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 (80%)
• Errors (20%)

Live Outage Map

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

City	Problem Type	Report Time
Iztacalco	Website Down	3 days ago
Boca da Mata	Errors	15 days ago
Township of Evan	Website Down	29 days ago
New York City	Website Down	1 month ago
Ciudad Jardín	Website Down	2 months ago
Kyiv	Sign in	3 months 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:

• 
  jd42 (@jdonovan42) reported 7 minutes ago

  @DarioCpx Pls recalculate considering the massive clouds the AI revs sit on. Each $1 in AI attaches $1.5-2.5X attached cloud biz and your down to 30-45% of revs. Then as open wgt models gain apply the 100% revenue retention vs. 60% on frontier and look at EBITDA % from frontier is 10-15% max. But nice try ;) No doubt Anthropic and OpenAI both stimulate demand for Amazon AWS. It creates 2x the cloud biz than it does the direct AI biz. So why not count the full picture of things vs just the #'s that fit one narrative.

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  Arian van Putten (@ProgrammerDude) reported 8 minutes ago

  @QuinnyPig @awscloud Not randomly redirecting to root console login page if I logged in with IAM identity center

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  mubs (@xmubsx) reported 12 minutes ago

  @AWSSupport MFA team is in-house team or outsourced. This is not acceptable. It has been 3 months. In June '26 this issue started. If the MFA device or app is out of sync. How is it my problem. This is aws issue. How can you guys ignore a genuine issue like this. All articles are useless.

• 
  Harshil Mathur (@harshilmathur) reported 14 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. 🔥

• 
  Adam 🤗 (@AdamMolnarHF) reported 15 minutes ago

  @cgeorgiaw @AnthropicAI @awscloud ooh, I've actually been working on an autoresearch space for a different problem (math) using Kimi k3 + inference providers, I could try forking and seeing if I can reshape the structure around this with this data + build it fully in the open!

• 
  X anonyn (@OmrX304) reported 15 minutes ago

  @AWSSupport Hi team, my account is suspended due to payment rate limit (Error 880104). I've sent a DM with my Case ID & details. Could you please escalate this to the billing team urgently? Thanks

• 
  Amanda (@AMyrick1989) reported 18 minutes ago

  @krassenstein @Tesla Be careful. All these at the same time makes me think they are hacked, I’m fairly certain Amazon AWS was hacked that day when everything when down, given my Grok we hacked and no one ever gave explanation. I’m no expert but ya know, all these signs are pointing to this. Also, the Obamas helped produce a movie where someone hacked our satellites and alluded to it being the Middle East and all the teslas went haywire and self drive themselves to pile up on all the freeways. I wish I remembered the name however I made note of this terrifying movie given Obama clearly knows things we don’t. Just sayin, these incidents aren’t scattered. I would not be driving that ***** if I were you.

• 
  Jaimin Vaghani (@jaiminvaghani) reported 20 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.

• 
  Nathan Den Herder (@natedenh) reported 20 minutes ago

  @xai @awscloud grok-4.6 on Bedrock us-east-1 is unusable today. 5-token prompt, maxTokens=16: claude-sonnet-4-5 → 3.0s us.xai.grok-4.6 → 150s timeout global.xai.grok-4.6 → 150s timeout Same account, region, creds. No throttle error, no 5xx — it just hangs.

• 
  Nathan Den Herder (@natedenh) reported 22 minutes ago

  I realize that I use @awscloud Bedrock, and this may be part of the issue, but @grok 4.6 is not usable on this platform compared to @claudeai Opus 5. I've really tried everything to make it work but I don't think there is any comparison.

• 
  Ikenna Iheanaetu (@Ikenna_dev) reported 24 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.

• 
  Lori-Colorado (@lori_colorado) reported 24 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! ”

• 
  Bravo One💀 (@RWayodi) reported 28 minutes ago

  @AWSSupport Hello. I received a charge on my credit card to Amazon EMEA but I do not have an Amazon AWS account. I am unable to create a case as it requires me to sign in. How is this even possible?

• 
  Vera ¤⩊¤ (@VeraSimila) reported 28 minutes ago

  @awscloud L+ Ratio Fix Twitch please, nobody wants AI hype beast corpo speak

• 
  mark seery (@140ismymax) reported 29 minutes ago

  Peter DeSantis talking at #agenticaisummit There will NOT be one AI chip type. If multiple AI chips take multiple years to bring to market, you have to be making assumptions about model requirements in that time frame. It's a systems problem, including the network. Constraint drives innovation. Future is bright and built together. Peter DeSantis SVP, Foundational AI Models, Custom Silicon, Quantum Computing, Amazon @awscloud @amazon @BerkeleyRDI #AI

• 
  Abdulkadir | Cybersecurity (@cyber_razz) reported 32 minutes ago

  ClarityCheck markets itself as a tool to detect catfishing. To verify identities. To keep you safe from liars online. Here's how it works. You upload someone's photo. ClarityCheck runs it through their reverse image search. Finds their dating profile. Their social media. Wherever that face appears. Seems solid. Except ClarityCheck just leaked 9 million photos sitting in an unsecured Amazon S3 bucket. 450 gigabytes of faces. Stored in folders labeled "faces" and "profiles." The photos came from people uploading images of strangers. Dating app screenshots. Private social accounts. Scanned prints. Photos of children. Most of these people never uploaded anything to ClarityCheck. They just got identified by someone else trying to figure out who they were. Then their face got indexed. Stored. And left wide open. For several months. The URL to access it was embedded in ClarityCheck's own website source code. The company's response: this was "temporary storage" and an "ordinary member of the public" would not have found it. An ordinary member of the public with the URL from their own website. Which is not temporary storage. That's just a server. And it wasn't invisible. You'd need 30 seconds and a basic understanding of how websites work. Now ClarityCheck says the data is "secured." A service built to prevent identity fraud just exposed millions of identities. The tool designed to catch people lying about who they are just showed everyone's actual face to anyone listening. The irony isn't subtle. It's a design flaw pretending to be an accident.

• 
  GIrls In Technology! (@francescaViking) reported 32 minutes ago

  @awscloud @amazonmturk are shuttering services barely months after announcing maintenance mode, giving barely a month now and leaving requesters and workers in the lurch. Amazon demonstrates again why it's terrible for businesses and users.

• 
  Webdesign29 Création de sites internet Brest⛓️🕸️ (@Webdesign29B) reported 35 minutes ago

  @AWSSupport There is no way to get a call back I have tried many different ways. And it always show's this issue

• 
  Priyanshu (@iproductAI) reported 36 minutes ago

  Here’s the cleaned-up version with fixed grammar, same hard tone, and no em dashes: I requested @awscloud to increase my Opus 4.6 V1 limit and I’ve been chatting with the AWS support team for almost 4-5 days now. They’re telling me this. Is @awscloud a government company? I mean, you guys can’t just pass the problem from one department to another. I mean, WTF? Now I have to raise my query again to sales? Why can’t you just pass this query? You already have more context about what the issue is. I can’t believe how these big MNCs are working these days. Totally absurd service from @awscloud. One more thing, please educate your support. I mean, she didn’t even know what the TPD limit is in the service quota. She literally replied the first time saying there’s only a TPM limit and no separate TPD limit.

• 
  barney (@Southclaws) reported 39 minutes ago

  @jonny_castles @awscloud I am seeing this too, ECR and S3 in eu-west-1, lots of "service unavailable" errors

• 
  Vinit Upadhyay (@vinitcodes) reported 40 minutes ago

  @AWSSupport @kirodotdev @awscloud I’ve already raised a case explaining the entire issue. Case ID: 178664512400070. Unfortunately, I still haven’t received any genuine help, which is really disappointing. I’m not the only one facing this issue-many others are experiencing the same problem. Please look into this.

• 
  Arick Goomanovsky (@g00manoid) reported 40 minutes ago

  @awscloud It's worse than technical debt in one way - sprawl compounds through the connections, not just the count. Ten agents with no shared context or ownership isn't ten problems, it's every pair of them. The fix is a connective layer that makes them a network instead of scattered debt.

• 
  TAPE Vector (@Tape_Vector) reported 41 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.

• 
  Jaimin Vaghani (@jaiminvaghani) reported 43 minutes ago

  @AWSSupport @AWSSupport Day 7.Production still down, Case still unresolved. Yesterday you said it was "forwarded internally for review" that's the third different phrasing for the same non-answer. I'm not asking for updates anymore. I'm asking: who owns this case, and when will it be fixed?

• 
  Brad (@beeradmoore) reported 47 minutes ago

  @QuinnyPig @awscloud I made a thing that posts in Slack when a new AZ goes live. It has pipped up twice in two days. I thought that was odd, it’s sat silent for so long and now twice in two days! Turns out it’s broken and will just repeat until I tell it that yes I know about eu-west-2d

• 
  Prateek Gupta (@p_valuee) reported 49 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

• 
  WPBeginner (@wpbeginner) reported 50 minutes ago

  You have spent months building your WordPress site. What happens the day it suddenly goes offline? 😱 It happens all the time. We have heard several scary stories. A plugin conflict, a bad update, or a security breach can wipe out your complete website without any warning. The scary part is that most site owners assume their host has them fully covered, right up until they actually need to restore. We have tested countless backup tools on our own projects, so we put together the exact methods we trust to keep a site safe. Here is what you will learn: ✅ Pick the Right Method: Compare backup plugins, host backups, and manual cPanel or FTP so you know which fits your skill level. ✅ Back Up the Full Site: Save your database, themes, plugins, and uploads together so you can restore everything, not just your posts. ✅ Automate It With @DuplicatorWP: Schedule daily or weekly backups and send them straight to the cloud so you never have to remember. ✅ Store Copies Off Your Server: Keep backups in Google Drive, Dropbox, or Amazon S3 so one server crash never takes your site and its backup at once. ✅ Restore in Minutes: Use a disaster recovery link to bring your site back even when it is completely broken. Ready to protect all that hard work before disaster strikes? Read our complete step-by-step guide from the link in the comments 👇 (Link is in the thread below)

• 
  ShenYubao (@ssybb1988) reported 51 minutes ago

  @AWSSupport @awscloudAWS 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

• 
  Mr Sage (@GreatSage_0x) reported 59 minutes ago

  Custos makes verdicts on AI agent transactions. Problem: it had no memory. Every decision started from zero. Meet Anamnesis — I gave it a memory layer using CockroachDB. Built for the @CockroachDB x @awscloud Hackathon

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  Nandkishor (@devops_nk) reported 1 hour 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.