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Jassy reiterates that AWS can become a trillion-dollar revenue business

On October 30, 2026, Amazon CEO Andy Jassy reiterated on the quarterly earnings call that AWS is expected to become a trillion-dollar annual revenue business within the next 5 to 10 years, and he expects growth rates to exceed 40% in both 2027 and 2028. He also said that cash capital expenditures in 2026 will be about $220 billion, most of it directed to AWS, and will increase significantly further in 2027. On the same day, Amazon reported its third-quarter 2026 financial results: net sales rose 12% year over year to $201.94 billion, slightly below expectations; AWS net sales rose 39.3% year over year to $45.98 billion, the highest single-quarter growth rate in nearly five years. Purchases of property and equipment in the quarter reached $77.26 billion, and free cash flow over the past 12 months swung from an inflow of $14.79 billion in the same period a year earlier to an outflow of $42.36 billion; the company maintained its full-year 2026 cash capital expenditure guidance of about $220 billion unchanged. After the results were released, the stock fell by as much as more than 10% in after-hours trading.

View story:Andy Jassy reiterates that AWS can become a trillion-dollar revenue business

AWS introduces new application and team permission management for QA Studio

On October 29, 2026, the AWS Machine Learning Blog published a post introducing two new capabilities added to QA Studio based on Amazon Nova Act. The first is applications: it groups test cases and test suites into application containers with owners, records CloudWatch metrics by application and environment, and provides a health score from 0 to 100, starting from the pass rate and deducting points for flaky tests, with support for two trend windows: 7 days/hourly and 30 days/daily. The second is team-based user management: it establishes five cumulative roles—Admin, Team Lead, Editor, Executor, and Viewer—where each application belongs to only one team, and permissions are managed by application rather than by individual test. There are currently no follow-up development reports.

AWS introduces enterprise-grade Agentic AI platform blueprint based on AgentCore

On October 29, 2026, AWS released an enterprise-grade Agentic AI platform blueprint based on Amazon Bedrock and AgentCore. The blueprint adopts a federated operating model: a central Platform team provides governed shared services, while each Workstream team retains ownership of its own agents, tools, and data. The platform enforces model admission, regional restrictions, rate and token quotas, and budget controls through an independent inference gateway and MCP tool gateway, and uses an evaluation pipeline to perform gate checks before agents go live. There are currently no subsequent developments.

AWS introduces new application and team permission management in QA Studio

On October 29, 2026, the AWS Machine Learning Blog published a post introducing two new capabilities added to QA Studio based on Amazon Nova Act. The first is applications: it groups test cases and test suites into application containers with owners, records CloudWatch metrics by application and environment, and provides a health score from 0 to 100, starting from the pass rate and deducting points for flaky tests, with support for two trend windows: 7 days/hourly and 30 days/daily. The second is team-based user management: it establishes five cumulative roles—Admin, Team Lead, Editor, Executor, and Viewer—where each application belongs to only one team, and permissions are managed by application rather than by individual test. There are currently no follow-up development reports.

View story:AWS introduces new application and team permission management for QA Studio

AWS introduces enterprise-grade Agentic AI platform blueprint based on AgentCore

On October 29, 2026, AWS released an enterprise-grade Agentic AI platform blueprint based on Amazon Bedrock and AgentCore. The blueprint adopts a federated operating model: a central Platform team provides governed shared services, while each Workstream team retains ownership of its own agents, tools, and data. The platform enforces model admission, regional restrictions, rate and token quotas, and budget controls through an independent inference gateway and MCP tool gateway, and uses an evaluation pipeline to perform gate checks before agents go live. There are currently no subsequent developments.

View story:AWS introduces enterprise-grade Agentic AI platform blueprint based on AgentCore

Dharmendra Raghuvanshi said Chai Point's monthly AWS bill fell 58.2%

Dharmendra Raghuvanshi of Chai Point said the company's monthly AWS bill dropped from $10,877 to $4,550, a 58.2% reduction. According to Analytics India Magazine, about 80% of the savings came from an Agentic Cost Audit performed by a senior engineer using Claude Code: the audit examined production and QA cloud accounts in read-only mode, identifying idle and orphaned resources, over-provisioning and misconfigurations, and ranked them by savings amount, implementation cost and risk. The remaining roughly 19% came from switching AWS reseller to Isaga for a 5% discount, and from Savings Plans reducing committed spending by about 15%. The report said the platform still supports more than 8,000 connected brewing devices across over 170 stores.

AWS launches SOCI-indexed images for SageMaker AI jobs

On October 28, 2026, AWS launched SOCI-indexed images for Amazon SageMaker AI training and processing jobs, enabling containers to start within seconds through lazy loading without requiring changes to job definitions. Official benchmarks published by AWS Machine Learning show that download times for images ranging from 1.8–35.4 GB dropped from 48 seconds to 7 minutes 17 seconds down to 3–11 seconds, a speedup of 8x–46x, with GPU idle time reduced by up to 90%. If the index is unreadable or lazy loading fails, jobs automatically fall back to standard downloads. There are currently no further developments.

AWS launches SOCI-indexed images for SageMaker AI jobs

On October 28, 2026, AWS launched SOCI-indexed images for Amazon SageMaker AI training and processing jobs, enabling containers to start within seconds through lazy loading without requiring changes to job definitions. Official benchmarks published by AWS Machine Learning show that download times for images ranging from 1.8–35.4 GB dropped from 48 seconds to 7 minutes 17 seconds down to 3–11 seconds, a speedup of 8x–46x, with GPU idle time reduced by up to 90%. If the index is unreadable or lazy loading fails, jobs automatically fall back to standard downloads. There are currently no further developments.

View story:AWS launches SOCI-indexed images for SageMaker AI jobs

AWS says Amazon Connect has been named a Leader in the CCaaS Magic Quadrant for the fourth consecutive year

On October 28, 2026, AWS announced that its Amazon Connect has been named a Leader in the 2026 Gartner Magic Quadrant for Contact Center as a Service (CCaaS) for the fourth consecutive year. In an official blog post, AWS introduced that Amazon Connect combines customer context with generative and agentic AI, emphasizing driving complex issues all the way through to resolution rather than merely transferring calls, with every step auditable and human intervention available when necessary. Currently, this news comes only from AWS's official source, with no other independent reports or subsequent developments yet.

AWS says Amazon Connect named a Leader in the CCaaS Magic Quadrant for the fourth consecutive year

On October 28, 2026, AWS announced that its Amazon Connect has been named a Leader in the 2026 Gartner Magic Quadrant for Contact Center as a Service (CCaaS) for the fourth consecutive year. In an official blog post, AWS introduced that Amazon Connect combines customer context with generative and agentic AI, emphasizing driving complex issues all the way through to resolution rather than merely transferring calls, with every step auditable and human intervention available when necessary. Currently, this news comes only from AWS's official source, with no other independent reports or subsequent developments yet.

View story:AWS says Amazon Connect has been named a Leader in the CCaaS Magic Quadrant for the fourth consecutive year

Chai Point says auditing with Claude Code cut its monthly AWS bill by 58.2%

Dharmendra Raghuvanshi of Chai Point said the company's monthly AWS bill dropped from $10,877 to $4,550, a 58.2% reduction. According to Analytics India Magazine, about 80% of the savings came from an Agentic Cost Audit performed by a senior engineer using Claude Code: the audit examined production and QA cloud accounts in read-only mode, identifying idle and orphaned resources, over-provisioning and misconfigurations, and ranked them by savings amount, implementation cost and risk. The remaining roughly 19% came from switching AWS reseller to Isaga for a 5% discount, and from Savings Plans reducing committed spending by about 15%. The report said the platform still supports more than 8,000 connected brewing devices across over 170 stores.

View story:Dharmendra Raghuvanshi said Chai Point's monthly AWS bill fell 58.2%

AWS releases an edge vision model lifecycle solution based on SageMaker SDK v3

On October 27, 2026, AWS released a fleet-level edge computer vision model lifecycle solution based on the SageMaker Python SDK v3. The solution replaces the deprecated SageMaker Edge Manager with SageMaker Neo for edge optimization, and uses a dual-branch SageMaker Pipeline to train an anomaly detector and a ResNet18 defect classifier for each camera; after passing quality gates, the models are compiled and registered to the Model Registry, with Step Functions controlling the fleet training cadence. AWS states that a full pipeline run on synthetic data costs approximately $0.07 in compute. There are currently no further developments.

AWS releases edge vision model lifecycle solution based on SageMaker SDK v3

On October 27, 2026, AWS released a fleet-level edge computer vision model lifecycle solution based on the SageMaker Python SDK v3. The solution replaces the deprecated SageMaker Edge Manager with SageMaker Neo for edge optimization, and uses a dual-branch SageMaker Pipeline to train an anomaly detector and a ResNet18 defect classifier for each camera; after passing quality gates, the models are compiled and registered to the Model Registry, with Step Functions controlling the fleet training cadence. AWS states that a full pipeline run on synthetic data costs approximately $0.07 in compute. There are currently no further developments.

View story:AWS releases an edge vision model lifecycle solution based on SageMaker SDK v3

Jassy says most AWS capacity is already booked through 2028

Amazon CEO Andy Jassy said that most of AWS's capacity is already booked through 2028. In an interview on the Andreessen Horowitz podcast, he revealed that AWS's large-scale compute capacity for 2026 has been fully booked, 2027 is essentially sold out, and a considerable portion of 2028 has also been locked in early by customers. Jassy mentioned that Amazon disclosed earlier this year that capital expenditure for 2026 will reach about $220 billion, mainly directed toward data centers, servers, and networking equipment; but he also said that data centers, power, and memory resources are tight, and the shortage may not end in 2027. He also said that 99% of AWS's top 1,000 EC2 customers already use its self-developed Graviton processor, that Trainium chips offer about 30% to 40% better price-performance than comparable processors, and that AI inference costs still need to fall by about tenfold to drive enterprise-scale adoption.

Jassy said most of AWS's capacity is already booked through 2028

Amazon CEO Andy Jassy said that most of AWS's capacity is already booked through 2028. In an interview on the Andreessen Horowitz podcast, he revealed that AWS's large-scale compute capacity for 2026 has been fully booked, 2027 is essentially sold out, and a considerable portion of 2028 has also been locked in early by customers. Jassy mentioned that Amazon disclosed earlier this year that capital expenditure for 2026 will reach about $220 billion, mainly directed toward data centers, servers, and networking equipment; but he also said that data centers, power, and memory resources are tight, and the shortage may not end in 2027. He also said that 99% of AWS's top 1,000 EC2 customers already use its self-developed Graviton processor, that Trainium chips offer about 30% to 40% better price-performance than comparable processors, and that AI inference costs still need to fall by about tenfold to drive enterprise-scale adoption.

View story:Jassy says most AWS capacity is already booked through 2028

AWS makes Continuum code vulnerability detection and remediation generally available

On October 23, 2026, AWS made Continuum's code vulnerability detection and remediation capabilities generally available. The service can find and verify vulnerabilities in code repositories, combine AWS environment information to determine whether an attacker can reach the vulnerability and what resources could be further accessed, rank them by business impact, and provide remediation recommendations; when reproducing vulnerabilities in an isolated sandbox, it provides PoC code and reproduction steps, and fixes are submitted as pull requests to the associated GitHub repository, requiring human review before being applied. Currently, this capability is available only in the US East (N. Virginia) and US West (Oregon) regions. In a benchmark announced on October 5, AWS said that Continuum completed 819 of 920 tasks on CyberGym-E2E, with an end-to-end success rate of 89.0%, compared with the previous publicly reported best of 65.9%; however, the proportion of patches that also fixed the historical vulnerabilities specified by the benchmark was only 37.8%, compared with the previous publicly reported best of 26.2%. The benchmark covers only C/C++ memory safety vulnerabilities in 139 open-source projects, and the tests were conducted by AWS itself.

AWS makes Continuum code vulnerability detection and remediation generally available

On October 23, 2026, AWS made Continuum's code vulnerability detection and remediation capabilities generally available. The service can find and verify vulnerabilities in code repositories, combine AWS environment information to determine whether an attacker can reach the vulnerability and what resources could be further accessed, rank them by business impact, and provide remediation recommendations; when reproducing vulnerabilities in an isolated sandbox, it provides PoC code and reproduction steps, and fixes are submitted as pull requests to the associated GitHub repository, requiring human review before being applied. Currently, this capability is available only in the US East (N. Virginia) and US West (Oregon) regions. In a benchmark announced on October 5, AWS said that Continuum completed 819 of 920 tasks on CyberGym-E2E, with an end-to-end success rate of 89.0%, compared with the previous publicly reported best of 65.9%; however, the proportion of patches that also fixed the historical vulnerabilities specified by the benchmark was only 37.8%, compared with the previous publicly reported best of 26.2%. The benchmark covers only C/C++ memory safety vulnerabilities in 139 open-source projects, and the tests were conducted by AWS itself.

View story:AWS makes Continuum code vulnerability detection and remediation generally available

LMSYS releases SGLang AWS Trainium plugin Beta

On October 22, 2026, LMSYS partnered with AWS to release the SGLang Trainium plugin Beta. Built on the newly released TorchNeuron, the plugin supports LLM inference serving and RL rollout generation on Trainium2 and Trainium3. Initial coverage includes Qwen3 (dense), Qwen3-MoE-30B-A3B, DeepSeek-V2-Lite, GLM-4, and GLM-5.2/5.3, and it supports data parallelism, tensor parallelism, expert parallelism, Radix Cache, BF16/FP8, and asynchronous scheduling. In the first end-to-end heterogeneous RL run, SGLang generated rollouts for Qwen3-MoE-30B-A3B on Trainium3, while Miles trained a GRPO policy with Megatron on a single H200 node. The Beta is currently limited to a small number of teams. The next day, Amazon announced the open-sourcing of TorchNeuron, a backend that converts PyTorch code into operations executable on AWS Trainium, allowing PyTorch code already running on other accelerators to migrate with almost no changes.

AWS publishes tutorial on deriving autoscaling targets for TTS on SageMaker AI

On October 23, 2026, AWS published a tutorial introducing a method for deriving autoscaling targets for TTS on Amazon SageMaker AI. The tutorial deploys the 82M-parameter TTS model Kokoro-82M as a custom container on a SageMaker AI real-time endpoint, and uses measured Q_max (the maximum concurrency a single instance can sustain within the SLO) and T_total (the end-to-end time of one scale-out) to calculate the scale-out threshold, scale-in threshold, and cooldown period, replacing target values set by guesswork. The solution scales on the ConcurrentRequestsPerModel metric natively published by SageMaker AI, avoiding the hidden failure of custom metrics that no one reports. There are currently no further developments.

AWS publishes tutorial on deriving auto-scaling targets for TTS on SageMaker AI

On October 23, 2026, AWS published a tutorial introducing a method for deriving autoscaling targets for TTS on Amazon SageMaker AI. The tutorial deploys the 82M-parameter TTS model Kokoro-82M as a custom container on a SageMaker AI real-time endpoint, and uses measured Q_max (the maximum concurrency a single instance can sustain within the SLO) and T_total (the end-to-end time of one scale-out) to calculate the scale-out threshold, scale-in threshold, and cooldown period, replacing target values set by guesswork. The solution scales on the ConcurrentRequestsPerModel metric natively published by SageMaker AI, avoiding the hidden failure of custom metrics that no one reports. There are currently no further developments.

View story:AWS publishes tutorial on deriving autoscaling targets for TTS on SageMaker AI