As a business due diligence consultant with deep experience in the Web3 storage sector, you understand that surface-level hype is never enough. While the market is impressed by Walrus’s $140 million private funding, $2 billion valuation, and the prestige of being incubated by Mysten Labs, the true focus should be on the team’s actual capabilities: their visible strengths, hidden strategic advantages, and the potential risks that could affect the entire landscape. A full due diligence review reveals that Walrus’s real competitive edge lies not in hype or capital alone, but in the team’s systematic approach of amplifying strengths, reinforcing foundations, and hedging against risks.
Your investigation looks across technical testing, ecosystem interviews, business retrospectives, and data verification. From the implementation of RedStuff coding to strategic integration with the Sui ecosystem and monetization of AI and RWA use cases, each element reflects the team’s operational capability. These findings guide you in identifying explicit advantages, uncovering hidden strategic cards, and anticipating core risks, providing both practical insights and professional depth.
When you examine Walrus’s metrics, the numbers are impressive: 14 million testnet accounts, 5 million Blob data blocks processed, 27.85TB cumulative active storage, 78% of partners from the Sui ecosystem, and over 90% of revenue from AI and RWA scenarios. But the real insight comes from what these numbers represent in capability. Analysis shows that 85% of accounts are native to the Sui ecosystem, highlighting a reliance on ecosystem traffic rather than independent acquisition. Despite this, the team converts traffic into paying customers at a 35% efficiency rate, nearly double the industry average, through precise scenario targeting and strategic subsidies. Funding is used to activate demand quickly, covering integration costs and waiving onboarding fees for smaller clients.
Technical validation confirms the effectiveness of RedStuff’s two-dimensional erasure coding, which maintains 4–5x redundancy. Annual storage costs for 100GB AI training data drop to $2,400, an 80% reduction compared to Filecoin. Single-point failure recovery takes only 36 minutes, 40% faster than Arweave, and 99.98% data availability has been confirmed through multi-node offline tests. These are real advantages that position Walrus strongly in AI and RWA markets. The core capability is not merely resource aggregation, but the ability to turn ecosystem advantages and technical strength into tangible commercial value.
The team demonstrates strong technology scenario adaptation, customizing solutions for AI and RWA use cases rather than competing on superficial technical parameters. AI solutions balance redundancy, security, and efficiency to reduce cost and support high-frequency access, while RWA solutions ensure long-term stability and compliance through verification mechanisms and partnerships with global institutions. Deep integration with Sui using Move-language interfaces reduces developer onboarding time significantly, improving implementation efficiency.
Walrus’s ecosystem strategy is equally strong. The team doesn’t rely passively on the Sui ecosystem; it actively co-creates value by solving storage bottlenecks for AI and RWA projects. Investment in ecosystem support captures a large share of partners and creates a positive feedback loop of dependency, value creation, and deep binding, far surpassing other projects that merely parasitize ecosystem traffic.
Commercial monetization is scenario-based and well thought out. In AI, a three-tier model including storage fees, compute coordination, and value-added services attracts clients and boosts per-customer profitability. RWA monetization incorporates tiered audit fees, annual storage charges, and staking services, contributing a significant portion of revenue. Real-world deployments, such as a Shanghai RWA project generating $200,000 in service revenue with only 25% in costs, confirm profitability.
Beyond visible strengths, Walrus holds strategic cards for long-term success. The team maintains full control of core technology, retaining RedStuff development, storage logic, and compliance verification, while relying on Sui only for non-core functions. Plans for cross-ecosystem expansion are underway, with lightweight interfaces for Ethereum and BSC being developed and pilot collaborations already initiated. Node network optimization is also in progress, reducing costs and incentivizing geographic expansion to ensure scalability and resilience.
Even with these advantages, risks remain. Heavy reliance on the Sui ecosystem makes the project vulnerable to changes in technology, competition, or regulation. Node network limitations and technical dependency could affect stability under high demand, while concentration in AI and RWA scenarios and small-to-medium clients may limit resilience and growth. Addressing these risks requires accelerated cross-ecosystem adoption, node expansion, and scenario diversification, all of which the team has already begun to implement.
Overall, the Walrus team demonstrates precise execution and long-term risk management. Its technology adaptation, deep ecosystem integration, and scenario-based monetization provide a solid competitive foundation, while strategic foresight in proprietary technology, cross-ecosystem planning, and node optimization enables future growth. Short-term potential within Sui is strong, but mid-to-long-term value depends on expanding beyond the ecosystem, optimizing nodes, and diversifying scenarios. Success in these areas could elevate Walrus from a niche market leader to a core Web3 infrastructure provider; stagnation could result in growth plateau and valuation pressure.