DGrid AI DGAI
Rank #236DGrid AI (DGAI) trades at $0.8628, the 236th-largest cryptocurrency by market capitalisation at $129.4 M. Over the past 24 hours it is -5.01% and over the week -11.69%. Our composite Price Strength score reads 41/100, liquidity health 52/100, and 7-day annualised volatility sits near 52% — higher than 38% of the top 100 coins.
DGrid AI · At a glance
Ecoonomia composite scores derived from live market data. Methodology →
DGrid AI · Price chart (7 days)
Price performance
| 1h | 24h | 7d | 30d | 1y |
|---|---|---|---|---|
| +0.00% | -5.01% | -11.69% | -11.69% | +0.00% |
Market statistics
DGrid AI · Market analysis
Volatility & risk
Realised volatility for DGAI is moderate, with a 7-day annualised reading near 52%, placing it above 38% of the top 100 by volatility. Expect wide swings and size positions accordingly.
Liquidity & depth
Around $24.4 M changed hands in 24 hours — about 18.9% of market value turning over daily — which we read as adequate liquidity for its size.
Correlation with Bitcoin
Over the past week DGAI's returns correlate 0.22 with Bitcoin: it moves with relative independence from Bitcoin. Correlation regimes shift, so treat this as a snapshot.
DGAI to USD
| DGAI | USD |
|---|---|
| 0.5 DGAI | $0.4314 |
| 1 DGAI | $0.8628 |
| 5 DGAI | $4.31 |
| 1 DGAI | $8.63 |
| 25 DGAI | $21.57 |
| 5 DGAI | $43.14 |
| 1 DGAI | $86.28 |
| 5 DGAI | $431.40 |
| 1, DGAI | $862.80 |
| 10, DGAI | $8,628 |
Supply & tokenomics
DGrid AI has a circulating supply of 150,000,000 DGAI against a hard cap of 1,000,000,000 — 15% of the maximum already exists. A meaningful share is still to be released, so emission schedules are worth tracking.
DGrid AI · Price prediction 2026–2027
| Period | Low | Average | High | Potential | Bias |
|---|---|---|---|---|---|
| 24 Hours | $0.8505 | $0.8625 | $0.8795 | -0.03% | Neutral |
| 7 Days | $0.8290 | $0.8607 | $0.9057 | -0.24% | Neutral |
| 30 Days | $0.7889 | $0.8540 | $0.9464 | -1.02% | Neutral |
| 3 Months | $0.7251 | $0.8364 | $0.9941 | -3.06% | Neutral |
| 6 Months | $0.6582 | $0.8108 | $1.03 | -6.03% | Bearish |
| 1 Year | $0.5591 | $0.7619 | $1.05 | -11.69% | Bearish |
| End of 2026 | $0.7392 | $0.8409 | $0.9851 | -2.54% | Neutral |
| End of 2027 | $0.5255 | $0.7426 | $1.05 | -13.93% | Bearish |
| 2031 | $0.1876 | $0.4634 | $0.8542 | -46.29% | Bearish |
The ensemble blends a technical (momentum), statistical (volatility) and relative (comparable peers) model. Probabilistic scenarios, not financial advice.
About DGrid AI
How DGrid AI works
DGrid AI is a layer-1 smart-contract platform secured by proof-of-stake: validators lock up (stake) DGAI to propose and attest blocks, and earn rewards for honest behaviour while risking penalties (slashing) for misbehaviour. Developers deploy decentralised applications on top of it.
What is DGrid AI used for?
DGrid AI powers DeFi, NFTs, stablecoins and on-chain applications; DGAI is used to pay transaction fees (gas), to stake and help secure the network, and to participate in governance.
Risks & considerations
All crypto-assets are volatile and high-risk; never invest more than you can afford to lose. Specific risks include validator centralisation, slashing, smart-contract bugs in the ecosystem and competition between chains.
Where to buy DGrid AI
Regional platforms
In Latin America and Spain you can buy DGrid AI on local Spanish-speaking platforms:
| Bitso | Mexico · LatAm |
| Lemon · Buenbit | Argentina |
| Bit2Me · Bitnovo | Spain |
| Binance · Coinbase · Kraken | Global |
DGrid AI vs comparable coins
| Coin | Price | 24h | 7d | Cap |
|---|---|---|---|---|
| Ether.fi ETHFI | $0.6947 | +12.69% | -0.10% | $670.9 M |
| XDC Network XDC | $0.0346 | +0.39% | -3.34% | $689.6 M |
| PancakeSwap CAKE | $2.19 | -0.72% | -0.21% | $695.2 M |
| Pyth Network PYTH | $0.0884 | +1.69% | +4.87% | $696.0 M |
| GHO GHO | $0.9986 | +0.01% | +0.01% | $698.0 M |
| Celestia TIA | $0.7152 | +30.39% | +47.09% | $702.2 M |
DGrid AI · FAQ
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Disclaimer: informational content only, not financial advice. Cryptocurrencies are volatile and high-risk. Predictions are model estimates and may be inaccurate.