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 (67%)
• Sign in (17%)
• Errors (17%)

Live Outage Map

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

City	Problem Type	Report Time
Boca da Mata	Errors	9 days ago
Township of Evan	Website Down	23 days ago
New York City	Website Down	26 days ago
Ciudad Jardín	Website Down	2 months ago
Kyiv	Sign in	3 months ago
Chennai	Website Down	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.

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Amazon Web Services Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Open_ERV (@open_erv) reported 6 minutes ago

  Unfortunately although they appear to be awesome people BrambleCFD is turning out to not be that hot. The main problem is the relationships/what they do of all the different settings is ridiculously opaque. There is no documentation. Their solution is to try to explain things in a video call, and if you need help, you ask for it, which it takes a week or more to get any kind of answer from an actual human, not because they are doing anything wrong but that's just not a good system. It's a long long way from the useability of simscale. I did however uncover an option that might be reasonably good, which is a virtual machine that I pay for the core-hours on. In many ways this is better. I can work directly with openFOAM and one of the front ends on a high powered linux computer with hundreds of gigs of ram and 96 high powered cores, and still only pay for what I use, theoretically. The openFOAM foundation has a system worked out ad directly offers the service, unfortunately they in turn use the amazon AWS or the microsoft Azure system, but what can you do. There are other companies that do similar things, but they probably aren't as well done as the one from the actual foundation. I think I'll try that one first. Having an AI in a harness on the machine is probably going to be indispensable, but I'll be using it primarily as a learning tool rather than asking it to do everything for me. I have been able to set up CowAgent, which is kind of basic but seems to work ok, with DeepSeek. A "harness" allows the AI to run commands on your computer and read the output automatically, as well as the other things web chat stuff can do. Secondly, it can store information in files and run the AI in a loop, doing many inferences one after the other, thus getting far more done than a web chat can (actually they might do something similar now IDK but they don't seem to).

• 
  Harun R. (@harundotdev) reported 7 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.

• 
  TAPE Vector (@Tape_Vector) reported 9 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.

• 
  Milan Jovanović (@mjovanovictech) reported 11 minutes ago

  @mrwcjoughin @awscloud @Azure I doubt this is a compute issue, looks like a bottleneck from past choices coming to bite you

• 
  jd42 (@jdonovan42) reported 12 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.

• 
  Felipe Rojas (@FipeRojas) reported 13 minutes ago

  @AWSSupport as root admin of a management account in Chile, your system failed to issue our statutory VAT invoice despite our tax settings were configurated according to your mails and RUT verified since june 2. Already 4 open cases without answer. ID 178524912400362

• 
  mark seery (@140ismymax) reported 15 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

• 
  absk (@abskwdkr) reported 15 minutes ago

  My ec2 ssh logs in too slow and lags @AWSSupport

• 
  Jonny Castles (@jonny_castles) reported 16 minutes ago

  Not seeing much on here, but seems like a massive outage across platforms? Looks like all AWS linked? @awscloud

• 
  M (@mdw864) reported 17 minutes ago

  @awscloud can you PLEASE answer me? You’ve just turned off my Business because you will not help me. I need you to stop charging me. Do not tell me to login because I do not know the email address that was used to open the account. All I know is my bank account is being charged

• 
  Ranbir Mahapatra (@ranbirnxt) reported 21 minutes ago

  @QuinnyPig @awscloud Is NAT Gateway a ‘pricing’ bug that you would care to fix? Forever?

• 
  zarrakh (@zarrakh) reported 22 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

• 
  Zenwatts 🇺🇸 (@BullCall101) reported 24 minutes ago

  @awscloud Not my problem. Maybe sell a yacht or two and fix it yourself.

• 
  Samrat Singh (Sam) (@isamrats) reported 27 minutes ago

  @KahlonGurshaan @Rushike32313211 Yes its down , but no official confirmation posted by @awscloud yet

• 
  Corey Quinn (@QuinnyPig) reported 33 minutes ago

  I want to hear the inside story of the largest @awscloud project. Not bill run, nor S3. I’m talking about what it took to fix all the hardcoded “jeff@“ userID stuff when @ajassy rose. Not kidding: it woulda been orders of magnitude easier for him to change his name to “Jeff.”

• 
  Piragash Velummylum (@piragash) reported 34 minutes ago

  At @awscloud we call this undifferentiated heavy lifting. @Cometml Opik does the hard work, so you can focus on the customer problems.

• 
  Keith Townsend (@CTOAdvisor) reported 35 minutes ago

  I spent over $120 in @awscloud so far this month. Most of it has been on GPU instances and Xeon 6. This is the most I've spent since I've had a long-running VM as a web app server a few years ago.

• 
  Himanshu Singh (@hsnice16) reported 42 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.

• 
  Harshil Mathur (@harshilmathur) reported 45 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. 🔥

• 
  Kartik Jain (@kartikjain0101) reported 46 minutes ago

  @awscloud you guy has lost your mind. payment got missing so team told me they will raising the request. account got hold, we have 250K unused credits, and now they are not initiation the account. wtf. our whole production is down.

• 
  banhchung (@banhchugxanh) reported 49 minutes ago

  @POTATOCHEAPGAM1 @trythreews @awscloud i'm so down for cloud gaming, but 5gb internet feels like a stretch

• 
  Matt Lea (@schematical) reported 50 minutes ago

  @PhilipAnderse @awscloud @amazon What's the problem? I know a thing or two about AWS.

• 
  masaki (@silenthill_x) reported 50 minutes ago

  @AWSSupport Could someone please take a look at my support case? I opened a case 6 days ago because my RDS Reserved Instance is showing "Payment failed", but the case is still unassigned. The automated AWS Support response confirmed that my account is in good standing, my payment method is valid, and my invoices are fully paid. It also indicated that this appears to be a reservation processing issue rather than a payment issue. I've tried both Phone and Chat but haven't been able to reach an associate. Could you please help me get this case assigned? I can provide the Case ID via DM.

• 
  ErinE (@WhoaNowNelly) reported 51 minutes ago

  @DRiceHockey @WNBA @awscloud Same applies to all their stats. Their PRA is gonna go down too. That's the nature of injuries. Makes it harder for the individual, but does not increase the difficulty of the actual shot itself.

• 
  Guilherme Lage (@ItsEasypop) reported 56 minutes ago

  Another OG goes down. I remember people calling Storj the next Amazon S3 back in 2018

• 
  Kailash Tulsi Gajra (@kailashgajara) reported 56 minutes ago

  Is @awscloud down right now? #AWSDownAgain #AwsDown

• 
  Weaver Labs (@Weaver_Labs) reported 57 minutes ago

  @amazon @awscloud @nvidia AI infrastructure is becoming a full-stack problem. GPUs are only one piece now, with networking, compute and data infrastructure all needing to scale together.

• 
  The Trading Guy (@thetradingguy_) reported 1 hour ago

  @AWSSupport I need resolution on this asap as I am having problem deploying my services. I am using alternative services which are costing a lot. So please resolve this and verify my account for cloudfront Case ID: 178582104300071

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  𝙵𝚊𝚒𝚣𝚎𝚕 𝙿𝚊𝚝𝚎𝚕 ⚡️ (@FaizelPatel143) reported 1 hour ago

  “AI will solve real problems, for the continent.” #AWSSummit2026 @awscloud

• 
  Yuyun Francis (@FrancisYuyun) reported 1 hour ago

  @AWSSupport case ID (178590975600210) "account activation blocked 2+ weeks by Error 880104, case escalated internally Aug 5, no update since."