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RESULTITY VISION: Inference is the newest type of energy

Resultity is an open, verifiable inference layer. Familiar APIs, no intermediaries, no lock-in. Consume and supply compute under transparent rules.

What is inference?

Definition • How it’s delivered

Inference is the forward pass of a pretrained model — you send in inputs (text or media data), the model runs its computations, and you get back the result. In production, it’s metered in tokens or other units derived from machine time. The larger the model (more parameters), the higher the demand for compute — most often powered by GPUs, built for massive parallelism.

A model runs on dedicated compute and delivers its output via an API service. Applications connect, send requests, and receive responses — whether the system is local or in the cloud, this is how AI becomes part of real products.

Cloud Inference: A Rapidly Expanding Market

High growth, high spend, and massive opportunities for efficiency.

AI inference is entering its scale phase: the addressable market rises from $97.2B (2024) to ~$253.8B by 2030, while ~90% of an AI system’s lifecycle spend accrues at the inference stage. At the same time, data-center compute demand is set to more than triple by 2030, and capital poured into AI compute has surged ~7.5× in two years. Tightening GDPR/CCPA regimes push compute closer to the data, and Llama-/Mistral-class open models now deliver enterprise-grade quality without expensive licenses.

The Challenges of Today’s Inference Market

Our roadmap outlines how Resultity will solve them through decentralization.

The current AI inference market is dominated by centralized providers, leading to inefficiencies, risks, and high costs. These issues stem from reliance on single infrastructures and closed ecosystems.

A decentralized architecture can distribute workloads, improve resilience, lower costs, and return control over data and infrastructure to the community.

Read roadmap

High Costs

Centralized inference providers often impose premium pricing, making large-scale deployments prohibitively expensive for many businesses.

Vendor Lock-In

Switching providers is costly and complex due to proprietary APIs, model formats, and billing structures.

Data Privacy Risks

Sending sensitive data to centralized servers increases exposure to breaches and regulatory non-compliance.

Single Point of Failure

Centralized infrastructure is vulnerable to outages, downtime, or service disruptions impacting all clients at once.

Limited Customization

Providers often restrict model tuning, runtime parameters, or deployment configurations, limiting innovation.

Latency and Geography Constraints

Centralized data centers can cause high latency for users in remote regions, degrading real-time application performance.

Resultity: Built to Change the Game

We are building a decentralized AI inference network — powered by expertise, scaled by community, and future-proofed by governance.

The Team

Resultity Team

Core engineers, product builders, visionaries, and support — shipping infrastructure that makes decentralized inference viable at scale.

The Community

Resultity Community

A network of committed compute providers and AI operators — from solo GPU owners to large-scale farms — delivering real capacity and testing every layer of the stack.

The DAO (Future)

Resultity DAO

A governance system to secure the network’s evolution, align incentives, and keep Resultity open, fair, and unstoppable.

We make inference cheap, reliable, and practical. Our network unlocks a market where compute providers earn by contributing GPU power — from individual operators to large-scale farms. Builders connect seamlessly, slash infrastructure costs, and scale without sacrificing control or performance. By bridging supply and demand in one decentralized system, Resultity removes vendor lock-in, maximizes resource use, and keeps AI accessible for those who need it most.

Resultity aligns with these tailwinds through a price advantage (50–95% vs centralized), a transparent 50/50 revenue share with node operators, and premium SLA clusters + multi-rail payments that increase LTV and widen accessible demand.

Our Approach to Building the Network

Key principles guiding Resultity towards affordable, scalable, and reliable AI inference.

AffordabilityApplicabilityReliability

OpenAI-compatible API

Plug in with familiar endpoints and schemas, cutting integration time and migration risk for existing stacks.

No idle hardware parks

Elastic supply from community nodes keeps utilization high so you pay for useful inference, not idle racks.

Decentralized capacity

Distributed nodes absorb demand spikes and avoid a single point of failure, improving service resilience.

Subclouds

Spin up isolated environments with custom latency, compliance, and runtime constraints on demand.

Pay-per-job model

Spend maps to completed work—reducing overprovisioning and eliminating unused subscription hours.

Redundancy by design

Automatic multi-node failover and mirrored jobs maintain continuity under load and regional incidents.

RAG and agent hosting

Bring your data, deploy workflows, and keep ownership end-to-end with native storage and routing hooks.

Open-source models first

Leverage open models for lower base cost and rapid upgrades—swap stacks without licensing lock-ins.

Verifiable execution

Cryptographic attestations and integrity checks prove jobs ran as requested—raising trust across parties.

Custom pricing tiers

Align cost with usage patterns, model classes, and SLA targets instead of one-size-fits-all bills.

No corporate overhead

Direct more spend to operators and capacity, not office leases or middle layers with fixed markups.

Security-first routing

Jobs flow to verified nodes with enforced posture, narrowing the attack surface across the network.

DAO-managed governance

On-chain rules and community votes steer upgrades and incentives—keeping the protocol adaptable and fair.

Pricing shaped by the DAO

Transparent incentives let rates track real supply/demand—reducing opaque markups over time.

Continuous monitoring

Health checks, benchmarks, and performance scoring feed the scheduler for predictable outcomes.

Plug-in API, zero lock-in

API compatible · subclouds · RAG/agents — integrate fast, keep control.

Reliable by design

Redundancy · verifiable jobs · secure routing · live monitoring.

Pay only for value

Pay-per-job · open models · DAO pricing · no idle spend.

Subclouds: Your AI, Your Rules

Custom inference environments, deployed on Resultity’s decentralized infrastructure.

Planned after the main network launch, Subcloud will let you spin up dedicated inference environments — fine-tuned for your workload, isolated for your control. Whether you need a specific model stack, custom latency targets, or compliance boundaries, Subcloud gives you the levers to configure it.

It’s AI-as-a-Service, but on your terms. All deployments can be paid for directly in $RTITY tokens, aligning usage with network growth.

Subclouds Docs

Space: Build, Host, Automate

A marketplace for agents, RAG pipelines, and AI-powered workflows — running on Resultity.

Also scheduled for post-mainnet delivery, Space will be the hub for building, hosting, and monetizing AI applications on top of our network.

From retrieval-augmented generation to fully automated agents, Space lets you deploy services that tap into decentralized compute, storage, and APIs.

Payment and monetization flow through $RTITY, creating a native economy for AI solutions.

Space Docs

Public-Good AI

Access where it matters most — supporting research, startups, and public initiatives.

Startups

Non-dilutive grants, inference credits, and guided onboarding reduce early burn and accelerate launch so founders can validate markets and ship faster without giving up equity.

Open Science

Shared datasets, reproducibility tooling, and capacity quotas for collaborative research unlock access to cutting-edge models beyond paywalled labs and promote global knowledge transfer.

Government

Secure deployments with data-residency and auditability support sovereign AI, mission-critical public services, and national digital resilience under clear compliance controls.

Medicine

Preferential rates for hospitals, labs, and biotech enable privacy-preserving clinical workloads with logging and traceability features to meet regulatory standards like HIPAA/GDPR.

Education

Affordable access for universities and technical schools: bundled credits, research quotas, and shared labs that bring hands-on AI into classrooms and capstone projects.

Civic Tech

Discounted compute for NGOs, civic platforms, and nonprofit ventures to power open data portals, community tooling, and impact-driven services at sustainable cost.

Our Foundation for Success

Core strengths that position Resultity for sustained leadership in decentralized AI.

A multidisciplinary team of engineers, product leaders, and operations specialists with a track record of delivering reliable, scalable, and innovative AI infrastructure.

An engaged global community of compute providers and contributors that continuously expands the network’s capacity and resilience.

Established best practices in security, observability, and operational discipline — ensuring sustainable growth and trust at scale.