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Fluence

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fluence.ai

Fluence is a decentralized compute platform delivering enterprise-grade AI compute via a global network of independent providers.

Overview

Fluence is a decentralized compute platform delivering enterprise-grade compute resources through a distributed network of independent providers with no dependence on cloud hyperscalers. Its mission is to create a more competitive market for AI compute, enabling teams to choose infrastructure on clear terms while providers compete to serve them. Fluence is governed by Fluence DAO and a community-driven governance model, with Cloudless Labs contributing to development, and operates a global marketplace for CPU and GPU capacity with APIs to provision and manage compute across providers and regions.

Mission statement

Our mission is to create a more competitive market for AI compute, enabling teams to choose infrastructure on clear terms while providers compete to serve them.

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What we offer

Fluence Virtual Servers

Fluence Virtual Servers provide affordable, scalable compute with no vendor lock-in or hidden fees.

fluence.ai/virtual-servers

Fluence GPU

Fluence GPU provides flexible, cost-effective AI compute resources with no vendor lock-in.

fluence.ai/gpu

Fluence CPU Cloud

Fluence CPU Cloud offers cost-effective, flexible compute resources for AI and general workloads without vendor lock-in.

fluence.ai/cpu-cloud

Fluence Blockchain Nodes

Effortlessly deploy and scale blockchain nodes with lower costs and full control.

fluence.ai/deploy-blockchain-nodes

AI Agents

Streamline AI agent deployment with unified infrastructure and cost-effective pricing.

fluence.ai/ai-agents

AI Inference

Optimize AI workloads with cost-effective, flexible GPU infrastructure and zero egress fees.

fluence.ai/solutions/ai-inference

AI Training

Leverage cutting-edge GPUs for efficient AI training without vendor lock-in.

fluence.ai/solutions/ai-training

Fine-tuning

Effortlessly fine-tune AI models using flexible GPU resources at competitive rates.

fluence.ai/solutions/fine-tuning

Generative AI

Enable versatile generative AI applications with cost-effective GPU computing.

fluence.ai/solutions/generative-ai

Market segments

Market size by segment

Growth potential (CAGR)

ML training infrastructure and orchestration

12.7 Billion USD24.5% CAGR

Capabilities to schedule, run, and scale GPU-accelerated training jobs, manage clusters and checkpoints, and provide fault tolerance and pre-validated high-performance compute for model development.

Managed model inference and serving

23.4 Billion USD26.8% CAGR

Managed hosting and serving of models with autoscaling endpoints, OpenAI-compatible APIs, batch inference pricing, and configurable serving modes to balance latency and throughput for production deployments.

Foundation model training and fine-tuning

3.15 Billion USD23.4% CAGR

Capabilities for pretraining, fine-tuning, curating, and managing foundation models and large-scale model workflows, including GPU-accelerated pipelines for video and multimodal data.

Model-Agnostic Runtime and Deployment

3.5 Billion USD44.2% CAGR

Model-agnostic access and orchestration across 200+ models and compatibility with multiple agent frameworks for deploying and running AI applications.

Blockchain node hosting and Web3 infrastructure

4.4 Billion USD38.7% CAGR

Decentralized, cost-efficient multichain node deployment and management on a distributed provider network with automated provisioning and reduced vendor lock-in.

More information about our offering

Fluence Virtual Servers

Fluence Virtual Servers deliver cloudless compute from a distributed network of independent providers, enabling enterprise workloads without hyperscaler lock-in. Host CPU workloads on affordable virtual servers and route inference to high-performance GPUs, all with zero data egress fees. Source CPU and GPU capacity from multiple providers in one console, with API driven provisioning and a global provider network.

  • Automate Compute Management
    Simplify your infrastructure management with API-driven capabilities, enabling seamless operations across multiple providers.
  • Ensure Resilience and Flexibility
    Achieve higher uptime and flexibility by utilizing a diverse network of independent providers.
  • Eliminate Hidden Costs
    Enjoy predictable pricing and avoid unexpected charges associated with data transfers.
  • Expand Global Reach
    Leverage a global network to enhance your application's availability and performance across different regions.
  • Centralize Resource Management
    Manage all your compute resources from a single console, making it easier to monitor and optimize your usage.
  • Maintain Data Independence
    Ensure your data remains intact and easily accessible across virtual machine instances.

Fluence GPU

Launch on demand GPUs for AI workloads across Fluence's cloudless platform. Access GPU capacity across providers without hyperscaler lock-in, with deployment options including containers, virtual machines, and bare metal. Enjoy transparent pricing with zero egress fees and API driven provisioning.

  • Choose Deployment Model
    Select from various deployment models based on your specific computational needs, allowing greater control and flexibility.
  • Access GPU Resources
    Leverage a wide range of GPU capabilities from different providers, ensuring access to the best options available at any time.
  • Enjoy Cost Predictability
    Know your exact costs upfront with no unexpected charges, supporting more efficient budgeting.
  • Execute AI Workloads
    Run high-performance AI tasks seamlessly, catering specifically to both generative and analytical applications.
  • Centralize Management
    Streamline the deployment process by handling all your GPU resources through a single interface.
  • Optimize Costs
    Select configurations that best fit your workload needs and manage costs efficiently.

Fluence CPU Cloud

Deploy CPU Cloud without hyperscaler cost or lock-in. Choose between shared CPU instances for variable demand or dedicated CPU for sustained workloads, across enterprise-grade providers with transparent daily pricing, unlimited bandwidth, zero egress fees, and API automation.

  • Automate Resource Management
    Easily provision, scale, and manage resources programmatically to streamline operations and reduce manual workloads.
  • Eliminate Budget Surprises
    Enjoy predictable costs upfront, steering clear of unpredictable billing practices commonly seen with traditional cloud services.
  • Ensure Consistent Performance
    Get reliable and constant compute power for production-grade applications and services to maintain operational integrity.
  • Reduce Data Transfer Costs
    Move data freely and operate workloads without incurring additional fees associated with data transfer, ensuring cost efficiency.
  • Optimize Cost for Light Workloads
    Utilize economical shared resources that allow flexibility and scaling based on workload fluctuations.
  • Choose Your Provider
    Select from a range of high-performance providers to fit specific workload needs, enhancing reliability and performance.

Fluence Blockchain Nodes

Deploy blockchain nodes on Fluence's cloudless compute platform with a decentralized backbone. Supports multichain node deployments and automated provisioning and management to reduce vendor lock-in and scale Web3 workloads.

  • Provide Decentralized Resources
    Leverage a global network of independent providers ensuring reduced dependence on centralized cloud services.
  • Manage Across Protocols
    Simplify multichain operations with seamless deployment and management of various blockchain node types.
  • Reduce Operational Costs
    Experience significant cost savings by utilizing Fluence's scalable pricing model designed for Web3 applications.
  • Automate Node Services
    Utilize automated features to reduce time and complexity in deploying and managing blockchain nodes.
  • Leverage Partnerships
    Enhance your deployment process with integrated tools and services from leading decentralized solutions.

AI Agents

An agent platform that runs agent logic, tools, memory, and model endpoints on Fluence, with transparent pricing for agent workloads.

  • Runs Workloads Efficiently
    This feature enables agents to operate seamlessly by managing their required logic and resources on Fluence's infrastructure.
  • Offers Predictable Costs
    Users can anticipate expenses without hidden fees, allowing for better budget management.
  • Streamlines Integration
    This integration allows users to deploy various AI models and services under one platform efficiently.
  • Optimizes Resource Usage
    This feature minimizes expenses by only utilizing GPU resources when required for inference tasks.
  • Enhances Data Transfer Efficiency
    With zero egress fees, users can operate data-intensive agents without additional costs associated with large data transfers.
  • Ensures Flexibility
    This flexibility allows users to optimize their infrastructure according to their evolving needs, avoiding reliance on a single provider.

AI Inference

Deploy production inference pipelines with GPUs optimized for throughput, latency, and cost. Route inference to open-source models in Fluence GPU containers or connect to external APIs for proprietary models.

  • Optimize Performance
    Achieve high efficiency and speed in AI computations to enhance user experience.
  • Connect Open Source And Proprietary Models
    Utilize both community-driven models and enterprise solutions for a flexible AI strategy.
  • Deploy Anywhere, Anytime
    Easily deploy AI inference across various environments without vendor lock-in.
  • Move Data Freely
    Streamline operations without worrying about data movement costs.
  • Know Your Costs Upfront
    Avoid unexpected charges and plan your expenses effectively.

AI Training

Train large models and run distributed ML workloads with high-performance GPUs and scalable infrastructure.

  • Harness Advanced GPU Technology
    Get access to high-performance GPUs like H100 and A100 specifically aimed at providing optimal performance for AI training workloads.
  • Empower Large Models Training
    Utilize Fluence's decentralized infrastructure to efficiently achieve distributed machine learning training for large-scale models.
  • Maintain Full Control
    Avoid dependency on a single vendor with Fluence's open standards, enabling fluid migration and interoperability.
  • Scale On Demand
    Flexibly scale your infrastructure up or down based on your project needs, eliminating wasted resources and costs.
  • Understand Your Costs
    Budget effectively with predictable billing and full transparency on costs associated with GPU usage while training your AI models.

Fine-tuning

Fine-tune large language models, vision models, and generative AI models with LoRA, QLoRA, supervised fine-tuning, or full-parameter training. Choose GPU containers, VMs, or bare metal with transparent hourly pricing and zero egress fees.

  • Customize Models Effectively
    Adapt large-scale AI models to your specific requirements by leveraging scalable GPU and CPU resources seamlessly.
  • Maximize Efficiency
    Utilize a diverse network of compute providers to enhance your model fine-tuning outcomes.
  • Tailor Deployments
    Select the optimal infrastructure for specific workloads, whether for quick tests or extensive training runs.
  • Control Costs
    Easily estimate costs based on GPU rates, count, and runtime, enhancing financial planning for AI projects.

Generative AI

GPU-based environment for generative AI workloads including language, image, video, audio, and multimodal generation; deploy via containers, VMs, or bare metal across multiple GPUs.

  • Supports All Modalities
    Fluence facilitates a wide range of generative AI applications by supporting text, image, video, audio, and other multimodal workloads all in one platform.
  • Access Diverse GPU Options
    Users can select from a diverse range of GPU models and configurations from multiple providers, allowing for flexible resource allocation based on specific needs.
  • Flexible Deployment Options
    Select the most appropriate deployment environment for your generative AI applications, whether you prefer containers, virtual machines, or dedicated hardware.
  • Clear Cost Management
    Avoid unexpected costs with a simple and clear pricing model for all workloads, ensuring predictability in budgeting.

References

Methodology and sourcing behind the market figures shown above.

ML training infrastructure and orchestration

Estimate based on reported AI training/infrastructure figures in the search results. TrendX lists the AI Training Market at USD 12.00B in 2025 with a 24.5% CAGR (2026–2034) — this most closely matches training infrastructure demand. ML orchestration tools are a smaller subset (~US$0.74B in 2024 per QYResearch/OpenPR). Precedence Research shows the broader AI infrastructure market at USD 72.02B in 2025, indicating substantial adjacent spend on hardware and platform services. Combining the AI Training market (primary driver) with the orchestration tools subset yields a 2025 market estimate of roughly USD 12.7B and adoption-driven growth aligned with the 24.5% CAGR reported for AI training.

Managed model inference and serving

Estimate based on published market reports for AI inference / inference-as-a-service. Precedence Research explicitly reports USD 23.40B for AI inference-as-a-service in 2026 and a 26.8% CAGR; independent estimates (inference server reports) show similar high-growth trajectories (mid-20% to high-20% CAGR), with at least one broader AI inference report citing a lower 16.6% CAGR.

Foundation model training and fine-tuning

Primary source: TrendX Insights forecast for the AI fine-tuning market (maps to foundation-model training and fine-tuning). TrendX reports a $3.15B market in 2025 and projects $20.90B by 2034 with a 23.4% CAGR (2026–2034). Other search results are technical/provider guidance without explicit market sizing.

Model-Agnostic Runtime and Deployment

Estimate derived from published AI platform and ModelOps market figures in the search results. MarketsandMarkets values the AI platform market at ~USD 18.22B (2025) and identifies the Model Deployment & Serving functionality as the fastest-growing subsegment (CAGR 44.2%). MarketResearchFuture reports the ModelOps market at USD 4.339B (2024). The model-agnostic runtime & deployment capability sits at the intersection of ModelOps, model hosting and deployment/serving; using ModelOps as a proximate upper bound and deployment/serving as the growth benchmark yields a conservative current-size estimate of about USD 3.5B and growth potential aligned with the 44.2% CAGR cited for deployment & serving.

Blockchain node hosting and Web3 infrastructure

Primary estimate based on Growth Market Reports' Web3 Infrastructure figures (USD 4.4B in 2025; 38.7% CAGR 2026–2034). A LinkedIn analysis cites a narrower dedicated-nodes subsegment CAGR of ~4.3% (2026–2033), indicating growth varies by scope (full Web3 infrastructure vs. node-only hosting).

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