- DGrid AI Overview
- Asset Role and Supply
- Market Structure
- YearBull Perspective
- Key Risks
- Primary Sources and Review Scope
- DGrid AI Connects AI Model Access With Decentralized Service Coordination
- DGrid AI’s stated infrastructure role
- The AI Gateway and multi-model access
- Marketplace and scheduling layer
- Routing, settlement, and the transparency claim
- Networks and the unspecified token role
- Recorded market context and open dependencies
- Key takeaways
- Risks and unresolved questions
- YearBull Rank timeline
DGrid AI Overview
DGrid AI (DGAI) is tracked under dgrid-ai. The local profile associates it with BNB Chain Ecosystem, Arbitrum Ecosystem. The source profile maps it to binance-smart-chain, arbitrum-one.
Asset Role and Supply
Its role should be evaluated through network or product use, supply design, governance, liquidity, and trading-venue quality. The reviewed record shows circulating supply about 150.00 million DGAI, total supply about 1.00 billion DGAI, maximum supply about 1.00 billion DGAI. Supply fields may change through issuance, burns, migrations, or source revisions and should be checked against project records.
Market Structure
At the 2026-09-12 review, the local snapshot placed DGrid AI at market-cap rank #251, with market capitalization about $111.64 million and reported 24-hour volume of $49.83 million. These values describe observed scale and turnover, not fair value or guaranteed executable liquidity.
YearBull Perspective
The dated snapshot recorded YearBull Rank #5,326, Bull Score 45/100, Risk Low, and Cycle Mid. Rank, Bull, Risk, and Cycle answer different questions and should be read together.
Key Risks
Material risks include market volatility, liquidity deterioration, protocol or governance failure, concentration, and regulatory change. Historical prices, rankings, and classifications do not predict future performance. Verify contract addresses, network support, custody, and venue availability before acting.
Primary Sources and Review Scope
YearBull methodology · Official website · Technical documentation or whitepaper · Source repository. Profile and market fields were checked against locally stored source records on 2026-09-12. The live snapshot above may be newer than this editorial review.
DGrid AI Connects AI Model Access With Decentralized Service Coordination
DGrid AI describes a network for accessing and coordinating AI inference services across model providers and node operators. Its documented components include an OpenAI-compatible gateway, a model marketplace, and scheduling and settlement layers operating across BNB Chain and Arbitrum One.
DGrid AI’s stated infrastructure role
DGrid AI presents itself as decentralized infrastructure for AI services rather than as a single model or standalone application. Its stated goal is to connect users, developers, model providers, and node operators through a common gateway, marketplace, and scheduling layer.
project materials identifies two broad audiences: Web3 applications and conventional developers. The proposed value is a shared access layer that could reduce the need for each application to integrate separately with multiple AI providers. public materials does not specify which applications currently use the network or how many participants operate its infrastructure.
The AI Gateway and multi-model access
The AI Gateway is described as providing an OpenAI-compatible interface for more than 200 AI models. Compatibility with a familiar interface is intended to let developers request AI services through an established integration pattern instead of building a separate connection for every model provider.
The gateway is also described as a coordination point for routing and service delivery. In practical terms, the network’s stated design requires incoming requests to be directed to participating providers or nodes and then returned through a common access layer. public materials does not identify the supported model families, performance differences, service-level guarantees, or the process used to add and remove models.
Marketplace and scheduling layer
DGrid AI says its marketplace brings AI service providers and users into the same network. The description does not provide a detailed marketplace workflow, but it identifies model providers and node operators as distinct participants whose services may be coordinated through the platform.
A scheduling layer is named alongside the gateway and marketplace. Its stated role is to help coordinate service delivery across participating providers, which implies that request handling depends on available capacity and the network’s routing logic. No specific scheduling algorithm, allocation rule, uptime standard, or dispute process is documented in public materials. These details are relevant to understanding how the system would behave when demand exceeds supply or providers offer different capabilities.
Routing, settlement, and the transparency claim
project materials says DGrid AI coordinates routing, service delivery, and settlement. Routing concerns where an AI request is sent; service delivery concerns the return of the requested inference; and settlement refers to the accounting or payment step between the relevant participants. The source does not define the settlement asset, fee structure, payment flow, or whether settlement occurs on-chain for every request.
DGrid AI also describes its intended services as transparent and verifiable. These are project claims rather than independently established characteristics in public materials. The description does not specify what users can verify, such as provider identity, request execution, model version, output provenance, billing records, or node performance. The practical meaning of these claims therefore depends on technical documentation and operating evidence that are not supplied here.
Networks and the unspecified token role
DGrid AI is categorized within the BNB Chain and Arbitrum ecosystems, and both BNB Smart Chain and Arbitrum One are recorded as supported networks. These network associations indicate the project’s stated blockchain environment, but public materials does not explain which functions use each chain or whether the gateway and inference workloads operate directly on-chain.
No token symbol, genesis date, supply information, token utility, governance role, or settlement denomination is provided. As a result, the asset’s relationship to the infrastructure cannot be described beyond the possibility that a token may be relevant to the broader project. Any claim about staking, access, node rewards, governance, fee payment, or value capture would require additional documentation.
Recorded market context and open dependencies
During the recorded observation window from August 25 to September 14, 2026, DGrid AI’s historical entries showed a best sequential rank of 2,500 and a worst sequential rank of 8,418. The recorded average Bull Score was 42.9, and the dominant cycle label was Mid. These observations provide limited historical context and do not establish adoption, service quality, or future performance.
The same window recorded low risk-state classifications throughout and a median absolute daily move of 2.64%. Those labels describe the historical observations available for the page and should not be read as a conclusion about the project’s technical or operational risk. The core dependencies remain the quality and availability of participating providers, the gateway’s routing and settlement implementation, the meaning of its verification claims, and the still-undocumented role of any associated token.
Key takeaways
- DGrid AI describes a decentralized access layer linking AI users, developers, model providers, and node operators.
- Its AI Gateway is presented as OpenAI-compatible and as offering access to more than 200 models.
- The named marketplace, scheduling, routing, and settlement components are intended to coordinate AI service delivery across participants.
- BNB Smart Chain and Arbitrum One are the recorded network associations, but the role of each chain is not explained.
- public materials does not define a token’s utility, supply, settlement function, or governance role.
- Claims about transparency and verifiability require technical detail showing what users can independently check.
Risks and unresolved questions
- public materials does not identify current users, active providers, node counts, inference volume, or adoption measures.
- The gateway’s routing criteria, scheduling method, uptime expectations, and handling of overloaded or unavailable providers are unspecified.
- The project does not document its settlement asset, fee model, payment process, or whether transactions are recorded on-chain.
- The meaning of transparency and verifiability is not defined, including what aspects of model execution, billing, or provider performance can be checked.
- No token symbol, supply data, utility, governance function, or launch information is provided.
- The source does not describe model licensing, data handling, privacy controls, output reliability, or safeguards for AI services.
YearBull Rank timeline
Newest YearBull Rank value for dgrid-ai: #321.
Rank change (nearest points).
Reading rule: lower is better in this ranking.
- 7d window (2026-10-16): #955 → #321 (up by 634).
- 30d window (2026-09-23): #97 → #321 (down by 224).
Flow context: If the line improves during quiet periods, it can be accumulation. bursty volume can create temporary re-ordering.
Where it trades: If the line breaks range, confirm it across a longer window. a new route can show up as a step change.
Regime context: If the line stair-steps, the cycle may be driven by discrete inputs. cycle pressure can surface as slow bleed in rank.
Volatility posture: If you see repeated snap-backs, assume sensitivity to one factor. big jumps can be data-driven, but also rotation-driven.
Practical note: read the move, then read the stability of the move.
YearBull Rank is a relative placement score used on YearBull to compare a coin against peers within the same dataset. Lower rank numbers indicate stronger placement in the current snapshot.
