From AWS to Walrus and Filecoin: How the Web3 Data Layer Challenges the Cost and Trust Structure of Cloud Computing

By 2026, cloud service spending has become the second largest expense for mid-sized IT and SaaS companies after labor costs, accounting for an average of 10% of annual revenue. AI and machine learning workloads make up 22% of cloud spending and cause monthly bills to fluctuate frequently between 5% and 10% of revenue, making financial forecasting and profit control extremely difficult. Meanwhile, in 2025, AWS, Microsoft Azure, and Google Cloud all experienced multiple large-scale outages. High costs, data lock-in, and frequent disruptions are jointly driving enterprises to explore alternative data infrastructure.

Against this backdrop, the Web3 data layer—encompassing decentralized storage, on-chain data availability layers, and AI-native memory layers—is moving from marginal experiments in the crypto-native community to the evaluation radar of infrastructure decision-makers. As of July 1, 2026 (Beijing time), according to Gate market data, the token UB of the decentralized data protocol Unibase was quoted at $0.08298, down 22.30% in 24 hours, but up 429.16% over the past year, with a market cap of approximately $207 million. This price volatility reflects strong market attention on the Web3 data layer赛道, while also revealing the high volatility of emerging infrastructure in its early commercialization stage. This article systematically compares the Web3 data layer with traditional cloud databases from four dimensions: cost structure, data security and transparency, scalability, and AI training data suitability.

Cost Structure: From "Rental Model" to "Competitive Pricing"

Traditional cloud storage pricing models are built on the capital and operating expenses of centralized data centers and include significant cross-region premiums. AWS S3 Standard storage costs approximately $267 per TB per year. Decentralized storage protocols are entering this market at significantly lower prices.

Walrus—a decentralized storage protocol backed by the Sui network with $140 million in funding—offers a subsidized price of $50 per TB per year. This means that under subsidy conditions, Walrus costs about one-fifth of AWS S3. Even without subsidies, Walrus's target pricing of approximately $0.005 per GB per month is still significantly lower than AWS S3's standard ~$0.023/GB/month.

But cost comparisons cannot be based solely on storage fees. The main cost trap of traditional cloud services lies in data egress fees—every time data crosses regional boundaries, cloud providers charge extra fees. Decentralized storage protocols like Shelby (co-developed by Aptos Labs and Jump Crypto) use a single global namespace design, allowing data to be migrated on demand between regions without incurring additional regional premiums. Shelby expects its egress pricing to be about 70% lower than traditional cloud providers.

In November 2025, Filecoin announced a full pivot to the "Onchain Cloud" strategy, positioning itself as a "verifiable, developer-owned infrastructure" offering on-chain storage services at prices below AWS. As of early 2026, over 100 teams are building on Filecoin Onchain Cloud, processing more than 6,500 payment routes.

From a cost structure perspective, the core advantage of decentralized storage lies in: no need to bear the infrastructure capital expenditure of large-scale data centers, storage nodes operated by globally independent participants, and supply-side competition driving down unit storage costs. However, it should be noted that some current project prices include subsidy components, and long-term sustainability remains to be observed.

Data Security and Transparency: Verifiability vs. Trust Assumptions

The security model of traditional cloud databases is built on "trusting a single service provider." Users rely on the internal systems of AWS, Azure, or Google Cloud to ensure data integrity, access control, and compliance. But this model has two structural flaws:

First, users cannot independently verify whether the cloud provider is handling data as promised. Shelby points out that traditional cloud storage "has no native mechanism to verify what data was provided, under what rights, or whether authorization was complied with." In the event of data breaches or insider unauthorized access, users can only rely on the provider's post-incident audit reports.

Second, centralized architecture has single points of failure risk. If a specific cloud provider's infrastructure experiences a regional failure or faces censorship, all applications relying on that provider will be affected. Decentralized storage protocols like Walrus, by dispersing data across globally independent nodes, aim to "return power to users," providing stronger privacy protection and censorship resistance independent of any single company.

The Web3 data layer introduces a different security paradigm: verifiability. Take The Graph as an example, its distributed indexing protocol uses multiple independent Indexers who stake GRT tokens to perform indexing work, and query results can be verified through cryptographic proofs. This design allows data consumers to ensure data correctness through economic incentives and cryptographic mechanisms rather than trusting a single centralized node.

Unibase's decentralized data availability layer (Unibase DA) further introduces zero-knowledge proofs and fraud proofs into the data verification process, making on-chain data verifiability the infrastructure layer for AI Agent interactions. For scenarios requiring high-certainty data—such as price oracles for DeFi protocols, voting records for governance systems—this verifiability has irreplaceable value.

However, it must be pointed out that the current security model of decentralized storage and data layers is not without cost. The decentralization of node operations introduces more complex key management and data redundancy strategies, and the learning curve and operational complexity of some protocols remain higher than traditional cloud services.

Scalability: Throughput Bottlenecks and Modular Breakthroughs

The scalability of traditional cloud databases is limited by the infrastructure capacity of a single cloud provider, but leading vendors like AWS and Azure provide ample scalability for most application scenarios through global regional deployments and elastic computing resources. The scalability challenges for the Web3 data layer are more pronounced—the throughput limitations of blockchain itself have long been the core bottleneck constraining the scaling of on-chain data applications.

This situation is changing. In January 2026, Celestia announced the Fibre Blockspace protocol, achieving a throughput of 1 terabit per second (1 Tbps) in tests across 498 nodes, a 1,500x improvement over the original roadmap target. Based on this infrastructure, OnchainDB launched a "pay-per-query" database model—developers store application data on Celestia's data availability layer, and earn revenue each time data is accessed. Its design allocates 70% of read/write revenue to application developers and 30% to the platform.

The underlying logic of this model is: when the per-byte cost of the underlying blockchain drops low enough, micropayment-based per-query data retrieval by AI Agents becomes economically viable. OnchainDB positions itself as a "discovery layer" for AI Agents—allowing AI Agents to autonomously discover datasets, pay per query, correlate information across applications, and process results without human intervention.

In the indexing layer, The Graph's 2026 technical roadmap covers 6 products and AI integration plans, aiming to position itself as the data backbone for Web3 applications. The core logic is: as the multi-chain ecosystem expands and the number of applications grows, the demand for indexing and querying on-chain data will rise exponentially, and centralized indexing solutions cannot meet the requirements of decentralized applications for data censorship resistance and verifiability.

From a scalability perspective, the Web3 data layer is shifting from the "blockchain is too slow" narrative to a new phase of "modular infrastructure supporting large-scale data applications." However, this transition still needs time to be validated—Celestia Fibre's 1 Tbps throughput is currently in the testing phase, and actual performance in large-scale production environments remains to be seen.

AI Training Data Advantages: Traceable, Verifiable, Monetizable

The quality and traceability of AI training data are becoming key bottlenecks constraining the development of large models. The collection, labeling, and verification processes of traditional AI training data are highly centralized, making it difficult to trace the source, authorization, and contribution of data. The Web3 data layer offers differentiated solutions in this area.

Unibase is a typical representative of this direction. As a decentralized memory layer designed specifically for AI Agents, Unibase provides continuous learning and cross-platform collaboration capabilities for AI Agents through three modules: Membase (AI long-term memory system), AIP Protocol (Agent interoperability protocol), and Unibase DA (data availability layer). Unlike traditional AI systems that rely on limited context windows, Unibase enables AI Agents to continuously retrieve historical information across time points, achieving true continuous learning. Its token UB was priced at $0.08298 on July 1, 2026, although down 22.30% in the short term, it surged 312.75% in the past 90 days and 429.16% over the past year, indicating that the market has assigned a significant premium to the AI + data infrastructure narrative, but short-term volatility also reflects that this track is still in early-stage game theory.

In terms of data provenance and contribution incentives, Poseidon (a blockchain AI data infrastructure project incubated by Story Foundation) is building a platform where users can contribute AI training data and receive compensation. Its core mechanism: recording the source, screening, labeling, and contribution value of each piece of training data on the blockchain, allowing data contributors to trace the usage of their data and receive corresponding rewards.

For AI training data providers, the Web3 data layer solves two problems that traditional models cannot handle well:

Verification problem: In traditional AI training data procurement, data buyers cannot independently verify the legality of data sources, the accuracy of labeling, and the scope of authorization. A verifiable on-chain data layer allows every data transaction to be independently audited.

Incentive problem: The distribution of revenue from traditional data labeling and collection is highly opaque. Through smart contracts and token incentive mechanisms, the Web3 data layer can achieve automated, transparent revenue distribution among data contributors, labelers, and model trainers.

Global AI demand is expected to reach $300 billion by 2026. At this scale, data acquisition costs and quality assurance will become core competitive factors for AI companies. The verifiability and disintermediation features provided by the Web3 data layer give it a unique ecological niche in AI training data infrastructure.

However, it should be noted that the actual adoption of the Web3 data layer in AI training scenarios is still in its early stages. Unibase's testnet has recorded over 200 deployed Agents and over 12.4 million on-chain memory entries, but these data primarily come from crypto-native projects, and adoption by traditional AI companies remains limited.

Conclusion

The market size for Web3 data indexing platforms is expected to grow from $2.12 billion in 2025 to $2.68 billion in 2026, a compound annual growth rate of 25.9%. By 2030, this market could further expand to $6.77 billion. This growth trajectory indicates that the market is responding with real money to a core question: the architectural choice of data infrastructure is shifting from "convenience first" to "verifiability and data sovereignty first."

From a cost perspective, decentralized storage has already shown a significant price advantage over traditional cloud services—Walrus is about 80% cheaper than AWS S3, and Shelby's egress pricing is expected to be 70% lower. But whether these price advantages can persist after de-subsidization remains to be tested over time.

From a security and transparency perspective, the verifiability provided by the Web3 data layer—ensuring data correctness through cryptographic proofs and economic incentives—is a differentiated value that traditional cloud services cannot offer. For high-stakes scenarios (DeFi, governance, AI training data provenance), this verifiability could become a decisive selection factor.

From a scalability perspective, Celestia's 1 Tbps throughput and The Graph's multi-chain indexing architecture are addressing the technical bottlenecks for scaling Web3 data layer applications. However, most of these infrastructures are still in testing or early production stages, and large-scale validation will take time.

From the perspective of AI data suitability, the design of the Web3 data layer in data provenance, contribution incentives, and verifiability aligns well with the infrastructure needs of AI training data. However, the adoption curve of traditional AI companies remains the biggest uncertainty variable.

The most reasonable judgment at present may be: The Web3 data layer is not a comprehensive replacement for traditional cloud databases, but rather offers differentiated value in specific scenarios—applications requiring verifiability, data sovereignty, and censorship resistance—that traditional architectures cannot achieve. As modular blockchain infrastructure matures and AI data demand grows, this differentiated value is gradually transforming from "theoretical advantage" into "quantifiable commercial advantage." For infrastructure decision-makers, closely monitoring developments in this area and conducting small-scale pilots in appropriate application scenarios may be the most pragmatic strategy at this stage.

FAQ

1. Can the Web3 data layer fully replace AWS cloud databases?

Currently, no. The Web3 data layer has advantages in verifiability, censorship resistance, and data sovereignty, but it lags behind AWS in read/write latency, operational maturity, and ecosystem toolchains. The two are more suitable for complementing rather than replacing each other. The Web3 data layer is suitable for scenarios requiring high transparency and auditability, while traditional clouds are suitable for high-frequency, low-latency businesses.

2. Is decentralized storage really cheaper than AWS?

In terms of pure storage fees, protocols like Walrus are indeed lower than AWS S3 at present, but it should be noted that their prices partially include subsidies. When including data egress fees, decentralized protocols may be cheaper due to no regional premiums, but long-term pricing stability remains to be observed, and additional redundancy and retrieval costs need to be considered.

3. How does the Web3 data layer ensure data security?

Through encrypted sharding, multi-node redundant storage, and economic incentive mechanisms (such as staking penalties) to prevent data loss or tampering. At the same time, on-chain verifiability makes data access records and change history publicly auditable, reducing risks of insider malfeasance and single points of failure, but users must manage their private keys.

4. Why does AI training need the Web3 data layer?

Because AI training heavily relies on the legality of data sources and the quality of labeling. The Web3 layer can trace the contributor, authorization scope, and labeling process for each piece of data, and automatically distribute revenue through smart contracts, solving the black-box problem of traditional data procurement, thereby reducing legal risks and improving data quality.

5. What are the main obstacles to adopting the Web3 data layer currently?

Main obstacles include: technical maturity (throughput and latency still lag behind centralized solutions), developer learning costs, lack of standardized interfaces, and regulatory concerns from traditional enterprise compliance departments regarding on-chain data. Additionally, token price volatility affects the stability of long-term budget planning for enterprises.