GoodData.AI

💸 Do you know where your AI tokens actually go? AI in production is billed in tokens, and agentic tasks can consume 5 to 30 times more than a standard chatbot exchange. Hidden reasoning, bloated context, and looping agents drive up costs that finance sees on the invoice but no one can explain. In this whitepaper, discover how AI observability makes token spend visible and controllable. You'll learn the six drivers that quietly inflate AI bills, five proven levers to cut costs without sacrificing output quality, and an ROI model you can adapt to your own numbers. Most teams can reduce AI spend by 30 to 50% once they can finally measure it.

🤖 One AI assistant for everyone was a safe start. Now 2027 budgets are on the table, and scaling AI analytics means answering for what it costs. In this GoodData.AI Product Journey session, see four live demos: analytics agents for different teams and customers, AI usage and LLM providers managed in one place, and AI-built reports ready to send. Join us live on Oct. 21. Can't make it? Register and we'll send the recording.

📊 Agentic tasks consume 5 to 30x more tokens than a standard chatbot exchange. And that's just one of the numbers behind rising AI costs: 🔹 Inference accounts for 80 to 90% of an AI system's lifetime cost 🔹 Nearly half of organizations name inference cost as the biggest blocker to scaling their AI products In this whitepaper, discover how AI observability makes token spend visible and controllable. You'll learn the six drivers that quietly inflate token bills, five levers to cut costs without sacrificing output quality, and an ROI model you can adapt to your own numbers. Most teams can reduce AI spend by 30 to 50% once they can finally measure it.

Your AI answers cost more than you think, and you might not even know why. Model choice, caching, routing, and orchestration all move your cost, latency, and UX. Most teams find this out the hard way, once it's already in production. Join Zdenek Musil (CTO, AI) and Jan Pansky (Sr. AI Solution Architect) for real benchmark numbers: our inference stack vs. a self-hosted open-source setup. No token-price math, just what it actually costs to run.

Who decides what your AI is allowed to know? As AI moves into self-service BI, executive reporting, and customer-facing apps, governance stops being optional. Access control, data sovereignty, and semantic layers determine whether your AI gives trustworthy answers — or confidently wrong ones. See how GoodData's CTO of AI and Sr. AI Solution Architect approach this in production, plus first-look benchmarks: managed inference vs. self-hosted.

🤖 AI analytics is moving past the prototype stage, and inference is no longer just a technical detail. In this webinar, see how inference strategy, governance, and semantic layers decide cost, latency, and how much control your team has over AI's answers. We're sharing first benchmark results: our inference stack vs. a self-hosted open-source setup. Real numbers, not token-price math.