Artificial intelligence developers face significant challenges in sourcing reliable, high-quality data for machine learning and model training applications. Centralized services are prominent but pricey. Moreover, they can lock developers into proprietary ecosystems that do not properly interact with other parts of their tech stack, causing headaches and extra costs.
With people’s ever-growing understanding of digital privacy, ethical data collection, and how online services use and sell their information, there has been a significant rise in demand for decentralized AI data clouds that respect privacy and are easily accessible. These services offer more control, better integrability, and more responsible resource provisioning.
This growing demand has led to the establishment and prominence of a new wave of data clouds, decentralized platforms that focus equally on contributors and consumers. They enhance data control, provide monetization opportunities, and protect privacy while offering lower costs, greater trust, and superior scalability compared to centralized services.
Below, we’ll highlight some of the top decentralized AI data clouds for developers in 2025. We’ll look at a mixture of providers, including comprehensive and niche services, as well as some operating within a few areas. We’ll provide an overview of each and discuss their services and what types of developers they’re best suited to. Let’s get started.
1. OORT – A High-Quality Data Cloud for Decentralized AI
OORT is the industry’s most robust and versatile data cloud for decentralized AI. Unlike other projects, such as Bittensor, which handles one aspect of the AI data lifecycle, OORT offers a comprehensive and holistic solution to the full cycle of AI data – from collection to storage segment, and will soon offer compute service.
The data cloud leverages blockchain technology and decentralization to enhance privacy, user control, and scalability. It has built an extensive network of resource providers for each segment, so developers can utilize OORT across large and small projects that are either mission-critical or non-priority, providing unparalleled versatility.
Read more: “Here’s How OORT Enables Decentralized AI Applications That Focus On Trust”
AI models are only as good as the data they’re trained on, which is why OORT established its DataHub. It incentivizes users to contribute diverse, real-world, high-quality data that’s then processed and validated for usage. OORT’s datasets have recently rolled out on marketplaces like Databricks Marketplace to acclaim, topping rankings on Google Kaggle.
OORT is a developer-ready cloud that offers processed and verified real-world data for AI model training. Developers can find or commission relevant, specific datasets ideal for Large Language Models (LLMs) and prediction models. Developers can connect to OORT’s products via APIs and SDKs, enabling seamless integration into decentralized AI workflows.
Although it’s an all-in-one AI data solution, OORT hasn’t sacrificed quality. It focuses on provisioning first-class resources and uses a specialized Proof-of-Honesty (PoH) consensus mechanism to verify the data, storage, or compute standard. As such, it’s ideal for developers seeking a complete data cloud with diverse, anonymized, real-world data for model training.
2. Ocean Protocol – Marketplace for Tokenized Datasets
Ocean Protocol is an innovative project that operates a decentralized data marketplace and compute-to-data platform for developers to discover, build, publish, or monetize data sets or AI models without revealing raw information. Like OORT, it uses the blockchain to enhance accountability, transparency, and traceability.
The project utilizes data NFTs and data tokens to connect data assets with decentralized applications (dApps), decentralized finance (DeFi) products, and blockchains, enabling simple integration into crypto applications. Its compute-to-data segment enables providers to monetize private data without revealing it, allowing access without impacting privacy or control.
Ocean Protocol runs on Ethereum and Ethereum Virtual Machine (EVM)-compatible blockchains. Integration is simple because it offers multiple libraries, such as Ocean.js and Ocean.py, each boasting extensive documentation. Because it protects data, Ocean Protocol is ideal for developers seeking access to private or specialized data sets.
3. Bittensor – Decentralized Network of AI Models
Bittensor is an open-participation decentralized AI network focused on AI models. It enables machine learning models owned by different people to communicate and process information collaboratively. The network is split into specific subnets that target specialized AI tasks, like finance, image, and semantic intelligence, which can be used in various applications.
Developers can deploy machine learning models with Bittensor or contribute their own to earn TAO tokens based on performance. People can browse 120+ subnets with different specialties, enabling them to select the one best suited to their purposes. Models effectively compete to earn TAO tokens, ensuring efficiency and high-quality outputs.
Bittensor enables secure and specialized decentralized model training and inference. The network has over 1,000 active nodes and offers SDKs for simplified integration. It uses the Proof-of-Intelligence (PoI) consensus mechanism and rewards validators for scoring contributions, creating an effective decentralized AI data ecosystem for developers.
4. iExec – Decentralized Marketplace for Confidential Computing
iExec is billed as the ‘Trust Layer for DePIN and AI’. It’s a confidential environment that enables the creation of specialized decentralized applications that run in iExec Trusted Execution Environments (TEEs), which offer privacy without requiring advanced confidential computing knowledge.
Developers can use iExec to acquire, securely share, and monetize data without revealing the underlying information, similar to Ocean Protocol. Contributors can earn RLC token rewards for providing computational power to the network. Thanks to its trust layer, iExec also enables the deployment of fully isolated AI agents that maintain scalability and privacy.
While its core function is providing a secure environment for AI model acquisition, monetization, and deployment, iExec also provides rewards for subscribing to marketing emails and enables people to send messages via Telegram or email to registered Ethereum account holders. Overall, iExec is well-suited to developers who require confidential computing or AI data.
5. DIMO – Vehicle Data for ML Models
DIMO is slightly different from the other AI data clouds we’ve discussed. Instead of being a general data provider suitable for many industries, DIMO focuses exclusively on vehicle data. The project has created a network of over 183,000 cars, whose data powers 2,200+ models. It leverages the blockchain to guarantee openness, fairness, and performance.
Since vehicle data is personal, DIMO ensures users retain total control over their cars and data. Moreover, its service works with any automaker, making integration straightforward for contributors, who are rewarded with DIMO tokens for their efforts. The DIMO protocol powers the project and manages on-chain verifiable identity, control permissions, and rewards.
DIMO provides real-world vehicle and telemetrics data (like speed, usage, and location), so it’s explicitly geared toward enterprises and developers. By staking DIMO, users can get API and SDK access. DIMO is a highly specialized vehicle data provider that best suits developers building AI applications related to mobility, logistics, and smart infrastructure.
Conclusion
The rate at which AI-centric applications are being created grows exponentially as the technology improves and use cases expand. All of these projects require data, storage, and computing power. However, centralized services are typically costly, have limited data sets, lack scalability, or reduce developer control, hindering innovation.
The restrictions and limits centralized AI data cloud providers place on developers have given rise to decentralized AI clouds like OORT. These clouds offer a comprehensive suite of services, greater data diversity, and often higher-quality datasets. They bridge the gap between the convenience of centralized services and the performance of decentralized alternatives.
Relying solely on centralized providers is no longer necessary. The blockchain enables greater trust, confidence, transparency, and verifiability. Decentralized AI data clouds can provide permissioned access to private, real-world data that’s diverse and ethically collected for high-quality model training. As AI expands, the demand for similar data can only grow.
Throughout this article, we’ve examined some of the top decentralized AI data clouds for developers. Each project contributes significantly to the industry and can empower developers in different ways according to their needs. We found OORT the most comprehensive and versatile solution, while Ocean Protocol is excellent for securely accessing private data sets.
