Decentralized cloud computing is rapidly emerging as a serious alternative to centralized providers. Built on blockchain, distributed hardware, and token-driven incentives, decentralized computing tackles the biggest pain points: opaque and rising costs, vendor lock-in, single points of failure, data sovereignty conflicts, limited workload portability, shifting policies and pricing, and GPU shortages that slow down AI teams.
Within the broader DePIN sector, decentralized cloud represents a substantial share, with decentralized compute and storage networks together valued at approximately $19.3 billion, or more than half of today’s DePIN market cap. For developers, IT managers, and decision-makers, this signals the need for a clear, data-backed guide to navigate the road ahead.
This article offers a comprehensive technical analysis. It covers architectural foundations, benefits over legacy cloud models, practical deployment strategies, economic mechanisms, and upcoming trends. Real-world implementations and structured frameworks support infrastructure decisions grounded in current market demands.
The Current Cloud Computing Landscape
For over a decade, cloud computing has been shaped by three dominant providers: AWS, Microsoft Azure, and Google Cloud Platform. Together, they control the majority of market share, creating a high concentration of power in centralized systems. This centralization has benefits—economies of scale, vast service catalogs—but it also introduces systemic risks.
When a single provider experiences an outage or policy shift, the effects ripple globally. The 2021 AWS East Coast outage disrupted major streaming services, financial institutions, and even transportation systems, underscoring the fragility of concentrated infrastructure.
Other constraints are vendor lock-in, opaque pricing models, and data sovereignty conflicts. Many organizations discover that migrating workloads away from a hyperscaler is prohibitively expensive, both technically and financially. Geographic limitations also create compliance hurdles in industries subject to local data residency laws.
The economics are shifting too. Enterprise cloud spending is projected to reach $723.4 billion in 2026, driven not only by demand but also by steadily rising prices for compute, storage, and network egress. For developers, this often translates into limited control, unpredictable bills, and frustrating restrictions on deployment geographies.
What is Decentralized Cloud Computing?
Decentralized cloud computing distributes workloads across a network of independently owned and operated nodes, coordinated by blockchain protocols. Rather than relying on a single corporate provider, the infrastructure is collectively provisioned by the community.
Its defining traits include:
- Distributed ownership: Compute, storage, and bandwidth come from individuals, businesses, and organizations rather than a central entity.
- Blockchain-based coordination: Smart contracts handle resource discovery and allocation, enforce service levels, and process payments without intermediaries.
- Integration of physical and digital layers: Real-world servers and hardware are linked via digital consensus mechanisms.
- Peer-to-peer architecture: Resources are exchanged directly between participants, reducing overhead and eliminating central chokepoints.
This structure reframes cloud computing as a shared public utility rather than a proprietary service.
Why Decentralized Cloud Matters Now
Several converging trends make this model timely. The Decentralized Physical Infrastructure Network (DePIN) sector surpassed $50 billion in market cap in 2024, with analysts projecting it will grow to $3.5 trillion by 2028. This trajectory mirrors both the adoption curve of traditional cloud services and the wider recognition of blockchain as a coordination layer for real infrastructure.
Enterprise demand is already evident: surveys indicate that 89% of organizations are pursuing multi-cloud strategies to reduce risk and improve flexibility. Many are actively evaluating decentralized providers to supplement or replace portions of their workloads.
From a technology perspective, blockchain performance, consensus algorithms, and decentralized storage systems have matured to a point where enterprise-grade workloads—including high-frequency trading, AI/ML pipelines, and large-scale data analytics—can now run reliably in a decentralized environment.
The economics are equally compelling. Token-based incentive models create self-sustaining resource markets where supply and demand determine price. This can reduce costs for consumers while ensuring fair compensation for providers, all without the pricing opacity common in centralized billing models.
Market Landscape and Competitive Analysis
Decentralized infrastructure is transitioning from niche experimentation to global infrastructure reality. As of Q1 2025, the ecosystem now includes over 1,170 active DePIN projects, up dramatically from 650 just two years ago.
These projects power a network of more than 10.3 million devices deployed across 199 countries, showing broad real-world reach. The sector supports 350+ infrastructure tokens and is valued at ~$35–50 billion in market capitalization, while revenues are projected to surpass $150 million in 2026.
Traditional hyperscalers still dominate in scale and service diversity, but their control comes with trade-offs—vendor lock-in, rising costs, and opaque billing. These weaknesses are opening space for decentralized alternatives that compete on flexibility, transparency, and workload mobility.
Market Drivers
Several trends are shaping adoption:
- Rising cloud costs: Network egress fees and incremental price hikes by centralized cloud providers are pushing enterprises to explore alternatives.
- Regulatory compliance: Data sovereignty rules, such as GDPR and sector-specific localization laws, are prompting selective infrastructure placement.
- Performance needs: AI/ML training, IoT, and latency-sensitive applications require capacity closer to end users.
- Sustainability considerations: Leveraging underutilized hardware across distributed operators can be more energy-efficient than centralized megacenters.
Venture capital is responding to these trends, with significant funding flowing into DePIN projects. Investor focus is shifting from speculative tokenomics toward platforms with measurable throughput, proven uptime, and developer ecosystem growth.
Competitive Landscape
The decentralized cloud market is diverse, with platforms differentiating themselves by workload focus and deployment model:
- Fluence Network: Offers virtual servers (with API support) and GPU compute through its decentralized cloud compute platform, supporting GPU containers, GPU VMs, and bare metal deployments at up to 85% lower cost compared to centralized cloud, powered by a distributed network of Tier-3 and Tier-4 compute providers with GDPR, ISO 27001, and SOC2 compliance.
- Flux: Flux (RunOnFlux) is a decentralized cloud that uses a global network of user-operated FluxNodes running FluxOS to deploy, run, and scale Dockerized apps via a web/API marketplace with monitoring and resilient, censorship-resistant infrastructure.
- Aethir: A decentralized GPU cloud (GPU-as-a-service) delivering enterprise-grade, globally distributed GPUs on-demand for AI and gaming, with an emphasis on cost efficiency and flexibility.
- Filecoin: A decentralized storage marketplace where independent providers store and serve data with on-chain cryptographic proofs, paid for via FIL.
- Akash Network: A decentralized compute cloud for Kubernetes-orchestrated containers (CPU/GPU) that matches tenants with providers via on-chain bids and leases, with network security and settlement powered by the AKT token (and stablecoin payments supported for price predictability).
- Golem Network: Targets high-performance computing such as rendering, simulations, and complex batch jobs.
Hyperscalers, meanwhile, are defending their position by extending infrastructure to the edge and supporting hybrid deployments: AWS with Outposts and Local Zones, Microsoft Azure with Arc, and Google Cloud with Anthos. While these initiatives offer more flexibility than the hyperscalers’ core offerings, they still lack the open pricing models and workload portability that decentralized platforms provide.
Developer and Community Insights
Developer engagement is growing steadily. Open-source repositories for decentralized platforms are seeing more frequent contributions, while community channels—Discord, GitHub discussions, and forums—are becoming hubs for sharing deployment best practices. Early case studies, particularly in Web3-native applications like decentralized exchanges, NFT platforms, and global content delivery networks, show that decentralized cloud can meet performance targets without central dependencies.
These early successes are crucial. They signal to both developers and enterprises that decentralized compute is here and already powering real services in production today.
Economic Models and Token Economics in Decentralized Cloud
Decentralized cloud changes how infrastructure is run and how value is settled: crypto-economic protocols enable open markets where providers and users set prices for compute, storage, and bandwidth, and on-chain rules handle payment/penalties (escrow, proofs, burns). Understanding these money flows helps you decide what to deploy, where, and how to measure ROI.
How value flows
Providers contribute capacity (CPU, RAM, disk, bandwidth) and receive on-chain payments for measured usage. Consumers fund a balance and spend it as workloads run. Many networks also require staking from providers to align incentives; if uptime or performance falls below thresholds, a portion of stake can be slashed. The result is a market that rewards reliable supply and penalizes unreliable operators without tickets or manual enforcement.
Pricing mechanisms (and when to use each)
- Fixed-rate offers: Capacity is listed at a posted rate (often stablecoin-denominated) per hour or per day/epoch. Good for production APIs, compliance-sensitive workloads, and predictable budgets.
- Dynamic/auction or spot pricing: Rates float with supply–demand. Ideal for bursty jobs (training, rendering, ETL) where timing is flexible and you can chase cheaper windows.
- Token-based pricing: Some platforms charge for workload in crypto tokens, which might be volatile and therefore not very convenient for costs management.
A growing number of networks blend the two, quoting in stable assets to protect buyers from token volatility while still letting providers express price based on hardware class, region, and datacenter tier.
Predictability vs. volatility
Teams coming from AWS or GCP care about billing clarity more than token theory. Two features make decentralized billing legible:
- Epoch accounting: Usage settles in fixed intervals (e.g., daily cutover), which you can align with job start/stop times to avoid paying for idle tails.
- On-chain meters: Every debit and credit is traceable. Finance and compliance teams can audit spend directly from the ledger, not a CSV export that changes format every quarter.
ROI for both sides
For providers, ROI is a function of utilization (keep hardware busy), energy cost, and the effective price per unit capacity. Staking increases capital at risk but also boosts earnings eligibility and reputation.
For builders/enterprises, the win shows up in TCO: lower effective compute price, reduced egress surprises, and less vendor lock-in. You also gain an optional upside—reselling unused capacity back to the marketplace when demand spikes.
Practical ROI levers
- Workload fit: Push stateless, latency-tolerant, or batch jobs to dynamic markets; keep strict-SLA components on fixed-rate offers.
- Placement strategy: Choose regions and tiers that meet latency/compliance at the lowest posted rate; spread across providers to reduce single-provider risk.
- Scheduling: Align long jobs with epoch boundaries; scale down before cutover to avoid paying for a full next epoch.
Funding and capital flows
Capital enters through VC rounds, token treasuries/DAOs, and ecosystem grants that subsidize early supply or new services. For founders, that means deployment decisions can double as go-to-market: integrate with a protocol, tap its grants, and acquire users where the incentives already exist.
Risks and how to manage them
Two risks matter most to buyers: token volatility and supply reliability. Fix the first by preferring stablecoin-quoted offers or contracts that hedge price. Address the second with multi-provider placements, health checks, and automated redeploys. For providers, the mirror risks are slash events and under-utilization; keep SLAs green and join demand channels (marketplaces, broker APIs) to keep rigs busy.
Core Components: Architecture and Layers
The decentralized cloud is composed of three essential layers:
- Physical Infrastructure Layer: Community-operated nodes contribute compute, storage, and bandwidth. These nodes can include idle servers, enterprise-owned systems, or even end-user devices, all securely connected.
- Blockchain Coordination Layer: Smart contracts and consensus algorithms regulate resource allocation, enforce service levels, and resolve disputes. Systems like Fluence and Akash Network implement either established or proprietary blockchain protocols here.
- Token Economics Layer: Incentives are structured through tokens. Resource providers earn tokens by offering capacity, and users spend them for services. Pricing may be dynamic, auction-based, or tiered through subscription.
Physical Infrastructure Layer
At its foundation, a decentralized cloud is a network of distributed hardware nodes. These nodes may range from enterprise-grade data center servers to smaller units like routers, wireless hotspots, and high-performance consumer devices. In some networks, IoT sensors and specialized edge devices also contribute processing or storage capacity.
Ownership is community-driven. Operators can be individuals, cooperatives, or organizations across multiple regions. This diversification mitigates regional outage risk and supports local compliance.
Key resources contributed include:
- Computing power (CPU, GPU, and memory resources)
- Storage capacity (object, block, and archival)
- Network bandwidth (uplink and downlink throughput). To maintain quality, most networks implement reputation systems that score nodes based on uptime, response latency, and historical reliability. Poor performance or malicious behavior typically results in reduced earnings or removal from the network.
Blockchain Coordination Layer
The control plane is powered by blockchain. Smart contracts handle service agreements, automate payments, and govern access permissions.
Consensus mechanisms vary by platform:
- Proof-of-Work (PoW): Security through computational effort, used in certain legacy systems.
- Proof-of-Stake (PoS): Stake-based validation, lowering energy requirements.
- Proof-of-Usage and similar variants:
- Prioritizing active resource contribution rather than raw staking.
- This layer records all transactions on a public ledger, ensuring transparency in usage metrics and payments. Governance is typically community-driven, where protocol upgrades and rule changes are voted on by token holders.
Token Economics Layer
Economic incentives align the interests of providers, consumers, and validators.
- Incentive mechanisms: Resource providers earn tokens in proportion to the quantity and quality of capacity they contribute.
- Payment systems: Consumers pay in tokens for compute, storage, or bandwidth usage, often with real-time microtransactions.
- Staking models: Some networks require staking tokens as a security deposit to operate nodes, deterring malicious behavior.
- Value distribution: Algorithms ensure fair earnings distribution while adjusting for variables like uptime and performance.
This modular stack emphasizes flexibility, transparency, and distributed governance.
How Decentralized Cloud Differs from Traditional Cloud
Sovereignty is a particularly critical differentiator. In decentralized networks, users can dictate exactly which jurisdictions their data resides in, helping satisfy regional laws such as GDPR or data localization requirements.
Here is a comparison of decentralized cloud vs. traditional cloud:
| Traditional Cloud | Decentralized Cloud |
| Centralized control by single providers (AWS, Azure, GCP) | Distributed governance with no single authority (Fluence, Akash, Filecoin) |
| Large, upfront infrastructure investments | Capital-free scaling through community-provided resources |
| Vendor lock-in via proprietary APIs and pricing | Open standards, interoperability, and data portability |
| Susceptible to large-scale outages | Geographic redundancy reduces single points of failure |
| Opaque pricing and limited billing transparency | Public blockchain audit trails for resource usage and payments |
| Restricted data sovereignty | Full control over data location and access |
Benefits Over Centralized Cloud Providers
Decentralized infrastructure offers tangible advantages:
- Resilience and Redundancy: Distributed systems can isolate and contain failures. A regional outage doesn’t compromise the entire network. For example, during the 2021 AWS incident, decentralized platforms like Arweave and Filecoin maintained consistent service levels for critical applications.
- Transparency and Cost Efficiency: Blockchain records enable accurate usage auditing and fair billing. For compute-heavy workloads, decentralized solutions offer measurable savings. A 2023 benchmark study showed a 25–40% lower total cost of ownership (TCO) for AI/ML training jobs compared to traditional hyperscalers. Decentralized virtual server savings are even higher, Fluence offers up to 85% cheaper alternative to AWS.
- Data Sovereignty and Access Control: Businesses can determine exactly where and how their data is stored. This reinforces compliance strategies, especially when operating across different regulatory regions.
Integration with Existing Multi-Cloud Strategies
Decentralized cloud doesn’t replace centralized providers. Instead, it complements them. Enterprises can also integrate both models in hybrid multi-cloud architectures that balance performance, cost, and compliance.
Integration methods include:
- Hybrid workloads: Deploying latency-sensitive applications on decentralized nodes closer to the end user, while keeping certain regulated workloads in private or centralized clouds.
- API compatibility: Many decentralized providers offer REST or gRPC interfaces that match centralized service APIs, reducing integration friction.
- Workload distribution: Resource orchestration tools can route traffic dynamically to whichever environment—centralized or decentralized—offers the best cost-performance ratio at a given time.
- Cost optimization: By comparing token-based spot pricing with centralized provider rates in real time, organizations can shift workloads to the most cost-effective option without compromising SLAs.
Fluence Virtual Servers: Leading Decentralized Compute Platform
Fluence offers a decentralized cloud computing platform purpose-built for developers and organizations that require greater control, cost efficiency, and flexibility than centralized cloud providers can offer. At its core, Fluence Virtual Servers enable users to deploy and manage compute workloads across a global network of independent infrastructure providers using a single protocol and API.
In addition to CPU-based virtual servers, Fluence now extends this platform with GPU compute capabilities designed for AI workloads. Developers can deploy GPU containers, virtual machines, or bare metal via its decentralized GPU marketplace depending on their performance, isolation, and control requirements, while maintaining the same decentralized execution model and predictable pricing structure.
Fluence’s architecture is designed to abstract away the complexity of decentralized infrastructure while preserving its economic and operational advantages. Virtual servers run in enterprise-grade data centers, giving users the performance characteristics of traditional cloud VMs without vendor lock-in or opaque pricing models.
Core Capabilities
- Global marketplace of providers: Select infrastructure based on geography, performance, and cost, enabling resilience and redundancy by design.
- Virtual servers on decentralized infrastructure: Deploy Linux-based VMs across multiple providers and regions without relying on a single cloud vendor.
- GPU compute options: Run AI workloads using GPU containers for fast iteration, GPU virtual machines for stronger isolation, or bare metal GPUs for performance-critical training and inference.
- Transparent and predictable pricing: Clear cost structures without egress fees or surprise charges, aligned with long-running and compute-intensive workloads.
- Programmatic control: Manage deployments, scaling, and lifecycle operations entirely through APIs, making Fluence suitable for automation-heavy and production environments.
By combining virtual servers and GPU compute within the same decentralized platform, Fluence enables teams to run everything from general-purpose backend services to advanced AI workloads without stitching together multiple clouds or sacrificing cost predictability.
Why It Stands Out for Builders
- No vendor lock-in: Migrate workloads freely between nodes or providers.
- Global reach: Deploy close to users to minimize latency.
- Predictable pricing: Usage-based billing with no hidden bandwidth or egress fees.
- Open integration: Works alongside centralized clouds in hybrid or multi-cloud strategies.
Whether you’re running a blockchain validator, scaling a DeFi backend, serving latency-sensitive APIs, or running AI agents, Fluence give you the building blocks to do so on your terms.
Technical Deep Dive: Building on Decentralized Cloud
Fluence operates a decentralized compute marketplace where virtual servers are rented through the Fluence Console or API from independent infrastructure providers. The marketplace is governed by smart contracts that facilitate VM renting, configuration, and management between customers and providers.
Users can choose where workloads run by selecting data center locations and server types, with visibility into hardware specifications, pricing, geography, and available certifications. Billing and lifecycle management are handled through the Console, while network-level verification is supported through cryptographic mechanisms such as Proof of Capacity, which providers submit to the blockchain to demonstrate readiness.
Choosing How to Work with Fluence
Fluence offers two main interfaces. The Console is a web dashboard for visually managing deployments. You can browse offers, configure virtual machines, choose an operating system image, and launch them directly. The API offers the same capabilities but is aimed at automation—perfect for integrating decentralized compute into CI/CD pipelines or scaling workloads programmatically.
Key Marketplace Concepts
The marketplace uses a few core terms:
- Offer: A provider’s listing showing specs, location, and cost.
- Compute Peer: The actual server your VM runs on.
- Epoch: A 24-hour billing period priced in USDC, with charges resetting daily.
- Datacenter Tier & Certifications: Quality markers like Tier 3/Tier 4, ISO 27001, SOC 2, PCI DSS.
These details let you match workloads to the right performance and compliance requirements.
How Deployment Works
Deploying on Fluence follows a clear rhythm: search the marketplace for an offer that matches your needs, configure the VM, and launch it. You then connect (typically over SSH) to start running workloads. Because billing is daily, timing your launches around the epoch rollover can save on costs. When you’re done, terminate the VM to stop charges.
Best Practices for Builders
Fluence works well on its own or as part of a hybrid cloud setup. You might run your workloads entirely on Fluence, or just use it for jurisdiction-specific placement and transparent pricing while keeping other workloads on AWS, Azure, or GCP.
Whatever your approach:
- Keep an eye on your prepaid balance—low funds can pause or terminate a VM.
- Design for resilience with backups or redeployment plans in case a provider goes offline.
By planning for flexibility and transparency, you make the most of what decentralized infrastructure offers, while avoiding the vendor lock-in of traditional clouds.
Decentralized Cloud Implementation Strategies: From Evaluation to Rollout
Organizations considering decentralized infrastructure can follow a phased approach.
1. Evaluate Current Infrastructure
Start by cataloging current workloads and identifying pressure points, such as rising costs, compliance issues, or latency problems. Not all use cases are well suited; decentralized environments are especially beneficial for:
- AI inference and model training
- Distributed content hosting and storage
- Resilient disaster recovery setups
- Edge computing environments
2. Compare and Select Platforms
Selection criteria include:
- Compatibility: Confirm support for specific APIs, workload types, and integrability with legacy environments.
- Token and Pricing Models: Understand how cost, volatility, and potential rewards balance within usage flows.
- Community and Tooling: Evaluate maturity, developer support, and production-level deployments of the ecosystem.
For context, Fluence is the only decentralized compute platform offering virtual servers with full flexibility, while Akash Network is optimized for containerized deployments with Kubernetes compatibility.
3. Apply Engineering Best Practices
- Use Modular Design: Microservice architectures simplify deployment across distributed nodes and help isolate issues.
- Build for Fault Tolerance: Employ timeouts, retries, circuit breakers, and distributed tracing.
- Ensure Security Practices: Encrypt data end-to-end, integrate identity protocols, and apply regular security scans.
Looking Ahead: Technological Drivers and Roadmaps
Decentralized infrastructure is evolving in step with mainstream enterprise needs (AI, storage, streaming, IoT, and compliance) making it a credible complement to existing multi-cloud strategies.
AI Workflows
Training and inference workloads increasingly rely on access to distributed GPU pools without centralized chokepoints, making a decentralized cloud for AI a practical option. Networks like Akash provide Kubernetes-compatible GPU markets, reporting 428% year-over-year growth in usage with utilization above 80% heading into 2026. For federated learning and privacy-first analytics, decentralized platforms such as iExec focus on confidential computing, enabling model sharing without exposing sensitive data. Other networks like Fluence are building their GPU layer to tap into the growing AI market.
Storage and Archiving
Verifiable, distributed storage has scaled into enterprise use cases. Filecoin secures over 23 EiB of capacity, with more than 1 EiB of paid storage projected for 2026. Sia has recorded five straight quarters of growth, with utilization reaching 34% in Q1 2025. These systems are increasingly used for backups, compliance archives, and as alternatives to hyperscaler object storage, reducing egress risk and increasing auditability.
Streaming and Media
Decentralized compute is already powering media at scale. Livepeer reports that 78–92% of network usage comes from livestream transcoding, while expanding into AI-driven editing and real-time video effects. This creates flexible, on-demand pipelines for content providers without relying on expensive centralized transcode services.
IoT, Mapping, and Telemetry
Infrastructure for connected devices and geospatial data is proving to be one of the strongest adoption verticals. Helium Mobile operates nearly 100,000 hotspots, extending decentralized wireless coverage. GEODNET runs more than 19,000 RTK stations across 145 countries, powering drone, robotics, and GIS applications. Hivemapper has mapped over 500M km of roads, covering 34% of the global network, creating an open alternative to closed mapping APIs.
General Compute and Hybrid Cloud
Platforms like Fluence Network are expanding decentralized compute beyond niche applications. With its Virtual Servers marketplace, developers can deploy VMs and workloads on enterprise-grade infrastructure worldwide, supported by transparent billing and no egress fees. Fluence has launched its GPU platform, enabling AI teams to deploy workloads using GPU containers, GPU virtual machines, or bare metal GPUs within a decentralized compute marketplace. This hybrid-compatible model makes Fluence especially suitable for startups, Web3 projects, node operators, and AI builders, bridging decentralized resilience with the performance standards expected in enterprise environments. Fluence reported surpassing $1M ARR in 2025, underscoring strong demand for its Cloudless solutions.
Adoption Timing
Momentum is building quickly. Gartner projects that 90% of organizations will operate hybrid cloud by 2027, with decentralized capacity playing a role in extending resilience, reducing costs, and improving workload flexibility. Early adopters are already realizing benefits in compliance, sovereignty, and cost-efficiency, while broader enterprise uptake is expected to accelerate over the next cycle.
Strategic Pathways
Enterprises can pursue decentralization incrementally or at scale:
- Incremental – test workloads like backups, overflow transcoding, or telemetry ingestion.
- Hybrid – integrate decentralized compute into multi-cloud environments for cost and compliance optimization.
- Full decentralization – greenfield applications requiring sovereignty, open pricing, and resilience beyond single providers.
The trajectory is clear: decentralized infrastructure is moving from specialized experiments into mainstream adoption, with platforms like Fluence, Akash, Filecoin, Livepeer, Helium, GEODNET, and Hivemapper each addressing critical parts of the enterprise stack.
Final Thoughts
Decentralized cloud has matured into a viable option for production workloads across Web3, AI, and enterprise use cases. By replacing centralized control with open, market-driven resource networks, it offers transparency, resilience, and jurisdictional flexibility.
For technology leaders, it’s a strategic third choice alongside on-prem and hyperscalers: one that can lower costs, reduce lock-in, and create new revenue models. Early adopters are already gaining an edge in a market set to expand rapidly.
The key is aligning the right workloads with the right platform, backed by strong governance, security, and economic planning. Done well, the shift delivers performance, compliance, and flexibility in ways centralized clouds can’t match.
Learn how Fluence can help you deploy and scale on a truly decentralized global compute network
