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 (14%)

Live Outage Map

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

City	Problem Type	Report Time
Preston	Website Down	2 hours ago
Preston	Website Down	11 hours ago
Thonon-les-Bains	Sign in	14 hours ago
Preston	Website Down	17 hours ago
Thonon-les-Bains	Sign in	1 day ago
Preston	Website Down	1 day 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:

• 
  RedxDevilx44 (@RedxDevilx44) reported 4 minutes ago

  @Card_Shop_Guys ebay takes down one ad and all the cringe YouTubers run with it. wild

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  Just Looking By The Way (@JustLooking_BTW) reported 11 minutes ago

  @ChevanceLo1 @TrevorAllenMD @YugiMuto91 Literally just takes a eBay listing & dropping it off at an ups or etc. to sell this thing, like you can’t be this slow to think winning the tournament was what he was referring too

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  Cori (DemShenaniganss) 🦝 | #TDST ⚡️ (@DemShenanigans) reported 11 minutes ago

  @Artemishowl_ Former SH staff circa early days (2010s) here: Can confirm, ebay kept the **** parts and got rid of the people like us who made sure that the same or better upgrades were offered. Afaik even shut down the main corporate office thay used to be in CT

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  SUPERmeme 🦝 (@MEMEnalu808) reported 12 minutes ago

  no one could have expected this to happen when a 1/1 rare gold jimothy card sold on ebay for over 20k?? almost 19mil mc just 2 days ago to currently 7mil but if you truly believe in the **** you keep buying more as i did all the way down because the bottom is in on my $jimothy we will rise again ☝🏽

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  Grey (@LeeGrey1105) reported 12 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 😭

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  Fitzzzy 🏴‍☠️ (@ShaunFitzzzy) reported 14 minutes ago

  @Psycho0111 It all comes down to if there is any material info out there. With everything around eBay it might be hard to get around the safe harbor laws Also, I’m not sure if the pre earnings release counts, we get the full thing next week

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  Tyler (@SeeifIhaveit) reported 16 minutes ago

  Beware of this eBay seller. Purchased a J Baez lot when he was hot and I had to cancel and refund him because it sold on Mercari probably 15 minutes prior. He purchased it literally as I was taking down the eBay listing. I messaged him and apologized and refunded him immediately. Well… he left negative feedback and said he never got the cards.

• 
  Danny Bennett (@RealDannyB) reported 16 minutes ago

  LONG READ: Off-brand post, but as a longtime @Nike shareholder, this needs to be said. Nike used to be a company you could invest in and trust to run itself. Now the stock is trading in the high $30’s, a fresh 12-year low, down roughly 76% from its all-time high of $163.63 in November 2021. This didn’t happen overnight. It’s been a slow burn downhill. In 2020, Nike handed the company to John Donahoe, a former eBay and ServiceNow executive with a consulting background and no real history in sneakers or sport. The stock hit its all-time high under him in November 2021, but that wasn’t a product win, it was pandemic tailwinds. Lockdown spending and a digital sales boom carried the whole brand. Donahoe leaned into it, pulling back from wholesale partners like Foot Locker and Amazon to chase direct-to-consumer sales. It worked while the boom lasted. The second it ended, that same move handed Hoka, On, and New Balance the shelf space to take over. He spent the rest of his tenure cutting costs and squeezing profit out of old product instead of investing in anything new. On June 28, 2024, Nike’s stock fell 20% in a single day after weak earnings and guidance for a 10% revenue decline, its worst trading day in 44 years as a public company. It never really recovered. Nike has now erased roughly $200 billion in market value since its 2021 peak, the largest drawdown in the company’s history. Even Jordan Brand, the crown jewel, isn’t safe. Revenue fell from $8.7 billion in 2024 to $7 billion in 2026, two straight years of decline. Hoka went from $153 million to $2.6 billion in sales in eight years. On grew 285% in three years. Nike handed them the shelf space and gave people no reason to buy new. Stop putting bean counters and consultants in charge of a brand people love. Put product people back in charge. Open the SNKRS app right now. The Jordan 4 “Rare Air” Tour Yellow drops September 5th, a 20-year-old colorway from 2006, priced at $220. A shoe that’s already been made once, repackaged, made cheaper than its original release and marked up. Stop chasing what flippers on StockX think a re-release is worth. Price product for the people who actually want to wear it. Every dollar Nike hands to resale culture is a dollar it’s not making itself, and a customer it’s not building loyalty with. Nike has an entire team of engineers dedicated to fighting bots on SNKRS, blocking billions of fake entries every month just to protect launches like this. You wouldn’t need bot protection if you weren’t manufacturing the scarcity yourself, especially when a good chunk of these same releases end up marked down in outlets and clearance stores months later anyway. So what was the artificial scarcity even for? Elliott Hill spent 32 years at Nike, most of it running product, commerce, and marketing, before retiring in 2020. Now he’s back, cleaning up a mess a consultant-era CEO left behind. Even so, JPMorgan just downgraded the stock again this month, warning the pain from the turnaround won’t stabilize until fiscal 2028. Bringing back someone who actually gets the brand is the right move. But, it shouldn’t have taken a 76% collapse to get there.

• 
  StuArt  (@StuHack73) reported 16 minutes ago

  @TheRealBonJavi @johnshanks1 They’re worth about as much as a Jim'll Fix It badge on eBay, bin them.

• 
  Teddy.com (@teddy__com) reported 16 minutes ago

  @Bgillz7 Spoiler alert… they have lost money now. They used it to buy BTC, eBay, and treasury notes. Down $100m on BTC. Even on eBay. Up maybe $100m or so on T notes. $350m in cash out the door and an extra 7m shares. $400 in value lost on $1.4b in notes that were 0% interest.

• 
  Tel3 (@Tel3133186) reported 17 minutes ago

  @bertie4lily Can't say I've had problems with Ebay, usually cheaper and free postage.

• 
  Blitz  (@PeriBlitzV2) reported 17 minutes ago

  @Digggon the type of game the MC of a creepypasta would buy off ebay, see charmander get brutually decapitated in it, and then shurg it off thinking it was just a weird glitch

• 
  Eric McCurry (@eric_mcCurry) reported 18 minutes ago

  @RussellCartwr18 Thanks man. I'm having trouble finding the robe by itself on eBay, Mercari, and Facebook marketplace. Might try Craigslist too

• 
  Reincarnated Raccoon (@bpdraccoon) reported 20 minutes ago

  @SaffronOlive Alot of this is sellers buying from themselves to inflate ebay has alot of thay issue rn trying to change the prices on things

• 
  Kyle Harrison (@kwharrison13) reported 23 minutes 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.

• 
  Pokémon Deals, Restock and Alerts (@PokemonRestockr) reported 24 minutes ago

  @J_Vetter14 @Stassibaby12 @Pacho9_3 I assume it was ebay to took the seller down, keep me updated on what happens. Wild times

• 
  Elementa Graphics (@elementaGFX) reported 25 minutes ago

  I’ve started a daily series on projects that already have something real but the presentation still hasn’t caught up to its potential. Project Breakdown #72 — @ChaseCardApp ChaseCard is a live aggregator. You name the card. Set, language, condition, budget. They watch every marketplace. Beta is open. Alerts are already going out. Umbreon 38% under. Gengar 45% under. That is a real loop. The page does not show the loop. It shows a still of a deal and a link to apply. A collector already hunting grails will get it. A stranger sees another TCG promo card. Two problems. The watch is invisible. The product is not “Umbreon is cheap.” The product is ChaseCard catching it across shops while you sleep. That is motion. A price still is what eBay already posts. Motion is the alert hitting, the listing opening, the card matching the chase. The feed is one template. Photo. Percent. Link. Fine for proof. Weak as a brand. A ChaseCard frame should be the chase itself. Card. Target price. Hit. Same rails every alert. Then the beta link has a face. They already have the deals. The page should look like the watcher, not like the listing. See you on the next one.

• 
  WesleyTech (@WesleyTech) reported 26 minutes ago

  eBay is the king of terrible customer experiences. 1 example of many: They make you solve a captcha in order to LOG OUT of your account on the web app 🤬

• 
  PokéCardsDaily (@PokeCardsDaily) reported 29 minutes ago

  @Oscvr9_ Hello! Best way to price sealed is to refer to eBay last solds for sealed - do not count any unsealed. Often I find issue with sold items under “best offer” and the listed price being the sold price. For this reason I always use 130point to get most accurate prices w/ offers.

• 
  Mr8000 (@mr8mil) reported 31 minutes ago

  A few weeks back I bought this card from a tip on @enzo_tcg discrod. This card comes from the Blue Trial Deck of FW from 2023, basically one of the first leader cards of all FW. Hard to find in EN, this is the only version I could get my hands on EBay. Even if this card does not meet expectations down the road, I love the “vintagy” look of it. Source and user name: dissociated, thanks for bringing this into attention. Appreciate it!

• 
  Byron Vallis (@Byronnn6) reported 32 minutes ago

  @calvinfroedge Free shipping and eBay fees will knock this down to like $4.5 my friend. Better luck next time

• 
  𝕒𝕤𝕥𝕣𝕚𝕕 / 𝕝𝕚𝕝𝕒𝕔 💜♥️💚 purples lover (@purpofsecurity_) reported 32 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

• 
  Sweethoneyxd (@Gods_sunset) reported 37 minutes ago

  @JRho_11 Starting the video w/ that musty *** helmet he prob got off eBay is insane. Literally hit record while putting the helmet in frame andddd putting his top down. Mean time he prob circling an empty parking lot

• 
  Nigel J Bevans Photo (@NJBDP) reported 40 minutes ago

  @hmefsww Hi, unfortunately I closed my website store down as they were charging a ridiculous amount to sell things. To keep prices reasonably I had to find an alternative and only have eBay at moment. I could do it via paypal cutting out ebay if you would like?

• 
  thanh (@thanhsells) reported 40 minutes ago

  @SeeifIhaveit Fifteen minutes while you were taking the eBay listing down is brutal. That's the one that gets you.

• 
  J & C Collectibles (@JC_colleccoast) reported 40 minutes ago

  🚨 SOS Card Peeps! Need some help ASAP please. 🚨 I received an offer on My Acuff 1/1 card on eBay. The guy offered me a fair price and is now negotiating with me…I’m totally fine with that, but in our chat (on eBay) the user name keeps changing…when I click on the first name at the top of the chat it seems like he is a legit seller/buyer. When the other name pops up, it says “error” Is someone trying to scam me or is this some kind of issue with eBay’s messaging?!? Thanks in advance!

• 
  Olivia Kory (@oliviaakory) reported 41 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.

• 
  Clazziquai (@Clazziquai_Nico) reported 48 minutes ago

  @TheNTDOfan Might have to hunt one down on ebay or Vinted

• 
  jay (@NyzTCG) reported 48 minutes ago

  @MK5Cards i constantly ship out 25-50 dollar orders in pwe with no problem very rarely ebay doesnt allow the option but yh

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  alex bud (@alex_budimir) reported 60 minutes ago

  Anyone else notice how much better listing has gotten? I hadn’t listed cards on eBay in a while because of the fees and how long it took. Used to be 3–5 minutes per card. Listed a few Laz and Rainiel cards today and the AI had me down to about a minute each. @CardPurchaser