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 (83%)
• Errors (17%)

Live Outage Map

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

City	Problem Type	Report Time
Pottstown	Website Down	2 days ago
Iztacalco	Website Down	5 days ago
Boca da Mata	Errors	17 days ago
Township of Evan	Website Down	1 month ago
New York City	Website Down	1 month ago
Ciudad Jardín	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:

• 
  Christian Nonis (@christiannonis) reported 4 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

• 
  Nachman Mostofsky ✡︎🇺🇸 (@Mostofsky) reported 4 minutes ago

  @awscloud I signed up w/ new account to get @AWS_Gov. Got usual error. Created ticket. At about 3pm. Just got my phone call. At 11:30. AT NIGHT! To then get a message while on hold that there was an error, that a tech was assigned to case & would call me back. C'mon!

• 
  ShenYubao (@ssybb1988) reported 6 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

• 
  Onkar Deshpande (@i_am_onkar) reported 11 minutes ago

  @bilal_akh @awscloud what were potential issues? account compromised? keys exposed? last time this happened for me was when some keys got leaked and someone emptied my startup credits -this was 8years ago tho

• 
  Nicholas Griffin (@ngriffin_uk) reported 15 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.

• 
  Stephen “The Yellow Dart” Schutt (@schuttsm) reported 15 minutes ago

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

• 
  Arian van Putten (@ProgrammerDude) reported 16 minutes ago

  @QuinnyPig @awscloud Are they gonna fix not being able to have to accounts logged in in two different tabs?

• 
  Adam 🤗 (@AdamMolnarHF) reported 16 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!

• 
  Jacklyn Taylor (@Ronindrake2) reported 17 minutes ago

  @awscloud hey, got a simple question When your moderation refuses to post a review of a book, can you do everyone a favor and instead of saying "one of these things" Say "here is the specific part that violates our guidelines. Pls fix it" It would be, so much more helpful

• 
  𝙵𝚊𝚒𝚣𝚎𝚕 𝙿𝚊𝚝𝚎𝚕 ⚡️ (@FaizelPatel143) reported 17 minutes ago

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

• 
  MrWho (@Mrwho23) reported 18 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

• 
  Dhananjay (@DhananjayKhark2) reported 23 minutes ago

  @AWSSupport Case open since Aug 13 — 15 days, no fix! AWS Glue blocked with AccessDeniedException (account-level) in ap-south-1. NOT an IAM issue. Needs backend account verification sync. Case: 178661799100381 Please escalate! 🙏 #AWS #AWSGlue @awscloud @AWSCloudIndia

• 
  Rami (@KingRomstar) reported 31 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.

• 
  Sai Manikanta ☕ (@rebuiltWithSai) reported 33 minutes ago

  Day 19 of #JobSwitchUntilPlaced 🚀 Today's progress: ✅ Started a new Microservices project by building the User Service ✅ Solved 2 DSA problems ✅ Learned about Blob Storage (Amazon S3) One step closer to building production-ready, scalable backend systems.💪 #BuildInPublic

• 
  Jaimin Vaghani (@jaiminvaghani) reported 34 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?

• 
  Kenton Parton (@kenton_parton) reported 35 minutes ago

  Anyone here work on AgentCore Gateway Targets at @awscloud? Been testing them for our MCP/agent platform. I like that it removes the credential leakage issue during development of new MCP's/Agents. But they’re slow AF!🐢 I’m seeing ~430ms added per request above baseline, in-region, with warm caches. Is that expected?

• 
  The AI Therapist (@TheAIShrink) reported 35 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

• 
  OneTradeWex (@OneTradeWex) reported 36 minutes ago

  $CBRS is trying to challenge $NVDA indirectly, here’s how: While $NVDA focuses on incredibly small and powerful chips, $CBRS has built a massive, highly specialized AI scaling semiconductor. This greatly decreases the amount of cloud tokens needed to process a large language model, effectively saving AI companies millions of dollars in the long run. Think of $NVDA racks as an industrial kitchen and $CBRS chips as a microwave, when all you need to do is cook a hot pocket you want the microwave for efficiency. $CBRS announced that core revenue more than doubled year-over-year to $210 million in its Q2 earnings report while its cloud business nearly quadrupled. The company also raised its 2026 revenue guidance to between $880 million and $890 million. If $CBRS can successfully fulfill their contracts with @OpenAI and @awscloud on time and at scale we will see even more growth, if not them only having two primary customers will be their down fall. Looking at the technical setup we see $CBRS is coming down into its months long support channel. If it holds the $180-$160 level I see no reason for it not to bounce back to $215 to $230.

• 
  Piragash Velummylum (@piragash) reported 39 minutes ago

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

• 
  Chaz Wargnier ♨️ (@DataChaz) reported 41 minutes ago

  Let's take @Snowflake as an example. Working there gave me a clear view of the data problems hiding behind most ambitious AI roadmaps. Because Snowflake is natively deployed on AWS and available through the AWS Marketplace, it fits right into a modern stack. If you are building an AI feature, a sensible pattern looks like this: > Isolated customer events land securely in Amazon S3 from day one > Snowflake transforms raw data into a governed, usable layer > Flexible Amazon ECS compute routes only approved context to Amazon Bedrock Getting this pipeline right early means less time fixing data leaks and more time shipping AI features ↓

• 
  The Trading Guy (@thetradingguy_) reported 42 minutes ago

  @AWSSupport , got no replies on this upon follow up message. Please look into it. Why is it so tough to resolve an issue?

• 
  Mon (@mon73x) reported 43 minutes ago

  @asimrazax @AWSSupport I have the same problem. Did you fix it?

• 
  Matthew (@OneShotCaller) reported 49 minutes ago

  @ZZiata15569 @awscloud I have billing and budget alerts set up with AWS already. Main problem with a lot of tools I’ve used is too much noise in the alerts vs AWS option. Still, I think every AWS customer gets at least 1 surprise lol… part of initiation (onboarding?)

• 
  @Randomfrequency@mastodon.social (@randomfrequency) reported 49 minutes ago

  @QuinnyPig @awscloud There is only an A, B, and C zone and never shall we acknowledge the existence of any other ones. // Being part of the problem since 20XX

• 
  TAPE Vector (@Tape_Vector) reported 52 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.

• 
  Graham Christensen (@grhmc) reported 53 minutes ago

  @apparentorder @QuinnyPig @awscloud I'm sure they'll fix it after they're done at the discotheque

• 
  Brad (@beeradmoore) reported 54 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

• 
  masaki (@silenthill_x) reported 57 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.

• 
  Ümit Kaan Usta (@umitkaanusta) reported 1 hour ago

  @AWSSupport I keep getting errors across the AWS Console where components or resource lists fail to load, showing messages like “An error occurred calling the API: describeAddresses.” Reloading sometimes fixes it temporarily. can you help? It's mostly on EC2 related stuff. us-east-1

• 
  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.