Amazon Web Services status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of Amazon Web Services 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 Amazon Web Services users through our website.

• Website Down (71%)
• Sign in (14%)
• Errors (14%)

Live Outage Map

The most recent Amazon Web Services outage reports came from the following cities:

City	Problem Type	Report Time
Township of Evan	Sign in	3 days ago
Pottstown	Website Down	8 days ago
Iztacalco	Website Down	11 days ago
Boca da Mata	Errors	23 days ago
Township of Evan	Website Down	1 month ago
New York City	Website Down	1 month 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.

Amazon Web Services Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Matthew Joughin | 🏗️ Cross Platform Dev Tools (@mrwcjoughin) reported 56 seconds ago

  @meaningoflights @jpschroeder @awscloud what issue do you have with that?

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  Matthew Joughin | 🏗️ Cross Platform Dev Tools (@mrwcjoughin) reported 6 minutes ago

  @ivanburazin Why are you still using windows server ? It’s less than 10 minutes to fire up a @awscloud ECS cluster running free Linux

• 
  Nandkishor (@devops_nk) reported 7 minutes ago

  Infosys DevOps Engineer L2 Round (Offline) My friend got these questions in the L2 interview: 1. What does the top command show in Linux, and what does load average represent? 2. What is the difference between Amazon S3 and EBS? 3. What is Amazon EC2, and can we use a custom OS/image with an EC2 instance? 4. What is the difference between self-managed PostgreSQL and Amazon RDS for PostgreSQL? 5. What are Primary Keys and Foreign Keys, and why are they used? 6. Can S3 data be automatically deleted based on a policy? How do S3 Lifecycle Policies work? 7. How would you install Nginx or Apache on Ubuntu? 8. What is sudo, and why do we use it in Linux? 9. What is the difference between Prometheus and Grafana? 10. How does Prometheus collect/scrape metrics? 11. How do you manage Docker images, containers, and running processes from the CLI? 12. Where is a Bearer Token normally passed in an HTTP request? 13. Do you use any third-party monitoring tools in your production environment? If yes, which one? 14. How would you design a standardized Jenkins pipeline for multiple teams? 15. How would you create reusable Jenkins templates and shared libraries? 16. How would you troubleshoot an intermittently failing Jenkins pipeline? 17. How would you integrate Maven into a CI/CD pipeline? 18. How would you manage builds involving Node.js, Python, or Go? 19. How would you troubleshoot a failed production deployment from Jenkins? 20. How would you investigate a Kubernetes workload with high CPU or memory usage? 21. How would you use cloud monitoring and logs to isolate an infrastructure issue? Save this if you're preparing for a DevOps Engineer interview.

• 
  ❤️‍🔥ViBe GuY_PrinCe😉 (@777PrinceFreaky) reported 9 minutes ago

  Amazon has very poor service in Warranty and Claim Procedure To Customer fix this for me soon @amazonIN @awscloud @AmazonHelp

• 
  Himanshu Singh (@hsnice16) reported 12 minutes ago

  Been using LLMs a lot to write code recently. Sometimes I learn a few new things, and sometimes it just confuses itself and me, maybe because there is no one right way to do things. Recently, while working on a pricing page for a new product we've been developing, a senior team member suggested that it would be better to extract it into a separate codebase and deploy the frontend separately. He would then route it through CloudFront under the same origin to avoid cross-origin issues when transferring credentials. I learned that using `*` for `Access-Control-Allow-Origin` doesn't work for credentialed CORS requests. But before all of this worked, I had extracted the pricing frontend into the same codebase, just in a new folder with the required files, and was trying to deploy it on the same infrastructure the rest of the application was using on @awscloud. It wasn't working because there were some issues between the build output and the CDK synthesis/deployment stages. Eventually, I extracted the code into a completely new repository and deployed it on @vercel.

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  Rami (@KingRomstar) reported 14 minutes ago

  @awscloud your multisession login doesn't even work right. I have to logout of one account and into another everytime I want to swap environments.

• 
  zarrakh (@zarrakh) reported 14 minutes ago

  @awscloud @AWSSupport as I told before, It will be just a correspondence and no solution. I have been getting correspondence since feb 2026 but no refund, despite of your team confirmed that the issue is on your side.#aws

• 
  Jaimin Vaghani (@jaiminvaghani) reported 15 minutes ago

  @AWSSupport 5+ days, Case 178738643800497. Production down, customers impacted. Same scripted answer daily: no ETA, no updates, just "check back in a couple of hours." This needs real escalation. Please have someone with authority look at this.

• 
  Mohammed Nafees (@mnafees) reported 16 minutes ago

  yo @awscloud seems like a broken cert chain from your side

• 
  Ikenna Iheanaetu (@Ikenna_dev) reported 18 minutes ago

  @awscloud Runtime request flow User → HTTPS → ALB → ECS Fargate → Container The ALB handles HTTPS using ACM and routes traffic to the ECS service. The service runs multiple tasks across Availability Zones, giving the application redundancy instead of relying on one server.

• 
  Catalin C (@CatalinC_Uk) reported 19 minutes ago

  @awscloud AI bot traffic’s surge forces a tough choice: lock it down or risk diluted engagement.

• 
  TAPE Vector (@Tape_Vector) reported 20 minutes ago

  JPP-KY $5284.TW is not an AI chip company. It makes the precision metal infrastructure that surrounds the chips, power systems and cooling hardware inside modern AI servers. That distinction matters. JPP Holding designs and manufactures precision metal mechanical parts, enclosures, cabinets and structural components. Its products are used across: AI server racks Server chassis Power supply housings Battery backup unit enclosures Liquid cooling components CDU and manifold structures Telecom equipment Aerospace avionics Aircraft structural and cabin parts Medical equipment Industrial systems The company is headquartered through a Cayman holding structure and listed in Taiwan, but much of the manufacturing engine sits in Thailand through Jinpao Precision Industry. That Thailand base is important. JPP is positioning itself between Taiwanese and global technology customers that increasingly want manufacturing capacity outside China. The operating model is high mix precision manufacturing rather than mass production of one standardized component. A customer brings JPP a mechanical design or performance requirement. JPP can then handle several steps internally: Engineering and design support Metal cutting Stamping CNC machining Sheet metal forming Welding Surface treatment Painting Assembly Inspection Final integration That means the company can take a customer from drawing to finished enclosure instead of supplying only one small step. For AI servers, this can include the physical rack or chassis holding compute hardware, power equipment and cooling systems. For aerospace, it can include avionics housings, structural parts and cabin components that require much tighter certification and process control. This combination is unusual. AI infrastructure gives JPP growth. Aerospace gives it another technically demanding end market with different cycles. The company describes this model as a mix of European engineering capability and Thai manufacturing. The phrase used by management has been: French brain. Thai heart. That comes from the European aerospace companies JPP acquired and integrated with its Thailand manufacturing base. The aerospace side matters because the qualification barriers are much higher than ordinary sheet metal fabrication. JPP has Nadcap certified processes and has worked within the European aerospace supply chain. Company materials and industry reporting have referenced customers and programs connected to Airbus, Thales and Safran. Those relationships do not automatically mean every JPP aerospace product goes directly into those companies. But they show that the manufacturing system has passed qualification standards far above normal commodity metal fabrication. Then AI arrived. This has changed the financial profile of the company very quickly. FY2024 revenue was approximately NT$2.39 billion. FY2025 revenue jumped to about NT$3.73 billion. That is roughly 56% growth. Net income reached approximately NT$618 million. EPS reached NT$12.05. Gross margin stayed around 37.8%. That margin is one of the numbers I find most interesting. JPP did not double its business by becoming a low margin commodity manufacturer. The company expanded rapidly while keeping gross margin in the high 30% range. That suggests the current product mix still carries meaningful engineering and manufacturing value. Q1 2026 continued the trend. Revenue reached approximately NT$1.17 billion. That was about 45% higher year over year. Gross margin remained around 37.5%. So the 2025 acceleration did not immediately reverse once the calendar changed. This is now a real operating ramp. The AI server side has become the main growth engine. JPP manufactures server racks, chassis, power enclosures and increasingly components associated with liquid cooling. That last category matters. AI servers are becoming more difficult to cool. Higher power GPUs produce more heat. More compute density means more thermal load inside each rack. That is pushing the data center industry toward larger cooling distribution systems, manifolds, cold plates and liquid cooling infrastructure. JPP does not manufacture the GPU or the cooling technology itself. It manufactures some of the metal structures and precision components that allow those systems to be installed inside the rack. That places the company several layers beneath the visible AI names. $NVDA and $AMD create demand for increasingly powerful accelerators. Those accelerators require more complex server systems. $DELL and $SMCI integrate servers and racks around those accelerators. $VRT and $ETN operate in the power and cooling infrastructure around the data center. JPP sits further inside the physical manufacturing chain. It produces some of the metal cabinets, chassis, housings and structural components required by this infrastructure. These are ecosystem comparisons. They are not all disclosed customer relationships. The most interesting potential US connection is the company's major cloud customer. Management commentary and Taiwan reporting have repeatedly described a major US cloud service provider as one of JPP's largest AI customers. That customer has widely been reported as Amazon AWS. If correct, that creates an indirect connection to $AMZN. But I would keep the wording disciplined. JPP has not provided enough English primary disclosure for me to treat the identity and exact revenue contribution as completely settled. The important hard fact is that a major US CSP has become a very large customer. Recent commentary has indicated that this customer may account for roughly 30% of revenue during parts of the AI ramp. That is both the opportunity and the risk. A customer that large can transform a small supplier. It can also transform the income statement in the opposite direction if orders slow. Another major relationship is in Thailand. JPP has been expanding production around a large power and server customer widely identified as Delta Electronics Thailand. That customer makes power supplies, thermal systems, data center equipment and related electronics. The geographical relationship matters because both companies operate major production facilities in Thailand. Shorter logistics. Faster delivery. Closer engineering cooperation. Just in time production. Dedicated manufacturing capacity. Those factors can make a supplier harder to replace once a large program is running. But they also deepen customer concentration. JPP is effectively investing ahead of these customers. The company has been adding production capacity in Thailand. One important bottleneck has been painting and surface treatment. JPP is expanding automated paint capacity. It is also investing in larger stamping capacity and dedicated production areas for AI server and power related products. The logic is simple. More AI server racks require more metal structures. More power density requires more sophisticated power housings. Liquid cooling adds additional structural parts. If JPP remains qualified inside those programs, each generation of AI infrastructure can increase the content opportunity per rack. That is the bull side. The risk is that the company adds capacity for demand that later slows. AI infrastructure spending is strong now. It will not grow in a straight line forever. A hyperscaler can change server architecture. An ODM can move a program. A customer can dual source. A competitor can cut price. If one large customer represents 25% to 30% or more of revenue, those decisions matter immediately. That is why I want the exact customer concentration table from the latest annual report. The aerospace business gives JPP some diversification. Before the AI acceleration, aerospace represented a much larger part of the company. That business went through a difficult period around the pandemic and the following aerospace supply chain disruption. It has been recovering. The company has continued obtaining certifications and expanding its European aerospace capabilities. That creates a useful second engine. AI server demand is fast and capital intensive. Aerospace is slower, qualification heavy and built around longer product cycles. The two businesses have different risks. Together they can potentially produce a more balanced manufacturing platform. But right now AI is clearly driving the growth rate. The financial question from here is not whether revenue can grow. It already has. The question is whether the current margins survive the next stage of scale. High 30% gross margins are strong for a precision metal manufacturer. I want to know how much of that comes from: AI server racks Power enclosures Liquid cooling components Aerospace Specialty low volume work New customer programs I also want the operating cash flow behind the reported earnings. Fast manufacturing growth consumes working capital. More orders require more raw material. More capacity requires more equipment. More inventory sits between production and customer delivery. Receivables rise. So a company can report excellent earnings while cash is being absorbed into expansion. That is not automatically bad. But the return on that capital has to remain high. JPP ended 2025 with roughly NT$7.4 billion in assets and around NT$3.7 billion in equity. The balance sheet does not currently look distressed. There is no obvious heavy dilution story. The primary capital allocation issue is expansion. Paint lines. Stamping equipment. Factory capacity. Dedicated customer production. Those investments are being made because demand already exists. Now they need to earn acceptable returns. For US market context, I see several useful layers. $NVDA and $AMD are demand drivers. More accelerator shipments can mean more server racks, more power density and more cooling hardware. $DELL and $SMCI represent the server integration layer. They assemble computing systems around GPUs, networking, storage and power. $VRT and $ETN represent the data center power and thermal infrastructure layer. $ANET sits in the networking layer connecting increasingly large AI clusters. $AMZN is relevant because AWS is widely reported as the major US CSP associated with JPP's AI server business. Again, I would treat that specific customer identity as reported rather than fully disclosed until the primary customer note confirms it. The aerospace familiarity is different. $BA is the obvious US listed aerospace reference. JPP is not primarily a Boeing supplier story. Its known aerospace footprint is more European. But the same qualification logic applies. Aircraft components require traceability, process control and long certification cycles. That experience can strengthen the overall manufacturing discipline of the company even when the fastest growth is coming from AI infrastructure. This is what makes $5284.TW more interesting than a generic sheet metal company. The metal itself is not scarce. The capability stack can be. A customer needs a supplier that can: Meet tolerances. Pass qualification. Build tooling. Handle design changes. Scale capacity. Deliver consistently. Maintain surface quality. Control welding and assembly. Locate production close to the customer. And do it without disrupting a multibillion dollar server or aerospace program. That creates switching friction. It does not create an unbreakable moat. Large customers still have enormous negotiating power. The company remains small relative to the customers it serves. That means the power relationship still favors the customer. The current strengths are clear. 2025 revenue grew about 56%. EPS reached NT$12.05. Gross margin remained near 38%. Q1 2026 revenue grew another 45%. AI server exposure is already producing real revenue. Liquid cooling adds another content opportunity. Thailand capacity is expanding. Aerospace is recovering. The balance sheet is supporting expansion without obvious distressed financing. The risks are also clear. Customer concentration is high. The largest AI programs are project driven. Formal long term volume commitments are not well disclosed. The company is investing heavily into capacity during an AI spending boom. Margins could compress as volume rises. Aerospace recovery could stall. And the current growth rate depends heavily on continued data center capital spending. For me, the next proof is not another monthly revenue record. I want to see: Exact top customer concentration. How much revenue now comes from AI server products. How much comes from liquid cooling. Whether the major CSP relationship is widening into additional products. Whether the large Thai power customer is gaining share of revenue. Utilization of the new painting and stamping capacity. Operating cash flow after expansion capex. Return on invested capital from the Thailand buildout. Aerospace revenue and margin recovery. Whether gross margin can remain above the mid 30% range as the company scales. Real manufacturing. Real AI infrastructure exposure. Real earnings growth. Real high margin execution so far. But also real concentration risk. jpp-KY $5284.TW does not need to invent the next GPU. It needs to remain the qualified company manufacturing the physical structures around the companies that do. If AI racks become larger, hotter and more complex while JPP keeps winning more content per system, the opportunity can grow much faster than the underlying server unit count. The question now is whether that position is durable enough to survive the inevitable cooling of the AI capital spending cycle. That is what I want to understand next. My investing journal, not financial advice.

• 
  WPBeginner (@wpbeginner) reported 21 minutes ago

  You built your WordPress site over months (or years). One bad plugin/theme update can wipe it all out overnight. 😱 Plugin conflicts. Malware. A botched migration. A hacked server. The disasters that take down WordPress sites usually happen without warning. And here's the mistake most site owners make: they think their hosting provider's backup is enough. It's NOT. If the server fails, you lose both your site and the backup. We share the complete step-by-step guide for backing up your WordPress site the right way. Here is what you will learn: ✅ Use a Backup Plugin (Best for Most People): @DuplicatorWP is what we use across our sites. Full-site backups, disaster recovery links, and restore without having the plugin pre-installed. Free version available, Pro has scheduled backups. ✅ Use Your Hosting Provider's Backup: SiteGround (where WPBeginner is hosted) includes manual and automated daily backups on all plans. Bluehost partners with CodeGuard and Jetpack for their built-in options. ✅ Manual Backup With cPanel or FTP: Use cPanel's Backup Wizard for a full backup, or connect via FileZilla FTP to download your wp-content, themes, plugins, and wp-config.php files directly. ✅ Send Backups to Cloud Storage: Duplicator and UpdraftPlus both connect natively to Google Drive, Dropbox, OneDrive, and Amazon S3. Never store backups on the same server as your website. If your host fails, both are gone. ✅ Set Up Automatic Scheduled Backups: Configure hourly, daily, weekly, or monthly backups in Duplicator based on how often you publish. eCommerce stores and busy blogs need daily. Slower-moving sites can get away with weekly. Ready to protect years of hard work with a proper WordPress backup system? Read the full ultimate step-by-step guide 👇 (Link is in the thread below)

• 
  Chris (@Mcatbeast) reported 22 minutes ago

  @AWSSupport Apologies for the earlier frustration. Turns out it was an issue from our end! My user was accidentally deleted.

• 
  ShenYubao (@ssybb1988) reported 28 minutes ago

  @AWSSupport @awscloud AWS account suspended for additional verification; Production services down for ~24h. All verification docs submitted. Unable to purchase Business Support+ due to suspension. Please expedite review & help restore production. Case ID: 178678971500932

• 
  Lori-Colorado (@lori_colorado) reported 30 minutes ago

  @marklevinshow The future, I bet: Giant Data—owner/consumers* long-term goal (5-10 years) Replace all of the servers in the AI data—mega center barns. Then they won’t need all these monster consumers of land, water, electricity. They will be empty silent mausoleums of AI’s startup period. * Meta, Open AI/Oracle, xAI, Microsoft, Amazon AWS, and Google. Reminds me of Jonathan Winters in The Loved One (1965) looking down from his helicopter over his empire of corporate cemeteries… “ I’ve gotta get these stiffs off my land! ”

• 
  Corey Quinn (@QuinnyPig) reported 31 minutes ago

  @awscloud You have all the tools to fix this, someone’s just apparently too scared to give Charlie a crossbow and diplomatic immunity. Give him the crossbow and stand back, @satyanadella.

• 
  Jacklyn Taylor (@Ronindrake2) reported 37 minutes ago

  @awscloud @AmazonHelp @amazon Yo, can any of yall explain why a review that gets flagged for "community guidelines" refuses to tell you what the issue is? Like yall's bot goes "it violates rules!" But cant tell me what part? It obviously had to note what the issue was...

• 
  Aqib Ansari (@Aqib_Ansari_) reported 38 minutes ago

  @AWSSupport Hey @AWSSupport any update on this? 8 Aug 2026: Issue opened 13 Aug 2026: Issue escalated to specialized team 18 Aug 2026: Still being investigated. Its 21 Aug 2026 now still no resolution or substantive update. Nearly 2 weeks for a service-access issue is frustrating experience

• 
  absk (@abskwdkr) reported 42 minutes ago

  My ec2 ssh logs in too slow and lags @AWSSupport

• 
  Harshil Mathur (@harshilmathur) reported 43 minutes ago

  4 billion payments. 3 trillion data points. One model trained on all of it. Meet @Razorpay Vulcan - India’s first transformer-based AI foundation model for payments. Built, trained and hosted in India, in partnership with @nvidia and @awscloud. Until now, every payments problem was solved separately: routing, fraud, risk, personalisation and more. We asked: What if one model could understand how money moves? And like LLMs are trained on text to understand language, Vulcan is trained on payments to understand how money moves. Already running in beta across 51,000+ businesses, Vulcan is delivering: - 8–10% improvement in payment success rates - the ultimate measure of whether a payment simply works. - 8x more international card fraud detected. - 5x more fraudulent or disputed transactions identified. - 1–2 lakh more purchases completed every month through better checkout personalisation. And we’re just getting started. The best part? Every payment Vulcan sees makes the next one smarter. We’ve spent years building the infrastructure that moves money for India. Now, we’re building the intelligence that understands and improves it. 🔥

• 
  Art Vandelay (@john15489) reported 44 minutes ago

  @amazon @awscloud @nvidia I need an actual customer service phone number so I can actually speak to a ******* rep regarding 4 orders now. Your email help system is broken.

• 
  Prateek Gupta (@p_valuee) reported 46 minutes ago

  @AWSSupport My entire production is down since 3 days, I believe this is a P1 and should be treated like one @AWSSupport. Please help me with an ETA

• 
  The AI Therapist (@TheAIShrink) reported 46 minutes ago

  @MikeLongTerm @amazon @awscloud EC2 on AMD CPUs. The cloud bill goes down, the margins go up. aws is quietly fixing its cost structure while everyone watches the models. smart

• 
  Christian Nonis (@christiannonis) reported 47 minutes ago

  I am (unfortunately) dealing with @googlecloud and @awscloud and what I am noticing is that in months they are unable so solve any kind of problem, it’s like being bounced back by ai replies that are sold like assistance from humans, saying that they are “working on that” but nothing changes in months and also replies are all the same.. idk if they are experiencing a shortage in human labor or what’s going on but the experience has become worse than ever

• 
  sof ࿔˚⋆ (@sophia_ray_17) reported 49 minutes ago

  @awscloud until the cloud has a power outage and i can't get into my car

• 
  krrawn (@krrawn) reported 50 minutes ago

  I was wondering why @awscloud support was slow the other day

• 
  Ohm Patel (@Techieohm) reported 51 minutes ago

  “Facing some problems with @awscloud on our new product. This is the 2nd time this has happened.”

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  Don KofiAdM1N 🎖️🎖️🎖️ (@DonKofiAdM1n) reported 51 minutes ago

  @anonymo6575 @AsakyGRN lol even a Jss2 can go all you just mentioned right now seriously you have no idea. Do you even know anything about Amazon aws javasscript c++ building a valid algorithm or a bank website that you login in with on your phone you think yahoo guys can do this ? lol your Knowledge is limited.

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  Harun R. (@harundotdev) reported 55 minutes ago

  2. The obvious fix: split responsibility. Metadata stays in a database like Postgres. The actual file goes to object storage, Amazon S3 being the standard example. Better. Still not the full fix.