AWS

AWS has extended support for Graviton4-based R8g and M8g database instances to numerous new regions, including Europe, Asia Pacific, and South America. R8g is now generally available for Aurora and RDS engines, delivering up to 40% performance gains and 29% better price-performance compared to Graviton3. M8g support for RDS engines has also widened to additional locations across North America, Europe, and Asia.

• R8g Graviton4 instances are now GA in 10+ new regions for Aurora and RDS.
• M8g instances see expanded regional availability for RDS PostgreSQL, MySQL, and MariaDB.
• Expect up to 40% performance improvement and 29% cost efficiency over Graviton3.
• Review regional quotas and capacity plans for new Graviton4 instance types.

Amazon RDS and Aurora now offer R8gd and M8gd instances in additional regions, bringing Optimized Reads capabilities to PostgreSQL, MySQL, and MariaDB. These instances leverage local NVMe storage for ephemeral data like temporary tables, significantly reducing network I/O. This architecture delivers up to 165% better throughput and 120% improved price-performance compared to R6g instances for Aurora PostgreSQL.

• R8gd and M8gd instances are now available in 12 and 6 additional regions respectively.
• Local NVMe storage handles ephemeral data, lowering query latency for complex operations.
• Aurora PostgreSQL sees up to 165% throughput gains and 120% price-performance improvements.
• Optimized Reads support extends to RDS for MySQL and MariaDB alongside PostgreSQL.

Amazon EC2 G7e instances, powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, are now live in Frankfurt, Stockholm, and Mumbai. These instances deliver up to 2.3x inference performance over G6e and support up to 8 GPUs with 96 GB memory each. They are optimized for LLMs, agentic AI, spatial computing, and hybrid graphics-AI workloads.

• G7e instances launched in EU (Frankfurt, Stockholm) and APAC (Mumbai) regions
• Up to 2.3x inference performance improvement compared to G6e instances
• Features 8x NVIDIA RTX PRO 6000 Blackwell GPUs with 96 GB memory each
• Optimized for LLMs, multimodal AI, spatial computing, and physical AI workloads
• Powered by 5th Gen Intel Xeon processors supporting up to 192 vCPUs

Amazon RDS now permits up to four storage modifications per database instance within a rolling 24-hour window. This change eliminates the previous six-hour cool-off period, allowing operators to initiate new adjustments immediately after the prior optimization completes. The update supports increasing volume size, changing storage types, and adjusting performance without downtime across PostgreSQL, MariaDB, and other supported engines.

• Removes the six-hour cool-off period between storage modifications for faster operational agility.
• Allows up to four storage changes (size, type, performance) per instance in a 24-hour window.
• Modifications are non-disruptive, keeping applications running with minimal performance impact.
• Improves responsiveness to sudden data growth or unexpected workload spikes.
• Available on Amazon RDS for PostgreSQL, MariaDB, and other supported engines.

AWS Elastic Disaster Recovery now accelerates failover for workloads already hosted on Amazon EC2 by bypassing unnecessary preparation steps. This optimization reduces recovery time by up to 65% for Windows instances and 40% for Linux. The service automatically handles networking, drivers, and licensing while allowing administrators to toggle this faster mode per account or server.

• Skip prep steps for AWS-hosted source servers to cut RTO significantly.
• Up to 65% faster recovery for Windows and 40% for Linux workloads.
• Networking, drivers, and licensing are applied automatically during failover.
• Control granularity allows enabling faster recovery per account or individual server.

Amazon CloudWatch now supports a lookup processor that enriches log events by matching fields against uploaded CSV reference tables within the pipeline. This feature allows engineers to append metadata, such as mapping IP addresses to application teams or user IDs to details, directly during ingestion. The processor executes these matches in real-time, eliminating the need for external services to perform data enrichment.

• Upload CSV lookup tables directly in the CloudWatch Pipeline config for immediate use.
• Enrich logs with external context like team ownership or user details at ingestion time.
• Matches log fields against table keys to append specified metadata columns to events.
• Simplifies workflows by removing the need for separate enrichment microservices.