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dreamfactory

Custom MCP Server vs. AI Data Gateway: Which Is Right for Enterprise AI?

Apr 6, 2026 By Kevin Hood In DreamFactory
The Model Context Protocol (MCP) is quickly becoming the standard for how large language models connect to enterprise data. As adoption accelerates, engineering teams face a foundational decision: build a custom MCP server from scratch, or adopt an AI data gateway that ships with MCP support, security, and governance out of the box. Both paths have real tradeoffs. This post breaks them down so you can make the right call for your stack, your team, and your risk profile.
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kong

LLM Cost Management: How to Implement AI Showback and Chargeback

Apr 6, 2026 By Alex Drag In Kong
Every enterprise moving AI into production is about to face a familiar problem in an unfamiliar form: the cost explosion, but for LLMs. This is *very *similar to what happened with cloud. In the early days of cloud, teams spun up infrastructure with no visibility into who was consuming what. Finance got the bill. Engineering got the blame. No one had the data to make good decisions. It took years of hard-won FinOps discipline to fix that. LLM spend is on the same trajectory *and moving faster*.
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perforce

Understanding ISO/PAS 8800 for AI in Automotive Safety

Apr 6, 2026 By Perforce In Perforce
With the rise of AI use in vehicle software development, concerns arise around its presence in safety-critical applications, especially when it comes to functional safety and regulatory compliance. ISO 26262, the essential standard for automotive development that requires processes for managing, designing, and verifying safety-critical systems, still applies. However, it can fall short when applied to AI models, which are inherently non-deterministic and continuously evolving.
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Hevo's Next Evolution

Apr 6, 2026 By Hevo Data In Hevo
Every company has an AI roadmap. Very few have the data infrastructure to execute it. At Hevo Data, we've spent 8 years building pipelines that are reliable, simple, and transparent so 2,000+ data teams can build without second-guessing their data. We sat down with Manish Jethani, Amit Gupta, and Scott Husband to talk about what comes next. If your data isn't AI-ready, your roadmap stays a roadmap. We've re-engineered the platform to serve as the context engine your AI vision actually runs on. Because the models are only as good as the data underneath them.
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Why 90% of AI Projects Never Leave the Pilot Phase? #ai #shorts #softwarearchitect

Apr 3, 2026 By WSO2 In WSO2
Struggling to scale your AI? You aren’t alone. Shafrine from WSO2 identifies the bottleneck holding companies back: Data Silos. Without integration, your AI agents lack the "context" needed to be useful in a production environment. Learn how to bridge the gap between a "cool pilot" and a "scalable enterprise agent" by fixing your fragmented workflows.
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InsightSoftware

How Manufacturing Leaders Deploy AI Faster with Governance-First Architecture

Apr 3, 2026 By InsightSoftware In InsightSoftware
AI workflows for manufacturing need to be deployed quickly. Quality control systems, predictive maintenance tools, and supply chain optimization algorithms may be going live, yet compliance infrastructure is lagging behind. The result is a familiar pattern: pilots that prove out technically but stall before production because they can’t clear audit, safety, or regulatory review.
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dreamfactory

Why Audit Logs Matter for AI Governance | DreamFactory

Apr 3, 2026 By Kevin Hood In DreamFactory
Audit logs are essential for making AI systems accountable, reliable, and compliant with regulations. They act as a record-keeping system, documenting every critical interaction within an AI system, such as user prompts, model decisions, and policy enforcement. Here's why they are crucial: Audit logs are not just a legal requirement - they are a key part of managing AI systems effectively and minimizing risks.
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katalon

AI Testing Best Practices - Why Human Governance Separates Real AI Platforms from Hype

Apr 2, 2026 By Huyen Nguyen In Katalon
There is a scenario playing out in QA teams everywhere right now. A team adopts an AI testing tool, runs it for the first time, and gets 300 test cases in minutes. The demo worked. The ROI math looked great. But three sprints later, 60 of those test cases are validating requirements that were updated in the last sprint. Twenty more test a user flow that was deprecated. The AI performed exactly as advertised. The governance system never existed.
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oril

From Executors to Strategic Partners: The Evolution of Software Vendors in the AI Era

Apr 2, 2026 By Zorian Fedoryga In ORIL
Artificial intelligence is transforming the global software industry. Some analysts refer to this shift as a “SaaS apocalypse,” with traditional software companies losing over a trillion dollars in market value. Historically, software vendors executed client visions by writing code. Now, as clients articulate their needs and AI generates code, the industry faces a critical question: What role remains for software vendors? This requires a fundamental shift.
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