Akamai Reduces Cloud Waste by 70%: How AI Agents and Kubernetes Reshape Cloud Optimisation for Digital Enterprises

🔄 In a period marked by escalating cloud costs and operational challenges, Akamai Technologies achieved a remarkable 70% reduction in cloud waste by integrating AI agents into a Kubernetes-driven automation platform. This article examines Akamai’s journey, assesses the current and potential impact for businesses undergoing digital transformation, and discusses the practical implications—opportunities, organisational constraints, and real-world use cases—of embedding intelligent automation in cloud management. Readers will gain a balanced perspective on cloud optimisation, infrastructure automation, and the role of NoCode tools in this evolving landscape.

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AI Agents and Kubernetes: Orchestrating Real-Time Cloud Optimisation

⚙️ The combination of Kubernetes orchestration and AI agents offers enterprises a dynamic approach to managing multicellular, hybrid, or multi-cloud infrastructure. Akamai employed an automation platform underpinned by Kubernetes, with hundreds of AI agents monitoring and optimising workloads every second. Unlike traditional, periodic manual tuning—often conducted only a few times per month—these agents deliver:

• Continuous workload analysis
• Real-time autoscaling
• Optimal resource selection across multiple cloud providers
• Automated rightsizing and usage of cost-effective spot instances

This approach leverages machine learning (ML), including reinforcement learning, to adapt to historic and real-time demand, all while ensuring business-critical performance is not sacrificed.

flowchart TD
  Start[Monitor cloud workloads]
  Analyse[AI agents analyse resource use]
  Decide[ML models recommend actions]
  Act[Automated optimisation: scale, rightsize, spot instances]
  Result[Reduced Cloud Waste & Costs]
  Start --> Analyse --> Decide --> Act --> Result

Key takeaway: Automated, AI-based infrastructure management unlocks a level of responsiveness and cost optimisation previously unattainable with manual intervention.

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Complex Requirements: Akamai’s Cloud Challenges

🔒 Akamai’s infrastructure presented a unique set of challenges:

• High-complexity, multi-cloud architectures, serving demanding industries (finance, security)
• Stringent SLAs and security requirements
• Unpredictable and volatile usage patterns (e.g., real-time security event spikes up to 1000x capacity)

Manual optimisation proved inadequate. Scaling infrastructure preemptively (“over-provisioning”) was financially prohibitive; code-level optimisations alone could not address the root of resource inefficiency. AI-driven automation targeted these deeper operational layers.

Implementation highlights:

• Automated exploitation of spot instances for critical workloads without engineering overhead
• Integration with existing workflows and no data leaving client clusters, ensuring privacy and compliance
• Cost analytics available within minutes of deployment, enabling rapid feedback loops

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Benefits and Organisational Opportunities for Digital Enterprises

✔️ Akamai’s case demonstrates several opportunities for digitally transforming organisations:

Benefit	Description
Cost Reduction	Up to 70% savings through continuous AI-driven optimisation
Increased Team Productivity	Less time managing infrastructure, more on innovation
Granular Real-Time Analytics	Immediate visibility into utilisation and cost drivers
Scalability	Capacity can flexibly track real business demand
Security & Isolation	No sensitive data leaves dedicated clusters

Accelerators: These benefits are not restricted to hyperscale firms. SMEs and mid-market organisations can access similar advantages, especially as Kubernetes automation platforms become increasingly plug-and-play and compatible with existing NoCode workflow tools.

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Use Cases and Synergies with NoCode Automation

⚡ Intelligent cloud optimisation is not limited to massive CDNs or cybersecurity. Examples include:

1. E-commerce Retailer (SME):
   Automatically rightsizes compute during traffic spikes (seasonal sales), avoiding costly over-provisioning and ensuring UX performance.
2. SaaS Provider (ETI):
   Deploys ML-driven infrastructure automation to optimise compute, integrating NoCode orchestration to trigger business workflows based on real-time system status (e.g., service degradation alerts activating customer notifications).
3. Data Analytics Startup:
   Leverages AI-autoscaling for analytics jobs (e.g., Apache Spark clusters), utilising spot instances without deep DevOps skills—freeing technical resources for feature work.

Synergies:
NoCode automation tools can complement AI infrastructure optimisation, acting as the business layer that bridges technical and operational responses. For example, after an AI agent scales down a cluster, a NoCode platform like those described in OpenAI Codex : L’agent IA qui révolutionne le No-Code et l’automatisation d’entreprise avec ChatGPT can automatically update budget forecasts or notify non-technical teams.

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Key Implementation Recommendations and Limitations

• Incremental Adoption: Platforms supporting gradual integration reduce risk and disruption; existing tools and workflows can often be retained.
• Human Oversight: Despite advances, automation should complement—not entirely replace—human judgement, especially for compliance and critical performance decisions.
• Security: Ensure all analysis and optimisation actions occur within secure, isolated infrastructure boundaries.

Limitations

• Change Management Complexity: Transitioning operational responsibility to autonomous agents requires organisational buy-in, new processes, and sometimes upskilling.
• Opaque Decision Logic: ML models may deliver optimal resource usage, but the logic can be difficult to audit or explain to stakeholders.
• Vendor Lock-In Risks: Integration with proprietary automation platforms can create switching costs or dependency.

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Key Takeaways

• AI agent orchestration within Kubernetes platforms delivers effective, continuous cloud cost optimisation and frees resources for core business innovation.
• Real-time infrastructure tuning is critical for businesses with variable, high-demand workloads and tight performance constraints.
• Enterprises of varying sizes can leverage automation and NoCode synergies for holistic operational agility.
• Adoption should be incremental, with a focus on transparency, security, and maintaining a role for human expertise.
• Organisational challenges—not technical barriers—are often the main hurdle to realising the full value of intelligent automation.