EQT eyes $50B India investment by 2030, led by AdaniConnex data-centre push
EQT is reportedly planning to invest $50 billion in India by 2030, including about $30 billion for data centres and $5 billion for renewable energy. The plan includes a major expansion through AdaniConnex, its data-centre joint venture with Adani Enterprises.
What happened
EQT plans to invest $50 billion in India by 2030, including a major AdaniConnex data-centre bet with Adani Enterprises. The Stockholm-based investor will
Key facts
- $50 billion planned EQT investment in India by 2030
- $30 billion planned for data centres
- $5 billion planned for renewable energy
- 50-50 EdgeConneX-Adani Enterprises joint venture formed in 2021
- $100 billion Adani Group AI-ready data centre investment plan by 2035
- $12.7 billion Amazon India cloud investment through 2030
- about $15 billion Alphabet AI infrastructure hub investment
- $15 billion-$20 billion EQT India private-equity investment plan by 2030
- $26 billion EQT invested in India since 1998
- around $7 billion EQT invested since 2023
Why this matters
Retail and commerce technology teams should monitor AdaniConnex as a potential infrastructure partner as expanding capacity may create regional cloud, AI and edge-computing collaboration opportunities.
What to watch
- EQT and AdaniConnex announcements of committed capital, project pipeline, commissioned megawatts and target cities.
- Hyperscaler region/availability-zone expansions and long-term capacity leases in India.
- Indian data-centre power tariffs, renewable energy procurement rules, grid-connection timelines and water-use restrictions.
- Data localization, privacy and AI regulations affecting retail customer-data processing.
- Cloud price reductions, colocation utilization rates and reported capacity shortages in Mumbai, Chennai, Hyderabad, Pune and Delhi NCR.
- Benchmark Indian cloud, colocation and content-delivery costs by metro; renegotiate multi-year capacity and disaster-recovery contracts before new supply is fully absorbed.
- Prioritize AI workloads with direct retail ROI: demand forecasting, dynamic assortment, fraud prevention, customer-service automation and store-level replenishment.
- Design workloads for multi-region and multi-cloud portability, using new Indian zones to improve latency while avoiding concentration exposure.
- Assess whether local data residency and lower latency enable new retail formats such as live commerce, vernacular AI shopping assistants and real-time hyperlocal inventory visibility.