ebay?? and you can't even disable it. it's an absolute joke!

• 
  Juan Tyrus (@GenesisFarmLLC) reported 23 minutes ago

  @tahiniok Yes it's called the AuraTone phone antenna. I you can pick one up on eBay from the manufacturer bypassing me and saving yourself some money. I don't want you to pay extra with me if money is an issue that is why it's open source. I have a post on who I go through somewhere.

• 
  Jay (@TheLobbyistGuy) reported 25 minutes ago

  Even in 2024, I had to track them down on eBay

• 
  norm19020 (@norm19020) reported 27 minutes ago

  @CardPurchaser Is anyone else having issues trying to make offers on @ebay this morning? I get a message saying “we are experiencing a technical problem”

• 
  𝕒𝕤𝕥𝕣𝕚𝕕 / 𝕝𝕚𝕝𝕒𝕔 💜♥️💚 purples lover (@purpofsecurity_) reported 27 minutes ago

  @toppatmin @astrrrx i just looked at ebay in hopes of finding anything on there but no😭 why did they take the store down i need that ******* amongi rhm poster

• 
  Olivia Kory (@oliviaakory) reported 27 minutes ago

  "All models are wrong, but some are useful." My friend @josephfwyer wrote the best summation I've seen of how we view the world at Haus. I'm saving you a click and posting his full substack article below. Please tell us where we are right, but more importantly, tell us where we are wrong! The Hitchhiker’s Guide to Marketing Measurement and Decisions A map of the Haus Analytics Platform The first two posts in Decisions and Data have been about what not to do: don’t trust statistical significance to make budget calls for you. Fair enough, but now we’re going to go from “stop doing that” to “do this instead.” So this post is the map. It’s the whole path from the data everyone starts with in marketing measurement to a system that recommends decisions and checks its own work. I’m really proud to say this is what Haus Analytics has built. We’ve walked down this intellectual map the last few years and now I want to walk you down it. Each stop on the path deserves its own deep dive, and those are coming. Today is just the strategic view of the problems and solutions. Last-click attribution has big shortcomings Speed-running last-click attribution real quickly: You click on a digital ad on a advertising platform. Later you buy something on the advertiser’s site, where a snippet of code reports the conversion back to the platform. Roll that up and you get the reporting most marketers watch daily. Suppose for a campaign there was a thousand clicks and a hundred tracked purchases on just a hundred dollars of spend. You’ve acquired those conversions for one dollar each. Streams of this kind of attribution data are flowing constantly and reporting ultra granular data. The problem is when you ask if this advertising spend “caused” those conversions. Around 2011, the economist Steve Tadelis was consulting at eBay and looked hard at their paid search spend. The ad in question sat at the top of search results page when you searched “eBay,” directly above the free organic link to eBay. His hypothesis was that people typing “eBay” into a search engine were largely already on their way to buying something at eBay. Radical, I know. He got his chance to look deeper when Ebay shut down brand keyboard bidding while negotiating with a major search engine. After 3 months of data had populated, he found that organic clicks rose to absorb almost all of the traffic the ads had been getting credit for. eBay was paying approximately $20M/yr to the search engine for customers it was already getting. He eventually published the results in a peer-reviewed journal. The word for what attribution measures is correlation. The word for how much customer activity advertising causes is “incrementality”: purchases that happen because of the ad and would not have happened otherwise. Modern targeting makes the gap between attribution and incrementality on digital channels worse because platforms can model each user’s propensity to buy your product and show ads to the people most likely to purchase anyway. Hold on to this: attribution is biased, but it is also fast and absurdly detailed. I’ll come back to it later. Experiments are where incrementality comes from If you want to know what customer activity an ad caused, you need a comparison: people who saw it versus equivalent people who didn’t. That’s called an experiment. Keeping track of individuals in experiments can be messy (although some ad platforms can pull it off) so a workhorse in this industry is the geographic experiment. You can randomly assign market areas in a country into receiving advertising (treatment) or not (control). Then you simply look at the difference in conversions between the two. Randomization removes that attribution correlational bias that tricked Ebay into lighting money on fire (and many, many other companies even today). On average, the differences between the treatment and control regions wash out, so the estimate is centered on the true incrementality. That sounds really simple, so why does Haus have all these experimentation scientists? It’s a lot of work to improve precision without messing up accuracy. Precision is the measure of how far off the estimate can be when it is off. Think of it like darts. A tight cluster in the upper right of the dart board is precise but inaccurate. A loose scatter centered on the bullseye is accurate but imprecise. Randomization gets your cluster centered on the bullseye. What the science team does all day is making the cluster tighter without dragging it off the bullseye. If you have a great experiment design and analytical model then you will get a precise estimate on the real number. At eBay, the real number for brand search keywords was close to zero. But that’s one company at one moment. I’ve seen brand search come back near zero for one advertiser and strongly incremental for another. You don’t know until you test. So everyone needs to test to find how much money they are leaking. As much as I love experiments, they have limitations. An experiment tells you the causal effect at the spend level you tested. That’s only one data point! You can’t trace a curve through one data point and you need a curve to allocate budgets because every channel eventually hits diminishing marginal returns. Causal MMM, debiasing the model Marketers have been fitting models to trace diminishing return curves for a long time. Media mix models (MMMs) trace your sales on your spend across multiple ad channels and let statistics tell you how much each is driving. In theory this gives you the full diminishing curve for every channel at once. In practice, MMM has two mortal flaws. Multicollinearity. Businesses tend to turn all their channels up and down together, so when sales change, the model struggles to tell which channel did it. Seasonality. Businesses spend the most going into Black Friday and Christmas season, which is exactly when people’s propensity to buy surges without needing ads. So were the gains in sales caused by the ads or the season? All models are wrong, but some are useful. MMM is very wrong, but very useful because it can go all the way to a budget recommendation when an experiment can’t. So we want to fix MMMs. We do that by stopping treating experiments and MMM as rival methodologies and instead merge them together. You ran a geo experiment and learned that at last quarter’s spend, a particular ad channel truly drove some specific number of purchases. We require the model’s curve for that channel to pass through the experiment data point. We call this experiment calibration. Pinning one channel’s curve to ground truth also disciplines the others, because the remaining sales have to be explained by the remaining channels plus organic demand. The seasonal bias gets squeezed out the same way. That’s causal MMM (cMMM): the curve-tracing power of an MMM, anchored to the causal truth of the experiments. And Haus refreshes it weekly instead of the traditional once-a-quarter-two-quarters-later read. Causal attribution, debiasing the daily feed A weekly model of whole channels is still too slow and too coarse for the person making changes every day. Remember what attribution had going for it: daily, granular, ad-level. Attribution is wrong, but what if how wrong is predictable? So apply a similar calibration move we did with cMMM but to Attribution. If your experiments show that only five percent of the purchases the channel claims are truly incremental, then discount its daily feed by 95%. Do that per channel. You can’t have experiments everywhere so let a model reason about how the causal correction shifts over time and across spend levels. Now the daily numbers marketers already watch become numbers they can trust. We also show the raw platform-reported figures next to the corrected ones, so you can always see what the feed said and what we did to it. Architect, where measurement becomes decisions Everything up to this point is measurement, and measurement adds no value unless it impacts decisions. We’re now going to get into the most exciting part of the Haus stack. We call it Architect. It takes the experiments, the causal MMM, and the causal attribution feed, and turns them into specific recommendations: move this much budget from here to there. Then, it measures what happened after you made the move and reports back within a couple of weeks. Did revenue improve? By how much? Continually updating its expectations and recommending again. Across the first companies acting on these recommendations so far, the average adopted Architect recommendation has improved conversions by 10%. Some companies have stacked changes and watched the gains compound. Architect earned the trust to make those changes by tracing the logic through the experiment, cMMM, and cAttribution data. That is why we’ve been working so hard to mak every layer underneath an automated and scalable system. An experiment alone is an isolated data point. A calibrated model alone is a forecast. The loop of recommend, act, verify, and update is the thing that turns marketing measurement from a reporting function into the situation room where strategic decisions are made. One more note, since the obvious question in 2026 is “why not just have an AI do all this?” Some companies will sell you exactly that right now. Personally, an AI reading raw attribution data inherits every bias in it and without each layer built, tested, debiased, and made explainable, you can’t trust if it’s going to work or know why it told you to move the money. The entire stack is what makes an automated recommendation trustworthy. Wrapping it up That’s the map! Attribution gives you speed and detail but with debilitating bias. Experiments remove the bias but are solitary data points. cMMM extends the causal truth to actionable curves. cAttribution pushes causal truth back into the daily feed. Architect reads the outputs of the whole stack and closes the loop by recommending and verifying decisions. In the coming weeks I’ll talk through some stops on the map with some illustrative math and examples of where things can go wrong. If you only take one thing from the altitude view, take this: never trust a marketing number that hasn’t been anchored to an experiment somewhere.

• 
  Sean (@firinzlol) reported 28 minutes ago

  @ThePokeMD It’s fine had a similar issue before. eBay rep basically said if it is a fake card then it will always get rejected so this “miscategorization” isn’t related to authenticity etc

• 
  Number 5 (@NumberFiveAlive) reported 29 minutes ago

  @clarkknowscards @phatjer300 @OnlyCharizard If you select “buyer requested cancellation” it will not ding your eBay account. All the other options from the drop down menu do. I lost top rated seller once for cancelling just 2 orders within a couple months.

• 
  johnny - Knicks 2026 Champion (@JOHNNYCATO000) reported 29 minutes ago

  @AmmarNasirMD I was just checking some out on eBay and they’re reselling for almost $400 but I don’t feel like overpaying for it honestly. I’d rather get some shirts down the line or the black Nba final hat

• 
  Chris Swanski (@ChrisSwanski) reported 32 minutes ago

  @CoCCollectibles Yeah. Still nice though. Don't feel bad. I owned THE Brady card for a minute in 2004. Got it for $400 from Ebay back when the photos were usually terrible. Seller said it was NM, but I couldn't tell from the pics. I trusted him. It wasn't NM, so I sold it at a loss.

• 
  Mike Palmer (シネフィル) 🎬 (@MikePalmerYT) reported 34 minutes ago

  Since July this year there are severe problems with orders from Japan and delivered from Japan Post in the European Union. I had three cases so far, and every parcel has been send back to Japan. A few days ago I ordered something very expensive of eBay.

• 
  auburn35 (@35auburn) reported 42 minutes ago

  @renoipgp That's true, I was just highlighting a new account that registered after the other eBay accounts were shutdown. Obviously very little effort from eBay to keep problem sellers off the site.

• 
  Eric (@GhostRunnerCard) reported 42 minutes ago

  @CCCollectorTCG I ship via eBay to third-party shipping companies almost daily. I've never had any concerns or issues with them. All the orders work out fine.

• 
  0xbonus (@coral_crypt) reported 42 minutes ago

  @anglio @eBay eBay’s broken, man. Your tracking should’ve been enough.

• 
  DogsBeAwesome (@Glasz_Half_Full) reported 45 minutes ago

  @mikelectricstuf @FedEx Before brexshit I used to have a small ebay store that sent parts overseas. Royal Mail had the same problem too.

• 
  Mr Hankey (@hivanchi) reported 46 minutes ago

  @paypal - leaving another message to be lost in wilderness of the internet. Had to do a chargeback for what seems to be a clear item not received issue. Never open these type of cases, but both @eBay and @paypal denied a simple case. Both their customer services are subpar.

• 
  Hysterian (@HysterianYT) reported 47 minutes ago

  @SonicBlueRanger The prices for them on eBay aren’t terrible in all fairness, given that not all that many copies were published. It just depends if you’re willing to spend anywhere from £30 to nearly £50 a piece for them.

• 
  Yianni (@fishytuxedo) reported 48 minutes ago

  @WristByAde I wonder about authenticity when I buy watches off eBay. I don’t spend much and I can’t imagine the oddballs I do buy are worth the trouble to counterfeit. I think the big issues I have are with “Frankenwatches” so watches that are put together with non-original parts

• 
  Jenny Jen Jennifer VCR Queen (@vcrqueenjen) reported 52 minutes ago

  @FoxgirlGoRawr Yep! Overall, I've not had terrible luck with returns given the amount of things I've sold on ebay but it does happen from time to time & for some cursed reason I've had a few returns in the past couple weeks. It's abnormal to have multiple in such a short time and I don't like it lol

• 
  Saul (@SaulSellsStuff) reported 1 hour ago

  Not sure who needs to hear this but you can spend $100,000+ a month just using Amazon as your source of inventory. You can do this at 40%+ ROI. You can beat section 3s and account health problems. You can sell your inventory on Amazon, Walmart, eBay, Whatnot, TikTok, or locally. If you’re an Amazon seller you can also get a complete P&L, cash flow analytics, ungating support, reimbursements, product monitoring, replen dashboards, FBM tracking, order tracking, and more. For free, then $3 a day. You can be as weird as me and do it all from your phone if you’d like. We are building the most complete software package publicly available. Taking no profits out. Doubling down on team and partnerships.

• 
  Kyle Harrison (@kwharrison13) reported 1 hour ago

  Maybe. Maybe you'd be fine without data centers. But let me ask you this. Do you use credit cards, debit cards, tap-to-pay, gas pumps, vending machines, parking meters, parking apps, ATMs, online banking, mobile banking, mobile check deposit, Zelle, Venmo, PayPal, Cash App, Apple Pay, Google Pay, splitting a dinner bill, autopay on your bills, payroll that isn't a paper check, direct deposit, digital 401(k), Robinhood, Coinbase, credit score checks, loan applications, mortgage applications, car loan approval at the dealership, insurance quotes, filing an insurance claim, e-filing your taxes, gift cards, store loyalty accounts, digital coupons, rebates, buy-now-pay-later, tipping on a screen, email, text messages, iMessage, WhatsApp, Signal, group chats, voicemail transcription, spam call blocking, FaceTime, Zoom, Google Meet, Discord, Slack, video calls with grandparents, phone number lookups, checking your data usage, paying your phone bill, two-factor codes, push approvals to log in, password managers that sync, "sign in with Google," resetting a forgotten password, digital IDs in your wallet app, gym check-in apps, apartment smart locks, hotel keys on your phone, office badge apps, patient portals, seeing your test results, booking a doctor's appointment, telehealth visits, prescription refill requests, the pharmacy knowing what you're on, insurance verification at the front desk, prior authorization, continuous glucose monitors, insulin pump apps, remote pacemaker checks, CPAP data reports, hearing aid apps, therapy apps, period trackers, fertility trackers, medical alert buttons for elderly parents, symptom checkers, finding an in-network doctor, the weather app, radar, hurricane warnings, tornado warnings, flood alerts, earthquake early warning on your phone, wildfire maps, smoke maps, air quality, pollen counts, Amber alerts, emergency alerts, road closure info, Google Maps, Apple Maps, Waze, live traffic, rerouting around a crash, transit apps, real-time bus and train arrivals, tapping your phone to ride the subway, Uber, Lyft, rental car reservations, Turo, bike share, scooter share, EV charging networks, paying for a charge, phone-as-car-key, remote start, finding your parked car, over-the-air car updates, in-car navigation, in-car voice assistants, stolen vehicle tracking, road trip planning, booking flights, checking in for a flight, mobile boarding passes, seat selection, flight status, rebooking after a cancellation, bag tracking, TSA PreCheck lookups, airport wifi, booking hotels, Airbnb, Vrbo, checking into a hotel, cruise bookings, theme park tickets, ride reservations, airline miles, hotel points, currency conversion, Amazon, all online shopping, order tracking, delivery notifications, returns and exchanges, price checks in-store, self-checkout, store apps, curbside pickup, Instacart, DoorDash, Uber Eats, ordering ahead at a restaurant, OpenTable, Resy, waitlist texts, QR code menus, tipping on delivery, subscription boxes, eBay, Etsy, Facebook Marketplace, Craigslist, Poshmark, StockX, Ticketmaster, StubHub, getting into a concert with a phone ticket, Alexa, Siri, Google Assistant, smart thermostats, video doorbells, security cameras, alarm monitoring, smart locks, smart lights, robot vacuums, garage door openers, baby monitors, pet cameras, automatic pet feeders, GPS pet collars, smart sprinklers, smart fridges, app-connected air fryers, cloud printing, printer ink subscriptions, routers you manage from an app, checking if you left the stove on, iCloud, Google Photos, every photo you've taken in ten years, Dropbox, Google Drive, OneDrive, shared albums, phone backups, setting up a new phone, notes apps, calendars, contact syncing, reminders, to-do apps, document scanning, e-signing a lease, Netflix, YouTube, Hulu, Disney+, Max, Prime Video, Twitch, cloud DVR, on-demand cable, Spotify, Apple Music, podcasts, audiobooks, Kindle books, library ebook borrowing, online multiplayer games, matchmaking, cloud saves, game downloads, game patches, single-player games that phone home for a license check, Steam, PlayStation Network, Xbox Live, Nintendo Online, Roblox, Minecraft servers, fantasy football, sports scores, sports betting apps, movie tickets, Google search, Wikipedia, ChatGPT, Claude, every other AI app, Instagram, TikTok, Facebook, X, Reddit, LinkedIn, Snapchat, Pinterest, dating apps, Yelp reviews, Google reviews, news sites, Substack newsletters, blogs, forums, checking if a business is open, looking up a phone number, recipes, translation apps, Duolingo, Google Docs, Sheets, Gmail, Outlook, Microsoft 365, Teams, Notion, Figma, Canva, shared calendars, scheduling links, VPNs into work, remote desktop, timeclock apps, shift scheduling apps, requesting time off, expense reports, job applications, LinkedIn recruiters, video interviews, Canvas, Blackboard, checking your kid's grades, school lunch accounts, attendance notifications, online homework, Khan Academy, Coursera, FAFSA, student loan portals, tutoring apps, Fitbit, Apple Watch health data, Strava, Peloton, sleep tracking, smart scales, calorie tracking, meditation apps, workout apps, DMV appointments, renewing your license online, paying a parking ticket, jury duty portals, checking your property tax bill, utility accounts, outage maps, paying rent through an app, HOA portals, storage unit access codes, wedding registries, baby registries, funeral arrangements, Ancestry, 23andMe results, church livestreams, volunteer signups, or GoFundMe? If you said yes to ANY of those then you do, in fact, NEED data centers.