How to achieve scalable AND cost-effective Inference & Serving? If you or your team are facing the pressure of finding an efficient way of hosting bigger models🤖 and serve them faster⏩ while keeping costs under control💰, this whitepaper might interest you. What it covers: 1. The Hidden Costs of Fragmented AI Systems – Understand how ad-hoc AI deployments can lead to technical debt and unnecessary expenses. 2. Key Challenges in AI Serving & Inference – Explore critical issues like cold start latency, infrastructure compatibility, and other factors to evaluate when choosing serving solutions. 3. Benchmarking Top Solutions – Compare Union’s performance against AWS SageMaker and Anyscale in areas like speed, flexibility, and efficiency. 4. Future-Proofing Your AI Platform – Learn how a unified approach to AI training and serving can reduce complexity and improve long-term efficiency. The whitepaper also highlights how Union delivers 2x faster🔥 model deployment with multi-cloud flexibility and reduced operational costs. Read the paper today!