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Amazon launches Kiro, its own Claude-powered challenger to Windsurf and Codex

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Amid the big news that Windsurf is being acquired by Cognition (after its founders went to Google), developers interested in AI-powered coding may be on the hunt for new […]

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Amid the big news that Windsurf is being acquired by Cognition (after its founders went to Google), developers interested in AI-powered coding may be on the hunt for new alternatives.

In a bit of fortuitous timing, today also saw Amazon’s release of Kiro, a new agentic Integrated Development Environment (IDE) built to help developers move from prototype to production using AI workflows grounded in structure, planning, and engineering rigor.

Kiro uses Amazon investment Anthropic‘s Claude Sonnet 3.7 and 4.0 as the default model backends. Users can switch between them, and future support for other models may be added.

Now in public preview, Kiro runs on macOS (Intel and Apple Silicon), Windows, and Linux for free to start (limited to $50 interactions per user per month), with additional pricing tiers starting at $19 for more features.

Kiro aims to bridge the gap between “vibe coding” — allowing AI to generate full blocks of code and even entire software processes and applications from plain text instructions, typically used by enterprises for rapid prototyping and interation — and the more demanding process of delivering secure, maintainable, and scalable applications in real-world environments.

The tool combines AI agents with project specifications, technical architecture, and automated task management to support a complete software development lifecycle inside a single interface.

Kiro vs. Q Developer?

But didn’t Amazon already have its own AI-code completion tool, Q Developer? Yes, and that’s still available for those users who rely on it.

So why launch a whole new product and brand name that offers some of the same functionality? Sources at Amazon told VentureBeat that “Kiro is a general-purpose agentic IDE for developers to work with any platform of their choice,” as opposed to Q Developer, which is more limited in its support for third-party IDEs, restricted to VSCode, JetBrains, Eclipse, and Visual Studio.

In addition, the sources pointed out that Kiro’s agentic spec-driven development was radically different from the code suggestions offered on discrete snippets by Q Developer. They said some developers may even prefer to use both in tandem, which is supported via the Q Developer Pro subscription which starts at $19 per month per user.

From prompt to production with spec-driven development

Kiro’s key differentiator is its spec-driven development model, which guides the process from ideation to implementation. A simple prompt like “Add a review system” triggers a chain of AI-assisted outputs that include:

  • User stories with acceptance criteria in EARS (Easy Approach to Requirements Syntax) format
  • Design documents with data flow diagrams, TypeScript interfaces, and API schemas
  • Task lists and sub-tasks automatically sequenced by dependency, with tests, loading states, and accessibility built-in

Developers can execute these tasks one at a time through Kiro’s built-in agent interface, with inline diffs, progress tracking, and access to historical agent execution logs. As development proceeds, Kiro keeps specs in sync with the codebase, helping teams avoid the typical drift between documentation and implementation.

Agent hooks automate routine quality tasks

Kiro’s agent hooks allow developers to configure automation triggers for everyday tasks like regenerating tests, updating documentation, or running security scans.

Hooks can be tied to actions such as saving files, editing components, or pushing commits. Once set up and checked into the repo, they provide team-wide consistency in code quality and standards enforcement.

For instance, developers can define a hook to ensure new React components follow the Single Responsibility Principle or trigger a secrets scan before commits. This approach adds automated quality control without slowing down individual developers.

Kiro is built on Code OSS, the open source foundation of Visual Studio Code, maintained by Microsoft. It provides the core editor experience without proprietary services, allowing third parties like Kiro to build their own IDEs with full compatibility with VS Code extensions and settings.

As such, Kiro remains compatible with VS Code extensions, settings, and UI conventions. It also supports:

  • Model Context Protocol (MCP) for connecting external tools
  • Agentic multi-modal chat, using files, URLs, or documents as context
  • Steering rules to customize and constrain agent behavior across a codebase
  • Social login via GitHub or Google, with no AWS account required

Pricing and availability

Kiro is currently free for all users during its preview period, including Amazon Q Developer and Q Developer Pro subscribers.

Preview access includes “generous” usage limits aimed at letting developers explore Kiro without frequent disruptions.

After the preview period ends, users will have a choice of three subscription tiers:

PlanMonthly PriceIncluded Agentic InteractionsNotes
Kiro Free$050 per monthSpecs, hooks, steering, and MCP support included
Kiro Pro$191,000 per monthAll features from Free, plus higher usage quota
Kiro Pro+$393,000 per monthDesigned for heavy users or teams

Agentic interactions include any direct invocation of Kiro agents—such as initiating a spec, triggering a hook, or sending a chat prompt. The processing work that happens after a request (like multi-step task execution) does not count toward the quota.

Users on paid plans will also be able to purchase additional interactions at $0.04 each, but overage billing must be explicitly enabled.

The AI-assisted development ecosystem is becoming increasingly crowded, with several prominent IDEs and agents competing for developer attention. Here’s how Kiro stacks up:

ToolSummaryPricing
Amazon Q DeveloperMulti-environment AI assistant integrated into AWS, IDEs, CLI, and chat. Agentic workflows via terminal or IDE. Great for cloud ops, migrations, and automation. Free and Pro tiers available.Free; Pro at $19/user/month
Claude Code (Anthropic)CLI-first coding assistant with chat-based iteration, plan/edit modes, and diff views. Strong for interactive code development, less structured than Kiro.Free; Pro at $17/month or $20 billed monthly
GitHub Copilot (Microsoft)Inline code completion tool in VS Code, GitHub. Best for quick assistance. Lacks structured planning or workflow support.Free trial; Pro $10/month or $100/year; Pro+ $39/month
CursorVS Code–based AI editor for conversational edits and navigation. Optimized for solo coding, minimal planning support.Free; Pro $20/month ($16/month yearly)
Windsurf (OpenAI, acquired)Discontinued AI IDE focused on rapid code editing. Offered minimal planning. Key staff/IP acquired by Google and Cognition in July 2025.Free; Pro $15/month; Teams $30/user/month
Cognition / DevinMulti-agent system capable of autonomous software engineering from planning to deployment. Developer-in‑loop model.Start at $20/month; older tiers at $500/month
KiroPlanning-first AI IDE with structured artifacts like specs, design docs, and task trees; supports feature planning, implementation, and QA automation. Developer‑in‑loop.Free (50 interactions/mo); Pro: $19/user/mo (1,000 interactions); Pro+: $39/user/mo (3,000 interactions)

Developer response and early impressions

Kiro’s launch generated active discussion on startup accelerator Y combinator’s popular developer forum Hacker News, where Nathan Peck, Senior Developer Advocate for Generative AI at AWS (username NathanKP) offered technical context and responded to questions.

He emphasized that Kiro reflects Amazon’s internal engineering practices and is designed to help developers scale from small ideas to robust, production-ready systems.

Initial community reactions were mixed but intrigued. Developers praised the emphasis on specs, hooks, and structure.

Some compared it favorably to tools like Claude Code and Cursor, citing the improved rigor in building and documenting features. Others voiced concern over tool churn and switching costs, while some preferred command line interface (CLI)-based tools or simpler interfaces.

Feedback also surfaced around authentication bugs, platform compatibility, and desire for dev container support. These early responses reflect both curiosity and the high expectations developers now have for AI coding tools.

Kiro enters a crowded field but carves out a niche with its structured, spec-first philosophy and support for developer-in-the-loop workflows.

It’s not trying to replace developers or automate entire codebases blindly. Instead, it’s offering a more disciplined way to collaborate with AI from planning to delivery.

With its preview now open and pricing models outlined, Kiro may appeal most to teams and individuals looking to build not just faster, but more thoughtfully—with long-term maintainability, clarity, and quality built in.

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Enterprises will strengthen networks to take on AI, survey finds

Private data centers: 29.5% Traditional public cloud: 35.4% GPU as a service specialists: 18.5% Edge compute: 16.6% “There is little variation from training to inference, but the general pattern is workloads are concentrated a bit in traditional public cloud and then hyperscalers have significant presence in private data centers,” McGillicuddy explained. “There is emerging interest around deploying AI workloads at the corporate edge and edge compute environments as well, which allows them to have workloads residing closer to edge data in the enterprise, which helps them combat latency issues and things like that. The big key takeaway here is that the typical enterprise is going to need to make sure that its data center network is ready to support AI workloads.” AI networking challenges The popularity of AI doesn’t remove some of the business and technical concerns that the technology brings to enterprise leaders. According to the EMA survey, business concerns include security risk (39%), cost/budget (33%), rapid technology evolution (33%), and networking team skills gaps (29%). Respondents also indicated several concerns around both data center networking issues and WAN issues. Concerns related to data center networking included: Integration between AI network and legacy networks: 43% Bandwidth demand: 41% Coordinating traffic flows of synchronized AI workloads: 38% Latency: 36% WAN issues respondents shared included: Complexity of workload distribution across sites: 42% Latency between workloads and data at WAN edge: 39% Complexity of traffic prioritization: 36% Network congestion: 33% “It’s really not cheap to make your network AI ready,” McGillicuddy stated. “You might need to invest in a lot of new switches and you might need to upgrade your WAN or switch vendors. You might need to make some changes to your underlay around what kind of connectivity your AI traffic is going over.” Enterprise leaders intend to invest in infrastructure

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Such a shift, analysts say, could offer short-term benefits for enterprises, particularly in cost and access, but also introduces new operational risks. “This acquisition may potentially lower enterprise pricing through lease cost elimination and annual savings, while improving GPU access via expanded power capacity, enabling faster deployment of Nvidia chipsets and systems,” said Charlie Dai, VP and principal analyst at Forrester. “However, service reliability risks persist during this crypto-to-AI retrofitting.” This also indicates that struggling vendors such as Core Scientific and similar have a way to cash out, according to Yugal Joshi, partner at Everest Group. “However, it does not materially impact the availability of Nvidia GPUs and similar for enterprises,” Joshi added. “Consolidation does impact the pricing power of vendors.” Concerns for enterprises Rising demand for AI-ready infrastructure can raise concerns among enterprises, particularly over access to power-rich data centers and future capacity constraints. “The biggest concern that CIOs should have with this acquisition is that mature data center infrastructure with dedicated power is an acquisition target,” said Hyoun Park, CEO and chief analyst at Amalgam Insights. “This may turn out to create challenges for CIOs currently collocating data workloads or seeking to keep more of their data loads on private data centers rather than in the cloud.”

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CoreWeave achieves a first with Nvidia GB300 NVL72 deployment

The deployment, Kimball said, “brings Dell quality to the commodity space. Wins like this really validate what Dell has been doing in reshaping its portfolio to accommodate the needs of the market — both in the cloud and the enterprise.” Although concerns were voiced last year that Nvidia’s next-generation Blackwell data center processors had significant overheating problems when they were installed in high-capacity server racks, he said that a repeat performance is unlikely. Nvidia, said Kimball “has been very disciplined in its approach with its GPUs and not shipping silicon until it is ready. And Dell almost doubles down on this maniacal quality focus. I don’t mean to sound like I have blind faith, but I’ve watched both companies over the last several years be intentional in delivering product in volume. Especially as the competitive market starts to shape up more strongly, I expect there is an extremely high degree of confidence in quality.” CoreWeave ‘has one purpose’ He said, “like Lambda Labs, Crusoe and others, [CoreWeave] seemingly has one purpose (for now): deliver GPU capacity to the market. While I expect these cloud providers will expand in services, I think for now the type of customer employing services is on the early adopter side of AI. From an enterprise perspective, I have to think that organizations well into their AI journey are the consumers of CoreWeave.”  “CoreWeave is also being utilized by a lot of the model providers and tech vendors playing in the AI space,” Kimball pointed out. “For instance, it’s public knowledge that Microsoft, OpenAI, Meta, IBM and others use CoreWeave GPUs for model training and more. It makes sense. These are the customers that truly benefit from the performance lift that we see from generation to generation.”

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

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2025 playbook for enterprise AI success, from agents to evals

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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