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 (64%)
• Sign in (22%)
• Errors (13%)

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	17 hours ago
Hannoversch Münden	Sign in	21 hours ago
Blackpool	Sign in	23 hours ago
Preston	Website Down	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:

• 
  T-Bob Hebert (@TBob53) reported 2 minutes ago

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

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  NVSportsCards (@NVSportsCards) reported 2 minutes ago

  @CardsMax If Comc didn’t take 6 months to process or ship, they could have caused real problems for ebay.

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  Adam Taylor (@adamtaylorl) reported 3 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.

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  brandon (@trancetheartist) reported 4 minutes ago

  @MTGSecretLair ngl that queue was nasty today - first issue since ordering from you guys. waited in queue for 3 hours just for it to sell out immediately after sales started again. multiple listings on ebay for over retail. kinda sucks :/

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  Wandering Biku. Professional cynic. (@BikuWandering) reported 9 minutes ago

  So, buying DVD's from eBay is cheaper than ordering movies to stream. Plus you get to keep them when the grid goes down 👍👍👍

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  jay (@NyzTCG) reported 11 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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  Stitch (@StitchKings) reported 12 minutes ago

  @ai_degen_hick @grok can you explain how much someone nets on an eBay card sale of $2,025 after fees for our slow friend here?

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  Ratchlock in the Rain ⛈️🥀 (@DamnitKnightly) reported 15 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

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  Jay (@TheLobbyistGuy) reported 16 minutes ago

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

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  rack$ (@mnstacks) reported 18 minutes ago

  He built an autonomous Grok Bot named Reaper, handed it his credit card debt and told it to pay off his credit card debt for him. He gave the bot its own computer in the cloud and access to his email. Then he basically said: “This debt is your problem now. Don’t ask me for money. Just report back every night.” What Reaper actually did • canceled old subscriptions he’d forgotten • talked the credit card company into a lower interest rate • moved most of the balance to a 0% card • got a couple of charges refunded • sold his unused PlayStation and monitors on eBay • put every extra dollar it found toward the debt I spoke to him and he said he plans to work on more upgrades and refine its function He is fully onboard and will join the community to tell us about his future plans Ca;

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  Eric McCurry (@eric_mcCurry) reported 20 minutes ago

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

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  Derek Huntley (@BigDerek_KU) reported 22 minutes ago

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

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  Chucky (@MasterChuck895) reported 24 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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  Chase Jordan (@goatcollect) reported 28 minutes ago

  Following the clear confusion between labeling a jersey with a MEARS letter game used, I am hearing @eBay and @GoldinCo have finally decided to change their policies. No word on exactly what the shift is, but it is clear @eBay has recognized the liability created by selling objects as game used that their own internal team would clearly have reason to believe are not. Times have changed and credit to eBay for responding to it. Probably saved themselves from big issues down the road.

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  Mike Palmer (シネフィル) 🎬 (@MikePalmerYT) reported 31 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.

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  Genius Business (@GeniusBusiness_) reported 31 minutes ago

  PayPal's real product in its early years wasn't payments. It was survival. The company was building something that regulators, banks and its own biggest platform had no category for. Stefan Heck: "It was a true disruption of a very established, very regulated industry, right? Payments, banking… and they were doing things that didn't fit an existing category." Not fitting a category meant almost everyone with power over PayPal had a reason to switch it off. Reid Hoffman, who became Peter Thiel's firefighter chief, describes the job he was handed: "Make sure eBay doesn't drive us off the service. Make sure Visa doesn't shut us down. Persuade the federal government that it's okay that we're not a bank." Nancy Lublin on the person they sent to handle it: "He was the wolf. I mean, he was the fixer." John Lilly remembers how routine the existential threats became: "I remember we had breakfast one time. He said, 'I have to leave at 8:30 because I got to get on the plane to New York to go talk to Eliot Spitzer, the attorney general of New York, who's suing PayPal for basically anti-money laundering type things.' And he's like, 'I just got to go fix this and here's how I'm going to fix it because New York was convinced that PayPal is being used for gambling, all sorts of other things at the time.'" There was no playbook, because the category didn't exist yet. So the company argued from first principles: "It was fun because part of what ends up happening is you say, 'All right, well, what's a bank? Like, what defines a bank? How do I persuade them? I've never talked to a regulator before. All right, let me go figure out how this looks.'" While all this was happening, PayPal was outgrowing its own ability to operate. June Cohen: "Their customer numbers had grown so exponentially that they could not keep up with customer service and they actually just made the decision that that was a fire they weren't going to fight at that moment." That was a decision, not a failure. The company sorted its problems by one test: "There was a whole set of fires at PayPal where if you didn't solve them, value of the company zero, out of business." John Lilly on how that worked in practice: "Here's the one, two, three top priorities. Everything else I'm not going to worry about right now because if I get these three things right, everything else will be okay." The fire that passed the test was eBay: "If eBay had turned us off, if we were no longer to operate on eBay, no initial users, no traction, no network effect, no ability to grow." And eBay had every incentive to do exactly that. John Lilly: "eBay had its own payment system that was competing with PayPal. PayPal was winning. And so I think those got very complicated and eBay very much didn't want to have to buy PayPal and eventually they felt like they had to." That's the ending. PayPal survived on someone else's platform long enough to become the thing that platform had no choice but to acquire. A reporter at the time: "We know these guys very well obviously because so much of their business is in fact on eBay. The synergies of putting these two companies together were incredibly outstanding and it just seemed like the right time to get this deal done." PayPal didn't win by solving its problems. It won by correctly ranking them and letting the rest burn.

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  ᴅᴏɴᴏᴠᴀɴ² 🇺🇸🇬🇧 (@arcanedonovan) reported 36 minutes ago

  @PunchingCat @michiganstan25 i put them on ebay for my house down payment

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  Cori (DemShenaniganss) 🦝 | #TDST ⚡️ (@DemShenanigans) reported 39 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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  Carolina24 (@c_collector24) reported 40 minutes ago

  @pasch_8759_eBay @CardPurchaser @tm1515152005 Had the same issue yesterday (TWICE) with the same Kirby Puckett card that I even talked to an eBay representative about and he suggested I just relist it? No idea what they’re doing with their new AI filters

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  HuntXr🫩 (@hunter_xzn) reported 41 minutes ago

  @anglio @eBay Woah woah Why didn’t you use some other platform or you didn’t know about this error ?

• 
  Olivia Kory (@oliviaakory) reported 47 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.

• 
  Stitch (@StitchKings) reported 51 minutes ago

  88 PSA 10 Pongo Enchanted’s From Fabled exist. My first raw just came in and is absolutely a contender. Grabbed another clean copy coming in from Australia for $259.86 last night with taxes and fees. The boys down under are lagging in market prices. Make sure you check out Australian sellers on eBay 🇦🇺 #Pokemon ————> #Lorcana

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  Archer (@Archer471653301) reported 53 minutes ago

  @NINJA_2029 @PanthoriusPrime Calm down ninja go to Ebay its a scalper sell out.

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  Penni (@tulipgennaro) reported 53 minutes ago

  Selling on eBay is so slow

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  𖦹ဗ The Hivemind ☆𖹭 (@BendyLoneWolf) reported 54 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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  virgo_☀️♊️🌚 (@virgorising_gem) reported 56 minutes ago

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

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  Eric (@GhostRunnerCard) reported 56 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.

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  theguru 🀄️ (@AyomideTheGuru) reported 56 minutes ago

  @eBay should fix up asap

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  Allyson 🪷 Bento (@NoSurrender_87) reported 57 minutes ago

  @Jtsbae69 I started using eBay in college & stick with that since I’ve built reviews. Starting fresh, I’d go down a Reddit rabbit hole, there are so many damn options now. See what sounds best. Make sure whatever you go with has seller protections!

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  Gavin Forsyth (@Gavin4syth) reported 60 minutes ago

  @eBay @askebay This does not work. The options only loop with no option to resolve my issue because I cannot contact an agent by phone.