🪟 AI coding agents blowing budget isn’t “AI failure”—it’s procurement finally discovering the tab. Uber/Microsoft just learned: model quality is free; token burn isn’t. windowsforum.com/threads/ai-… #EnterpriseGovernance #FinopsForAi #AiCodingAgents #ProcurementCosts

FinOps for AI succeeds when you treat cost as a first class design constraint. Here is the exact framework I use with clients. Define AI scopes aligned to business activities. Establish model level visibility and unit economics from day one. Optimize across model, runtime, and infrastructure layers in that order. Govern with quotas, tagging, and real time dashboards. Measure ROI continuously against clear business KPIs. Apply this and you stop 70 percent of pilot failures before they happen. You deliver predictable spend and measurable value at scale. This is no longer optional. It is how leading enterprises run AI today. Save this framework and implement one piece this week. Your next architecture review will thank you. Which step will you tackle first? #FinOpsForAI #AIArchitecture #CostGovernance #InferenceOptimization #SolutionArchitecture #EnterpriseAI #AIFinOps #ROI

FinOps for AI succeeds when you treat cost as a first class design constraint. Here is the exact framework I use with clients. 1. Define AI scopes aligned to business activities. 2. Establish model level visibility and unit economics from day one. 3. Optimize across model, runtime, and infrastructure layers in that order. 4. Govern with quotas, tagging, and real time dashboards. 5. Measure ROI continuously against clear business KPIs. Apply this and you stop 70 percent of pilot failures before they happen. You deliver predictable spend and measurable value at scale. This is no longer optional. It is how leading enterprises run AI today. Save this framework and implement one piece this week. Your next architecture review will thank you. Which step will you tackle first? #FinOpsForAI #AIArchitecture #CostGovernance #InferenceOptimization #SolutionArchitecture #EnterpriseAI #AIFinOps #ROI

Every Solution Architect needs a core FinOps toolkit for AI platforms in 2026. Start with granular monitoring that surfaces tokens, requests, and GPU utilization per model. Layer on unit economics dashboards that connect spend to business outcomes. Add automated guardrails for budget thresholds and anomaly alerts. Choose inference servers like vLLM that bake in optimization by default. Integrate cost visibility directly into CI/CD so architects see price impact before merge. I standardize this stack on every project. It gives teams visibility they never had and turns cost conversations into engineering decisions. Tools alone do not deliver results. The architecture decisions you make with them do. What FinOps tools are you using for your AI workloads today? #FinOpsForAI #AIFinOps #LLMOps #SolutionArchitecture #CostOptimizationTools #EnterpriseAI

One client cut inference costs 62 percent in 90 days using pure FinOps discipline. Here is exactly what we did. We applied quantization and continuous batching first. Then we introduced model routing that sent simple queries to smaller distilled models. Infrastructure changes moved 70 percent of traffic to spot instances with aggressive auto scaling. Governance came next. Real time dashboards surfaced cost per inference by business unit. We tied every spend line to a revenue or productivity KPI. The result was not just lower bills. The team gained confidence to scale the platform because costs stayed predictable. ROI turned positive in quarter two. This pattern repeats across industries. FinOps for AI is now the difference between pilot and production scale. Share your biggest AI cost win in the comments. #FinOpsForAI #InferenceOptimization #CostSavings #AI #CaseStudy #SolutionArchitecture #EnterpriseAI #ROI

Measurable ROI separates successful AI programs from expensive experiments. FinOps gives you the exact framework. Calculate net benefit as tangible savings plus productivity gains minus total investment. Track payback period in months, not years. Focus on five dimensions: cost reduction, revenue impact, error reduction, cycle time compression, and strategic advantage. Move beyond infrastructure metrics. Measure cost per inference against business value delivered per query. Build dashboards that show finance, engineering, and product teams the same numbers in real time. I run these ROI models on every client deployment. Organizations that adopt them see clear payback within 12 months and sustained value thereafter. What is your primary ROI metric for AI initiatives right now? #FinOpsForAI #AIROI #MeasurableROI #AIFinOps #BusinessValue #SolutionArchitecture #EnterpriseAI

Cost governance turns AI spend from unpredictable to controlled. Build it into the architecture, not as an afterthought. Tag every resource at model, endpoint, and workload level. Set hard quotas and automated alerts on anomaly detection. Shift left with pre deployment cost estimates so teams know the price tag before they ship. Track unit economics relentlessly. Cost per inference, cost per token, and cost per successful automation give you the language finance understands. Link those metrics to business KPIs and suddenly everyone aligns. I enforce this governance layer on every enterprise AI platform. It prevents shadow AI and keeps innovation moving fast without budget blowouts. Governance is now table stakes. How do you embed cost controls in your AI reference architectures? #FinOpsForAI #CostGovernance #AIGovernance #EnterpriseArchitecture #SolutionArchitecture #AIUnitEconomics

FinOps for AI just became its own dedicated category in the 2026 FinOps Framework. That tells you everything you need to know about where enterprise spend is heading. Inference now drives 80 percent of AI GPU costs. Training grabs the headlines, but production inference quietly eats the budget. Without proper governance, 70 percent of AI projects stall after pilot stage. Solution Architects own this shift. We design the cost visibility layer from day zero, tag every model endpoint, and tie spend directly to business outcomes. Cost per inference replaces vague GPU hour metrics. The result? Predictable scaling and zero surprise bills. I apply this on every engagement. What unit economics do you track for your AI workloads? Drop your top metric below. #FinOpsForAI #AIInference #CostGovernance #AIFinOps #SolutionArchitecture #EnterpriseAI #ROI

Inference optimization delivers the fastest wins in AI cost control. Focus on three layers and watch your bills drop. Start with the model layer. Apply FP8 quantization and distillation to cut GPU memory by 50 to 75 percent while keeping accuracy intact. Right size aggressively. Smaller models often match performance for most use cases. Move to runtime. Continuous batching and PagedAttention in vLLM boost throughput 40 to 80 percent. Speculative decoding and smart KV cache management squeeze every last token out of your hardware. Finish with infrastructure. Spot instances, auto scaling on queue depth, and specialized silicon deliver another 40 to 65 percent unit cost reduction. I implement this stack on production platforms and routinely hit 60 percent savings in the first quarter. No quality trade off. Which layer are you tackling first on your inference pipeline? #FinOpsForAI #InferenceOptimization #LLMOps #AIOptimization #CostReduction #SolutionArchitecture

FinOps for AI just became its own dedicated category in the 2026 FinOps Framework. That tells you everything you need to know about where enterprise spend is heading. Inference now drives 80 percent of AI GPU costs. Training grabs the headlines, but production inference quietly eats the budget. Without proper governance, 70 percent of AI projects stall after pilot stage. Solution Architects own this shift. We design the cost visibility layer from day zero, tag every model endpoint, and tie spend directly to business outcomes. Cost per inference replaces vague GPU hour metrics. The result? Predictable scaling and zero surprise bills. I apply this on every engagement. What unit economics do you track for your AI workloads? Drop your top metric below. #FinOpsForAI #AIInference #CostGovernance #AIFinOps #SolutionArchitecture #EnterpriseAI #ROI

We clearly need this to measure the « value » contributed by agents when implementing a feature / PR : Automatic export of « AI agent threads » and the associated results (the updated files) in the same commits The goal: identify value associated with genAI usage #FinOpsForAI

Everyone's talking about AI's power — but what about its cost? Join Stephen Old (Head of FinOps & GreenOps at Synyega) for a webcast on FinOps for AI — tackling cost, carbon, and control. Register now! attendee.gotowebinar.com/reg… #FinOps #AI #CloudCost #FinOpsForAI #321gang

Feeling AI costs spiraling out of control? My new article “FinOps for AI-A Practical Guide” is live! It explains why classic FinOps doesn’t fully apply to GenAI - and offers a step-by-step approach to regain cost control. shorturl.at/BluWv #FinOpsForAI #CostManagement #AI

Vijay Simha has built teams, platforms, and India operations for brands like Salesforce, Oracle, Siebel. Now, as VP of Engineering and Head of India Operations at @CastlightHealth, Vijay Simha blends product innovation with a strong leadership ethos, navigating both ambitious startups and established enterprises with equal finesse. Hear his insights on building scalable, resilient tech ecosystems at the Nasscom GCC Summit & Awards 2025. Register now - nasscom.in/gcc/ Apr 22–23 | HICC, Hyderabad #NasscomGCC #GCCAwards #GCC #DeepTech #AILeadership #HybridIT #TechStrategy #Flexera #FinOpsForAI @nasscom_member_

最近FinOps界隈でも話題の FinOps × AI の概要をまとめたドキュメントが公開されていました！ なかなか読み応えがありそうなので、週末にじっくり読んでみる予定👍️ FinOps for AI Overview finops.org/wg/finops-for-ai-… #FinOps #AI #クラウドコスト管理 #FinOpsforAI