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 (65%)
• Sign in (22%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Berlin	Sign in	8 minutes ago
Goiânia	Sign in	3 hours ago
Preston	Website Down	4 hours ago
Preston	Website Down	9 hours ago
Preston	Website Down	19 hours ago
San Antonio	Sign in	21 hours 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:

• 
  Teddy.com (@teddy__com) reported 5 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.

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  Driftbound - Father Son Card Slinging (@dRIFTBOUNDtcg) reported 7 minutes ago

  35 T1 box transactions on ebay yesterday. One sale for a raw Miss Fortune as the auction ended at $7k Is this higher or lower than you expected? I think it will settle down significantly as more supply hits the market. By next week we might have 30 auctions going on at once, and inevitably some will fall between the cracks, going for well below the established market.

• 
  Regan Reyzja (@ReganReyzja) reported 10 minutes ago

  The success and likeability of the Victoire has had a terrible effect on their ebay hockey card pricing

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  StuArt  (@StuHack73) reported 12 minutes ago

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

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  Chucky (@MasterChuck895) reported 13 minutes ago

  @Dookie_Trousers Yeah, not sure how that works. If that’s the case everything we sell is illegal every little collecting agenda is illegal. They might as well shut down eBay and TCG 💀

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  𖦹ဗ The Hivemind ☆𖹭 (@BendyLoneWolf) reported 16 minutes ago

  I already had the first two Bendy and Boris ones from back when they were being sent out. The scary lil Christmas ones I got later down the line on eBay, and the naughty or nice one this year on Mercari.

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  Macy 🛸 (@mace_face18) reported 19 minutes ago

  @EmilyDiorXO So stupid. I can get a new head off of ebay for ~$60 but I've literally had this vacuum less than a year. 2-3 times per week usage except the last few months just once per week. The piece itself is not broken I'm seeing but it's breaking the other removable parts (burning them)

• 
  kitchen witchen 🏳️‍⚧️ (@kitchen_witchen) reported 22 minutes ago

  @eBay This *** AI listing system is slow as molasses in Antarctica. I could do this in half the time and not have to fix literally every step. **** offffff

• 
  Koda (@koda6699) reported 23 minutes ago

  The photograph that disappeared for 150 years Richard Carrington was an astronomer. In 1859, he looked through a telescope and saw something nobody had recorded before. A huge flash erupted from the Sun. Less than a day later, a powerful solar storm hit Earth. Telegraph systems failed. Auroras appeared far beyond their usual range. The event became known as the Carrington Event. But there was one strange problem. Nobody knew what Carrington looked like. No photograph of him could be found. For decades, historians searched museums and archives for a missing portrait. Nothing. Then, in 2025, an archivist at the Royal Astronomical Society checked something much less obvious. eBay. She found an old photograph being sold by a shop in Pittsburgh. The seller had simply listed it as: “Photo of Mr Carrington.” It was him. On the back, faint writing gave another clue: “R C Carrington, Esquire for C V Walker, Esquire.” The photograph had been missing for about 150 years. Now it has a home in the Royal Astronomical Society archives.

• 
  Adam Taylor (@adamtaylorl) reported 26 minutes ago

  Me at 20: - £50K/year at Jaguar Land Rover - Acting team leader, dream job by every measure - Watching agency videos in the car park on my lunch break Me at 24: - $100M+ generated on Meta - Scaled a brand in LA from $90K to $2.1M/mo in 7 months - 40+ team members, run from Dubai My first money online was flipping scooter parts on eBay at 13. I opened my own bank account for it. Nobody told me to. I just wanted a bike and needed to make £200 to afford it. Fast forward. Late 2022 I bought an Iman Gadzhi agency course and landed a client in 2 months. They paid £900/month. I was going to say £1,000 and bottled it at the last second. Then 7 months passed and I made £80. In total. I didn't tell my friends what I was doing. I barely even told my family. There was nothing to show anyone, so I just went quiet and kept going. I handed in my notice at JLR in July 2024. Everyone at the factory thought I'd lost it – to them I was walking out of a job people wait 20 years for. So I flew to Bali alone in early 2025. I knew nobody there. I wasn't a huge fan of Bali. Then I moved to Thailand 30 days later. Hit $50K/mo whilst there. Then $100K. Now I'm living in Dubai and the business does more in a month than JLR paid me in four years. The crazy part is I wasn't good at this. I was terrible for a long time. I just kept putting in long hours and watching what happened until some of it started working. And I'm still doing that. The same method, just with bigger numbers. If a 13-year-old selling scooter parts to fund a bike can turn it into this, you're fine. Win each day. That's the whole thing.

• 
  EverydayReseller (@EverydayResell) reported 27 minutes ago

  Day 208 Total sales: $737.96 eBay earnings: $504.99 Buy cost (COG): $82.27 Net profit: $422.72 ROI: 514% Items sold: Electrical equipment, printer ink, printer parts, automotive transmission parts, marine electronics, & a toilet repair component. Another strong day. $737 in sales & over $422 in net profit from just 7 completed sales. What stands out to me is the variety. Electrical, automotive, marine, printing, plumbing, you don't necessarily need to specialize in one category to build a profitable eBay business. You need to get good at finding the right products at the right price. That's still the biggest lesson I've learned through this journey. A $100 sale isn't nearly as exciting if you paid $90 for it. But when you can consistently find inventory for a fraction of its potential selling price, the numbers start working in your favor. Today's $82.27 in COG turned into $422.72 in profit. That's the game. Buy right. List consistently. Be patient. Keep improving. 208 days down. On to Day 209. 📦💪

• 
  Ratchlock in the Rain ⛈️🥀 (@DamnitKnightly) reported 28 minutes ago

  @boomboomfaia Yeah we had to turn off shipping TO the EU because of LUCID and the new PPWR rulings (US based issue for Etsy/Ebay) But I do believe acggoods does handle most of that since they are the ones producing and shipping the items. Only thing is the tariffs but that is customer based

• 
  Mr8000 (@mr8mil) reported 35 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!

• 
  Eric the Red / Kelenic Collector (@FatherBreaks) reported 37 minutes ago

  I think that every time EBay or another platform has issues they should reduce the fees for the equivalent amount of time.

• 
  Pokémon Deals, Restock and Alerts (@PokemonRestockr) reported 38 minutes ago

  @Pacho9_3 It looks like eBay just took down another seller of the 30th pre orders. Seems only seasoned sellers like footballpete and a few others are actually safe. I think I’ll avoid posting 30th eBay links regardless moving forward until September 16th

• 
  collectorfix (@collectorfix) reported 38 minutes ago

  @CardPurchaser @CardPurchaser why have you all-of-a-sudden been focusing primarily on selling cards and how on eBay lately? Like micro breaking it down. Like obsessed with it.

• 
  OptionsFlow - Smart Money Alerts (@OptionsFlowApp) reported 39 minutes ago

  $GME popping on prelims. Sales down vs last year, profit up on the eBay mark, and they used cash to cap dilution. That's the meme print.

• 
  Olivia Kory (@oliviaakory) reported 39 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.

• 
  Victory Gundam V1 (@Gundam_mf) reported 42 minutes ago

  @SylusMk2 I found the metal fix on eBay for like 1k Gl, the hummingbird figure is like a mythological creature at this point

• 
  Les Twigg (@LesTwigg) reported 44 minutes ago

  @eBay @eBay_UK Not really a customer support issue though is it. Just pointing out where yet another change has made things worse.....

• 
  隨便 🐝 (@imbeingtheone) reported 45 minutes ago

  this guy just rejected my lowball offer on ebay. dude part of the thing is broken

• 
  Alison Scott 🐻 (@bnosilagm) reported 52 minutes ago

  @CalBearsHistory Every time these are brought out for the season count down I have to tell myself to stay away from Ebay. I really don't need another thing to collect.

• 
  A.P.O.C (@AOchiawuto) reported 52 minutes ago

  @AirtelNigeria @lydiacreatesng Airtel Nigeria, why is your service so poor? I’ve tried registering on eBay, but the verification code never arrives on my Airtel line. Yet, the same code comes through immediately on MTN. What’s wrong with Airtel? Is Nigeria cursed? Please fix your SMS service!

• 
  Ripper (@ripper0x) reported 54 minutes ago

  @anglio @eBay this is terrible

• 
  Fitzzzy 🏴‍☠️ (@ShaunFitzzzy) reported 56 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

• 
  J & C Collectibles (@JC_colleccoast) reported 59 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!

• 
  theguru 🀄️ (@AyomideTheGuru) reported 59 minutes ago

  @eBay should fix up asap

• 
  ♋️nahgem💫 (@danisnotesquire) reported 59 minutes ago

  @MarButHockey i bought boxes. and cards off ebay. some of the ebay cards are like im buying cards For my sid and geno cards. this is a Real Problem.

• 
  Sam Parr (@thesamparr) reported 60 minutes ago

  Former CEO of PayPal + Inuit Bill Harris on Moneywise today. He broke down his portfolio and expenses (and what it was like working w/ Elon + thiel. - Net worth: ~$100m. Divorce cut it roughly in half - ~$50m in operating companies he's starting, ~$25m diversified securities, ~$25m bonds. No PE/hedge/alts - "absurd fees" - PayPal/eBay exit: personally "more than 20 million, less than 50" - Personal Capital: sold to Empower for $825m with $23b AUM; his take was north of $100m post-tax - New venture: ~$10m of his own money in - Total annual burn: ~$70-80k all in, property tax included ("well less than a hundred thousand bucks") - No mortgage (cottage paid off), no car, bikes to work - Owned a ton of stuff (houses, cars, planes). Sold it all because it took too much time to maintain.

• 
  Jenny Jen Jennifer VCR Queen (@vcrqueenjen) reported 1 hour 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