There are many new solutions to counter threats that undermine the efficiency of multi-cloud setups, and a compelling, emerging option is slowly but steadily gaining ground: decentralized compute for multi cloud.
Multi-cloud strategies are becoming increasingly common among enterprises, with up to 92% of large organizations leveraging multiple cloud providers.
While it enhances resilience and flexibility, the reality of multi-cloud is multifaceted and fragmented: spiralling costs, operational complexity, and the age-old threat of vendor lock-in.
This article examines how decentralized cloud computing solutions like Fluence can play a significant role in multi-cloud stacks, delivering up to 85% cost savings.
The Challenges and Complexity of Multi Cloud Infrastructures
The promise is simple. Leverage the strengths of separate cloud providers to optimize costs, avoid single points of failure, and access best-in-class services.
But the reality is not as rosy as it seems. Teams often face hidden costs, operational overload, and security blind spots.
Separate cloud providers mean managing disparate dashboards, APIs, and billing models. It also means a larger attack surface and more complex identity and access management (IAM) across different platforms. Most hyperscalers charge high egress fees, with data egress accounting for up to 45% of project expenses and cloud waste reaching up to 35% of total cloud spend.
More clouds, even if well planned, could mean greater complexity. This is where decentralized compute for multi cloud environments can play a significant role.
Decentralized Compute for Multi Cloud
What is decentralized compute? To put it simply, it’s a global network of independently owned, enterprise-grade data centers that provide compute resources without any centralized control or intermediaries. This novel approach is part of a growing sub-sector called Decentralized Physical Infrastructure Network (DePIN), and is fast becoming a key contender to hyperscalers.
Unbound by the limitations of big cloud, DePIN platforms can significantly reduce Total Cost of Ownership (TCO), enable true vendor-agnosticism, and prevent single points of failure.
By removing the middle layer of centralized control, decentralized compute platforms for multi cloud platforms like Fluence can deliver enterprise-grade clouds at a fraction of the cost of hyperscalers.
Fluence: A Cloudless Solution for Any Multi-Cloud Stack
While many DePIN solutions pool unused compute from consumer devices, Fluence takes a much more robust approach. Teams can access enterprise-grade, certified hardware through a globally distributed network of compute providers. That means not depending on a single hyperscaler’s pricing or capacity.
Most importantly, Fluence delivers three key things that matter the most to DevOps leads:
- Transparent, predictable economics
Pricing is flat (e.g., $10.78 per month for 2 vCPUs, 4 GB RAM, 25 GB NVMe storage), with no hidden or surprise fees, and includes unlimited bandwidth. With Fluence, teams can manage their FinOps budget more efficiently and without pricing shocks.
Note: Fluence reduces costs by up to 85% compared with hyperscalers such as AWS.
- Enterprise-grade performance
Fluence only accepts Tier-3 and Tier-4 compute providers that are SOC2, ISO 27001, and GDPR-compliant. Despite the distributed nature of the network of providers, teams can scale globally without sacrificing performance or throughput.
- No frills integration
Spinning up and down virtual servers on Fluence can be done in just a few clicks on Fluence Console. Custom OS images are supported, allowing teams to plug it directly into existing CI/CD pipelines and orchestration tools. For those looking to automate and scale, there’s an option to use the Fluence API.
How to Integrate Decentralized Compute
Despite the “DePIN” label, decentralized cloud compute solutions, such as Fluence, work like any other hyperscalers. Its platforms align with the same orchestration and compliance models most teams already use. There won’t be any need to overhaul your cloud stack. Instead, you layer it in.
Here is a quick birds-eye view on how add Fluence to your cloud stack:
Phase 1: Experiment
Select a low-risk workload (e.g., development sandbox). Deploy it on Fluence Virtual Servers to validate pricing, performance, and reliability.
Phase 2: Automate
Integrate Fluence API into your existing pipelines. Provision and scale resources automatically, the same way you handle AWS, GCP, or Azure instances.
Phase 3: Standardize
Run your applications in containers, orchestrate them through Kubernetes, and manage everything with Terraform. This step ensures total portability between separate clouds and providers.
Phase 4: Scale
Only once the process feels familiar do you shift to heavier workloads and critical services. The goal isn’t to replace your multi-cloud setup but to make it more cost-aware and fault-tolerant.
By treating decentralized compute as an additive layer, teams can expand capacity, test new regions, or rebalance workloads without friction. Fluence fits neatly into that model and optimizes your multi-cloud infrastructure.
Final Thoughts
There has been considerable debate about the real-world feasibility of DePIN solutions. But platforms like Fluence have proven otherwise.
It’s true that hyperscalers offer scale, reach, and managed services. But in many multi-cloud setups, there is still significant friction in reducing infrastructure complexity and keeping TCO predictable.
By blending the strengths of centralized cloud with decentralized compute for multi cloud, such as Fluence Virtual Servers, teams can better manage their multi-cloud stack while keeping their infrastructure responsive to real business conditions.
Photo by fabio; Unsplash
