{"id":108,"date":"2025-08-11T09:00:00","date_gmt":"2025-08-11T09:00:00","guid":{"rendered":"https:\/\/multicloudcommand.com\/?p=108"},"modified":"2025-08-10T16:43:49","modified_gmt":"2025-08-10T16:43:49","slug":"2025-devops-tech-news-aws-and-azures-latest-tools-for-multi-cloud-mastery","status":"publish","type":"post","link":"https:\/\/multicloudcommand.com\/index.php\/2025\/08\/11\/2025-devops-tech-news-aws-and-azures-latest-tools-for-multi-cloud-mastery\/","title":{"rendered":"2025 DevOps Tech News: AWS and Azure\u2019s Latest Tools for Multi-Cloud Mastery"},"content":{"rendered":"\t\t
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AWS and Azure Unleash New DevOps Tools in 2025: What\u2019s Hot for Multi-Cloud<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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The DevOps world is heating up in 2025, with AWS and Azure rolling out cutting-edge tools to streamline multi-cloud workflows. From AI-driven observability to hybrid cloud solutions, these updates are game-changers for DevSecOps engineers. We\u2019ve dug into the latest announcements to bring you actionable insights for your multi-cloud projects. Here\u2019s the scoop on what\u2019s new and how to leverage it to Master Multi-Cloud with Code<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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AWS\u2019s Big Moves: Elastic VMware Service and CloudWatch AI Observability <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/path><\/svg><\/span>\n\t\t\t<\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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AWS kicked off August 2025 with two standout updates. First, the Amazon Elastic VMware Service (EVS)<\/strong> simplifies migrating VMware workloads to AWS while keeping your existing architecture intact. Announced on August 5, 2025, via AWS\u2019s blog, EVS is a win for hybrid cloud setups, letting you integrate with AWS tools like CodePipeline for automated deployments. This reduces migration headaches for teams juggling AWS and on-prem environments.<\/p>

Second, CloudWatch Generative AI Observability<\/strong>, launched in early August 2025, brings AI-powered monitoring to LLMs and agentic apps on services like Amazon Bedrock and ECS. It detects anomalies in real-time, slashing mean time to resolution (MTTR) for complex pipelines. We\u2019ve tested it with Python scripts, and it\u2019s a must-have for proactive multi-cloud monitoring.<\/p>

How to Use It<\/strong>: Pair EVS with Terraform for hybrid cloud setups (see our Terraform guide). For CloudWatch, try this Python snippet to monitor costs:<\/p>

import boto3\nfrom datetime import datetime, timedelta\nclient = boto3.client('cloudwatch')\nresponse = client.get_metric_data(\n    MetricDataQueries=[{\n        'Id': 'cost',\n        'MetricStat': {\n            'Metric': {\n                'Namespace': 'AWS\/Billing',\n                'MetricName': 'EstimatedCharges'\n            },\n            'Period': 3600,\n            'Stat': 'Maximum'\n        }\n    }],\n    StartTime=datetime.utcnow() - timedelta(hours=24),\n    EndTime=datetime.utcnow()\n)\nprint(response['MetricDataResults'][0]['Values'])<\/code><\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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 Azure\u2019s AI Push: GPT-5 in AI Foundry and Arc Enhancements <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/path><\/svg><\/span>\n\t\t\t<\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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Azure made waves on August 7, 2025, with GPT-5 integration in Azure AI Foundry<\/strong>, offering a single endpoint for advanced AI models with intelligent routing. This simplifies automation for DevOps tasks like code reviews or log analysis. The Foundry\u2019s seamless integration with Azure DevOps makes it a powerhouse for multi-cloud pipelines.<\/p>
Azure also showcased Azure Arc<\/strong> and Azure Local<\/strong> in a CSX case study, highlighting their role in hybrid cloud management. Arc lets you manage on-prem and multi-cloud resources alongside AWS or GCP, streamlining data-driven DevOps.<\/p>
How to Use It<\/strong>: Use GPT-5 for automated testing in Azure DevOps. Here\u2019s a starter Python script:<\/p>
from azure.ai.ml import MLClient\nfrom azure.identity import DefaultAzureCredential\ncredential = DefaultAzureCredential()\nml_client = MLClient(credential, subscription_id=\"your-sub-id\", resource_group=\"your-rg\", workspace_name=\"your-workspace\")\n# Query your AI model endpoint (configure with your setup)<\/code><\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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 Why This Matters for Multi-Cloud DevOps <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/path><\/svg><\/span>\n\t\t\t<\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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These 2025 updates underscore the shift toward AI-driven DevOps<\/strong> and hybrid cloud flexibility<\/strong>. AWS\u2019s EVS and CloudWatch tools simplify migrations and monitoring, while Azure\u2019s AI Foundry and Arc enhance automation and cross-cloud control. Together, they empower you to tackle multi-cloud complexity with code.<\/p>
Get Started<\/h3>