Amazon Outage Map

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Amazon Issues Reports

Latest outage, problems and issue reports in social media:

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  Caryl Wright (@old59crow) reported 2 minutes ago

  @AmazonHelp Real live American support with a brain could fix it in 5 minutes.

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  Abhishek Kumar (@AbhiSingh5183) reported 2 minutes ago

  Followed up 25 & 26 June — same script, same prerefund promise, still nothing. Called today, told no ticket was even raised. Asked for written confirmation — flatly refused. Weeks of false assurances, zero resolution. @AmazonHelp fix this. #AmazonFail #ConsumerRights #AmazonIndia

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  Ramkishan choudhary (@RKKataniya) reported 3 minutes ago

  I ordered American eagle men casual pant from Amazon but I have a size issue so I want to return my order . But I am unable to create return so Please create return Order number 40560177340182704 @AmazonHelp

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  Bonnie Blue and Zoe (@BonnieBlueTK) reported 8 minutes ago

  @weimrnr Every single one. It's rare to get a good delivery. Last week, I got an empty amazon envelope. The envelope was obviously never sealed, so who knows when the contents fell out. Yet, an obviously empty envelpe just kept getting passed through the system, I'm sure with a "not my problem" attitude. Down to the delivery person who tossed in by my door with a box, where it blew half way across the driveway. I only knew to look for it because of the delivery pic. Pathetic.

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  𝖘𝖊𝖗 𝖈𝖍𝖆𝖗𝖌𝖊⚡️ (@sercharge) reported 9 minutes ago

  Big Tech's AI surge is spiking emissions. Amazon +16% to 81M tCO₂e in 2025,Google also rising sharply. The fix? Verifiable carbon-aware compute. Energy Web's Carbon Aware delivers decentralized, cryptographically proven scheduling & tracking to run workloads on cleaner power.

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  Five Points Capital (@fivepointscap) reported 11 minutes ago

  People are reading the $META cloud news completely wrong. Meta JUST inked a deal with a new provider (I forget their name) to rent compute. Internal compute needs are and will always be variable. There will be times when a large amount of compute is needed (model training, algo training, peak app/agent usage), and times when less is needed. They are simply going to rent out the excess when they don’t need that much capacity. And when you’re building out 50GW+ over time, you’re not gonna need that many chips running constantly for internal use. But sometimes they will. They are doing the same thing with energy. They’re securing massive energy capacity, and then they can wholesale it when they have too much. This is not bearish Meta, it’s not bearish semis, it’s not bearish AI. It’s the same story for how AWS started. They needed huge amounts of server capacity to handle things like Black Friday, but then the rest of the year they had way too much. It’s not bearish Amazon retail that they need more capacity in December than January.

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  Janny (@Jannyiswise1) reported 15 minutes ago

  @ukhomeoffice More bikes and cars work for these company’s. Most coming are given details of who will get them a job. Not just food delivery also Amazon and big shops. They have done for years, that is in the public domain. Saying you will do this is just to make it look like you clamped down.

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  BreeZe TW (@WitnessGamingTW) reported 17 minutes ago

  @hard8_times It’s $38.99 on Amazon because NOBODY WANTS IT. It is a PHYSICAL DISC, which is OBSOLETE. When nobody is buying, PRICE GOES DOWN. That’s why it’s still $70 digital, because NOBODY WANTS DISC. GROW UP!!!

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  Avi Chawla (@_avichawla) reported 17 minutes ago

  4 strategies to test ML models in production: (a popular ML interview question; bookmark this) Despite rigorously testing an ML model locally, it could still be a terrible idea to instantly replace the previous model with the new model. This is because it is difficult to replicate the exact production environment and conditions locally, and justify success with val/test accuracies. A more reliable strategy is to test the model in production (yes, on real-world incoming data). While this might sound risky, ML teams do it all the time, and it isn’t that complicated. Note: > Legacy model: The existing model. > Candidate model: The new model. The visual below depicts 4 common techniques to do so. 1) A/B testing Distribute the incoming requests non-uniformly between the legacy model and the candidate model. This limits the exposure of the candidate model to avoid any potential risks. So, say, 10% requests go to the candidate model, and the rest are still served by the legacy model. 2) Canary testing A/B testing typically affects all users since it randomly distributes “traffic” to either model (irrespective of the user). In canary testing, the candidate model is exposed to a small subset of users in production and gradually rolled out to more users if its metrics signal success. 3) Interleaved testing This involves mixing the predictions of multiple models in the response. For instance, in Amazon's recommendation engine, some recommendations can come from the legacy model, while some can be produced by the candidate model. Alongside, we can log the downstream success metrics (click-rate, watch-time, reported-as-not-useful-recommendation, etc.) for comparison later. 4) Shadow testing All of the above techniques affect some (or all) users. Shadow testing (or dark launches) lets us test a new model in a production environment without affecting the user experience. The candidate model is deployed alongside the existing legacy model and serves requests like the legacy model. However, the output is not sent back to the user. Instead, the output is logged for later use to benchmark its performance against the legacy model. We explicitly deploy the candidate model instead of testing offline because the exact production environment can be difficult to replicate offline. Shadow testing offers risk-free testing of the candidate model in a production environment. But one caveat is that you can’t measure user-facing metrics in shadow testing. Since the candidate model’s predictions are never shown to users, you don’t get real engagement data, like clicks, watch time, or conversions. And this is exactly how top ML teams at Netflix, Amazon, and Google roll out new models safely. They never flip the switch all at once, but rather first test in production, observe, compare, and then promote the model to 100% traffic. Of course, alongside all this, you would also measure latency, throughput, resource usage, and downstream success metrics. A model that’s 2% more accurate but 3× slower isn't desired from a user experience standpoint. That said, all this is downstream of one thing though. Production testing only means something if the offline evaluation underneath it was honest. If the test set gets touched during model selection, it leaks into your choices, and the score is inflated before production testing even starts. I wrote an article that covers several misconceptions engineers have about using train, validation, and test sets. It also covers the way they're actually meant to be used. Read it below.

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  Akshay Saini (@akshaymarch7) reported 20 minutes ago

  Skilled engineers who are good at problem solving will always make money. Recently I was talking to an HR at Amazon, she is still struggling hard to find good talent. And it's not just Amazon, many big tech companies are struggling to find people who can actually cook.

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  Colin Dilworth (@Redno5) reported 22 minutes ago

  @AmazonHelp how come no one can help resolve my Problem? I have a promise in writing and you have the conversation yet you refuse to resolve

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  UG H (@nchallag) reported 22 minutes ago

  Hello Amazon Are you shutting down your business in india? None of our orders were delivered in last 1 month. Orders getting cancelled with some stupid reasons. #Amazon @amazonIN

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  crOwlyCat (s1 is canon) 🐱🐱🐱🐍🐍🐴🐴 (@Lizzie_owlycat) reported 24 minutes ago

  @guessimdemons I wonder how and what that will be. I mean, what about legal issues with Amazon and NG? Would that be different for sth like the lockdown video or animation or so? Genuine questions, I'd love to know. I'm sure TPE could afford creating stuff, but legal and time issues?

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  jiya baxi (@JiyaBaxi) reported 26 minutes ago

  I have called Amazon Customer Care multiple times and followed up continuously. Every time, I receive the same response: "Please wait, your refund will be processed soon." Unfortunately, nothing has changed. There has been no proper response, no ownership of the issue

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  Syed Ahmad Ali (@SyedAhm1106) reported 29 minutes ago

  📸 Your product isn't the problem; your visuals might be. Most Amazon listings fail not because of bad products, but weak images. High-converting visuals = more clicks, more trust, more sales. Want a free improvement idea for your listing? Drop a "YES" 👇 #AmazonFBA #EcomGrowth #ProductListing