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	10 days ago
Pottstown	Website Down	14 days ago
Iztacalco	Website Down	18 days ago
Boca da Mata	Errors	29 days ago
Township of Evan	Website Down	1 month ago
New York City	Website Down	2 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:

• 
  X anonyn (@OmrX304) reported 3 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

• 
  Krishna.Ki.Shor (KK) (@kkbava) reported 3 minutes ago

  @AmazonHelp You are still giving me work!😡 Why not look inwards and check if order was indeed delivered? And tell the software geeks and nerds that there is a problem with conflicting messages? Learn from failures guys! That's what I learnt from @awscloud

• 
  Matthew Joughin | 🏗️ Cross Platform Dev Tools (@mrwcjoughin) reported 7 minutes ago

  @jeffdafo @ivanburazin @awscloud Even aspnetcore can run on Linux now - there is no excuse to have (and pay through the nose) for Windows server licenses)

• 
  MrWho (@Mrwho23) reported 7 minutes ago

  @AWSSupport I create a case for your support and till now waiting for the reply My whole system and company is waiting for your support, but you are so slow

• 
  🦋Kieran🦋 💻 (@sir_divs_alot) reported 8 minutes ago

  @AWSSupport Thanks for your response and I've been keeping track for any updates very closely but the problem is these cases are either unassigned and if they are, no one is following up. I'm at the point of just giving up entirely cuz it's pointless waiting endlessly.

• 
  ShenYubao (@ssybb1988) reported 9 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

• 
  barney (@Southclaws) reported 12 minutes ago

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

• 
  Stephen “The Yellow Dart” Schutt (@schuttsm) reported 12 minutes ago

  @QuinnyPig @awscloud I get charged $0.55 every month. Fix that

• 
  John Darin Holloway (@bofkentucky) reported 16 minutes ago

  @QuinnyPig @awscloud Hopefully better than the "new" broken elastic beanstalk console experience.

• 
  Corey Quinn (@QuinnyPig) reported 18 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.”

• 
  Jaimin Vaghani (@jaiminvaghani) reported 19 minutes ago

  @AWSSupport @AWSSupport Appreciate the reply, but I've heard this exact message every day this week. At what point does a 5+ day production-down case qualify for actual escalation? Please have an escalation manager or TAM contact me directly, the Support Center loop isn't working.

• 
  Jaimin Vaghani (@jaiminvaghani) reported 21 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.

• 
  Chris (@Mcatbeast) reported 22 minutes ago

  @awscloud has to have the worst sign-in page EVER. Every time I try to login as an IAM user it’s a complete headache and takes 30 minutes to figure out

• 
  Hitesh Kumar Gupta (@iHiteshAgrawal) reported 22 minutes ago

  @kunalstwt @WeMakeDevs @awscloud Tried registering but the form needs a student email ID. My college doesn't issue one, which is common in tier 2 and 3 colleges. Happy to send university marksheets or college ID instead. Otherwise the online-for-all-India part misses the students who'd benefit most.

• 
  Dhanush N (@Dhanush_Nehru) reported 26 minutes ago

  Opened a support portal ticket on AWS. For a week, I heard nothing. contacted @AWSSupport by direct message and the issue was resolved in a matter of days. Their team still responds to thousands of queries every day. That's how genuine support appears. Thank you AWS!

• 
  Catalin C (@CatalinC_Uk) reported 28 minutes ago

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

• 
  Reina Cruz 🥼🧤🇨🇺 (@rea1ReinaCruz) reported 28 minutes ago

  @Cloudflare @awscloud Fix human verification

• 
  Nathan Den Herder (@natedenh) reported 28 minutes ago

  @grok @awscloud @claudeai That makes it too slow, I tried that already

• 
  Payal (@payal_codes) reported 35 minutes ago

  Day 1 : "How to Scale an App to 10 Million Users on AWS" If I have to design a system for 10 million users, I won't build everything on Day 1 because it will add unnecessary complexity and cost. I'll start simple with one application server and one database. As traffic grows, if the server starts reaching its CPU, memory, or storage limits, I'll first scale vertically by moving to a bigger instance. Once that is not enough, I'll separate the backend and database so both can scale independently. To avoid a single point of failure, I'll deploy the application across multiple Availability Zones and put a Load Balancer in front so if one server or AZ goes down, traffic is automatically routed to healthy servers. As the number of users keeps increasing, I'll make my application stateless by storing sessions in Redis. This allows me to add multiple application servers behind the Load Balancer and scale horizontally. If my database starts getting overloaded with reads, I'll use Redis to cache frequently accessed data and add read replicas to distribute read traffic. For static assets like images, CSS, and JavaScript, I'll store them in Amazon S3 and serve them through CloudFront so requests don't keep hitting my application servers. If traffic suddenly spikes during sales or events, I'll enable Auto Scaling with CloudWatch metrics so AWS automatically adds or removes servers based on demand. As the application becomes larger, I'll split the monolith into microservices. This allows each service, like authentication, payments, or notifications, to scale independently instead of scaling the entire application. If the database becomes the bottleneck, especially for write operations, I'll use sharding or federation depending on the data and business requirements. Finally, when users are spread across the world, I'll deploy the application in multiple AWS Regions to reduce latency and improve availability. My approach is always the same: find the bottleneck, solve that bottleneck, and only introduce more complexity when the current architecture can no longer handle the traffic.

• 
  Sumedh More (@DroidBoyJr) reported 38 minutes ago

  Hey @awscloud @AWSSupport im trying Bedrock with root user and getting NOT_AUTHORISED error with 400. Tried support and the ai assistant couldn't find anything. Tried raising ticket, it asked me to upgrade support plan. Plan upgrade failing with error. Going nowhere #SOS

• 
  Harikrishna KP (@harikp2002) reported 39 minutes ago

  @AWSSupport Two days into migrating to AWS and both new accounts I created are locked out of every single service. S3, EC2, Lambda, DynamoDB, all of it. The accounts show as ACTIVE. Support confirmed it needs a manual fix from an internal team, then went quiet for 28 hours. Three open cases, one phone call, still completely blocked. Sitting on AWS credits I literally cannot spend. Do better please!

• 
  Nicholas Griffin (@ngriffin_uk) reported 39 minutes ago

  @QuinnyPig @awscloud that’s real? it looks like someone who doesnt know how to prompt made a sign in screen and didnt look at it.

• 
  Quoc Huy (@q_huy_ngo) reported 40 minutes ago

  @Cloudflare @awscloud mine transacted with Cloudflare about 700M times because of a broken DO alarm 🥲 no users, just a dev app, then a $655.83 bill. i fixed it and opened case 02252354 a month ago. still no billing decision and now i’m downgraded. can someone pls help? i really can’t afford this

• 
  Ricci Research (@ricci_nov) reported 46 minutes ago

  @nvidia @awscloud The buried detail is "Vera CPU server" — NVIDIA hand-delivering an ARM CPU to the company that built Graviton to escape CPU vendors. The GPU is the ticket; the CPU is the trojan horse into the one socket AWS thought it had reclaimed.

• 
  TAPE Vector (@Tape_Vector) reported 46 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.

• 
  Harun R. (@harundotdev) reported 47 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.

• 
  Matthew Joughin | 🏗️ Cross Platform Dev Tools (@mrwcjoughin) reported 48 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

• 
  Ikenna Iheanaetu (@Ikenna_dev) reported 55 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.

• 
  Omni (@omnishredder_) reported 56 minutes ago

  @AmazonUK @Twitch @awscloud sort yo ****, I'm getting tired of ironically the last two days unable to watch twitch without lag or buffering at random with no explanation, it's almost like the ******* AI training is the problem that nobody wanted! How about ******* remove it!

• 
  Mahadev Vyavahare (@mahadevkv) reported 57 minutes ago

  @SarvamAI should learn from American companies on how to and what to document on internet Documentation and visibility is broken compared on sarvam website. Learn from @OpenAI and @awscloud