Simplifying Multi-Cloud Chaos: Why AI Innovators Need emma

Amid deepening political rifts, AI-related environmental concerns, tariff wars, and supply chain uncertainties, the AI race is very much still on. For big tech, it’s a race to release bigger, better models. For businesses, it’s about harnessing AI models faster to achieve operational efficiencies and real business value.

In 2025, businesses will collectively spend $644B on their GenAI initiatives, and cloud will be a key enabler, acting as a powerful democratizing force in AI. Despite cloud's widespread availability, political uncertainties, tightening regulations, GPU shortages, and climate-related disruptions are pushing organizations to embrace the flexibility, scalability, and freedom of a multi-cloud. Unfortunately, multi-cloud does come with operational complexity and fragmented visibility.

The good news is that these challenges are rather easy to overcome. emma is deeply invested in fully leveraging AI advancements, and is continuously maturing and expanding its AI capabilities to help businesses accelerate their AI innovation by harnessing multi-cloud and expanding choices for AI projects.

The Critical Role of Multi-Cloud in AI Success

Multi-cloud setups for enterprise AI and GenAI initiatives have become more than a matter of choice and preference – they’ve become a non-negotiable essential.

• AI and Data Regulations: Regulations like GDPR have already forced companies to carefully plan where and how their data is stored and processed. Others, like the EU AI Act and DORA (Digital Operational Resilience Act), require businesses to ensure operational resilience and mitigate vendor lock-in risks by having an exit strategy and failover option in the event of a cyberattack, grid failure, or catastrophe. Distributing workloads across multiple compliant cloud providers is the only way to reduce these third-party service provider risks.

• ‍Geopolitical Uncertainties: As global tensions rise, geopolitics is increasingly dictating tech decisions. The de facto exit of Kaspersky from the U.S. market is a clear example of how tools and platforms critical to you today could face abrupt discontinuation. Similar concerns have repeatedly surfaced for AI and cloud players like DeepSeek, Alibaba, and Huawei because of their Chinese roots. Organizations depending on foreign vendors must weigh in on potential bans, access limitations, and price hikes due to tariffs. In these circumstances, the flexibility to pivot AI strategies and reroute workloads whenever needed is absolutely crucial for sustained AI innovation.

• Customization and Fine-tuning: While proprietary AI platforms like Claude and ChatGPT are enterprise-ready and quick and easy to deploy, they come with limited customizations and potential lock-ins. In addition, fine-tuning them requires sending proprietary data to vendors’ servers. As a result, many feel compelled to utilize a mix of frontier models hosted on hyperscale infrastructure and fine-tuned, open-source models hosted on local and sovereign cloud options. Multi-cloud provides this ability to pick best-of-breed models and mission-fit platforms for both broad, out-of-the-box capabilities and domain-specific use cases.
• GPU Options and Availability: With ongoing supply chain shortages and circumstantial shocks like climate-related catastrophes and cyberattacks, it’s too risky to rely on a single GPU provider. In addition, having multiple options allows you to explore different GPUs and TPUs and find the most suitable and cost-efficient ones.

The Reality of Multi-Cloud Complexity in AI

Despite the critical need for multi-cloud deployments and flexibility for AI innovation, the operational challenges of multi-cloud management are still a deterrent for organizations.

• Interoperability Issues: Different providers have variations in APIs, standards, and data formats, which can make multi-cloud interoperability and integration a challenge, especially if different components of your distributed applications need to communicate and exchange data.
• Fragmented Infrastructure Visibility: Fragmented visibility across various cloud providers means that teams can’t have a holistic view of resource consumption, performance, and costs across various cloud platforms. This leads to operational inefficiencies, resource wastage and uncontrolled costs.
• Inefficient GPU Utilization: Coordinating GPUs across multiple clouds requires centralized scheduling and orchestration capabilities. You need to ensure reserved GPUs are utilized optimally and workloads run cost-effectively and efficiently.

emma’s Multi-Cloud Advantage for Breaking Vendor Lock-In

Being wired into a single cloud environment often results in vendor lock-in, which has always been one of the biggest cloud fears. But an even greater drawback is the reverse: being locked out of accessing cutting-edge AI breakthroughs from one provider simply because you're too entrenched in another provider’s tools and ecosystem. To stay ahead in the AI innovation race, you need agility to explore newer, more robust models and applications as they emerge.

The emma platform abstracts the complexities that generally keep organizations from embracing what multi-cloud offers. emma enables:

• AI-powered Workload Orchestration: emma intelligently selects optimal environments for your AI workloads based on cost, performance, and availability.
• Seamless Interoperability: It offers advanced integrations and APIs across multiple cloud providers to ensure your workloads can seamlessly transition between providers without manual reconfigurations or disruptions.
• Managed Kubernetes: A single dashboard and consistent mechanisms to manage Kubernetes clusters across multiple providers.

Imagine you’re running training jobs on AWS GPU instances, but can’t always accurately predict and reserve GPUs in advance. As your demand unexpectedly grows, emma can automatically find and allow you to redirect additional training tasks to available instances elsewhere.

Streamlined Visibility for Strategic Decision-Making

Unified visibility across multi-cloud environments allows you to monitor metrics like training speed and GPU utilization across cloud providers to compare cost-performance ratios and identify and resolve correlated bottlenecks.

The emma platform provides single-pane-of-glass monitoring and observability across major and small-scale cloud providers, which allows you to:

• Get real-time cost, performance, and availability insights across providers to choose the optimal environment and precise resources for your workloads.
• Get consistent telemetry – cost, consumption, and performance – even as the underlying infrastructure shifts.
• Correlate issues like cost spikes or performance bottlenecks in different environments for faster root-cause analyses and troubleshooting.

Consider an organization handling training jobs in AWS and real-time inference in Azure. If inference latency in Azure occurs due to a possibly misconfigured training job looping in AWS, emma can help AIOps and engineers detect and correlate looping-related compute spikes in AWS and the following latency issues in Azure tasks. Such correlations may take weeks with siloed observability systems.  

Intelligent Automation for Simplifying Complex Operations

Orchestrating AI workloads is particularly complicated in multi-cloud environments. Organizations need to handle unpredictable GPU availability, deploy and scale workloads across regions and providers, and manage batch operations simultaneously.

emma can take on much of this operational overhead through AI-enabled automation. It lets you:

• Dynamically scale workloads up or down and optimize based on real-time demand, availability, and resource costs across clouds
• Deploy automatic failover and self-healing containers to ensure operational continuity without having to restart the task if a GPU node fails or becomes unavailable.
• Schedule training jobs based on idle windows, low-demand periods, and GPU spot instance availability.

These automated capabilities free up teams to focus on AI innovation. The operational agility and efficiency gains from emma result in fast time-to-market and measurable gains in launching and scaling AI-driven solutions.

Turning Multi-Cloud Complexity into an Innovation Driver

Both multi-cloud infrastructure and simplified multi-cloud management are key to accelerating AI innovation. emma’s AI-powered multi-cloud management delivers strategic benefits through reduced vendor dependence, operational resilience, best-of-breed flexibility, intelligent cost optimization, and streamlined infrastructure operations. emma enables AI and engineering teams to make faster, smarter decisions when it comes to planning, deploying, and scaling AI initiatives.

Explore the emma platform today with a 14-day free trial to maximize your AI potential and build confidently on the cloud platforms and services of your choice.