Synopsis
Big tech and startups are developing orbital data centers to process AI-driven data in space, reducing latency and energy use. This edge computing approach allows satellites to prioritize and transmit high-value data, enhancing mission autonomy and enabling quicker insights for sectors like defense and climate monitoring.Listen to this article in summarized format
This approach is becoming increasingly important as modern satellites, especially those used for earth observation, produce vast amounts of data that can overwhelm bandwidth and slow down real-time decision-making. Experts believe that by processing data at the “edge” in space, we can gain quicker insights, selectively transmit data, and enhance mission autonomy.
A growing ecosystem of players, including global firms like Hewlett Packard Enterprise and Indian startups such as Pixxel, Skyroot Aerospace, Dhruva Space, SatSure and Digantara, are developing capabilities that combine onboard computing with ground-based systems.
Early applications span sectors such as defence and intelligence (ISR), agriculture monitoring, climate modelling, disaster response and border surveillance, where timely data is crucial.
The rise of AI is central to this shift. Machine learning models enable satellites to prioritise, compress and interpret high-value data in orbit despite tight power and compute limits, making space-based computing a complementary extension of terrestrial cloud and edge infrastructure in an increasingly distributed, AI-powered world.
Bengaluru-based Space technology company Digantara aims to deploy a constellation of 15 satellites for space domain awareness by 2027 to track and monitor adversary satellite movements and space debris.
Anirudh Sharma, CEO of Digantara, said edge computing serves in reducing downlink and information latency by processing closer to the source. “The second is enabling inference and autonomy onboard, for instance, when two satellites within the same constellation exchange data between them and support through inter-satellite links to allow for constellation maintenance and collision avoidance.”
“From a defence standpoint, this becomes significant at higher orbits, GEO and beyond, where ground-in-the-loop decision cycles are extremely difficult. Onboard autonomy is the infrastructure that holds significant value for decision making. The critical constraint is ensuring reliability of such analysis and ensuring that there are no false positives,” he said.
Ryan D’Souza, country manager for AI and high performance computing at Hewlett Packard Enterprise, an enterprise technology company, said the company’s Spaceborne Computer programme demonstrates how data-centre-class computing can be extended into space, allowing missions to process data closer to where it is generated rather than relying entirely on earth-based systems. “For deep-space or lunar missions led by Indian Space Research Organisation, near real-time data analysis at the edge can significantly improve responsiveness and operational efficiency,” he said.
HPE’s Spaceborne Computer-2, deployed aboard the International Space Station, integrates high-performance computing (HPC), AI and machine learning using commercial off-the-shelf hardware, tested for resilience in extreme space conditions. Industry leaders said such capabilities will be essential as space assets become more autonomous. Pawan Kumar Chandana, chief executive, Skyroot Aerospace, a Hyderabad-based private space launch company, said processing large volumes of data directly in orbit is critical. “To enhance real-time autonomy of space assets, ability to process large data originating from them is a must… space compute qualifies as critical infrastructure and demands sovereignty,” Chandana said.
Awais Ahmed, founder and chief executive of Pixxel, a Bengaluru-based private space technology company building a hyperspectral imaging satellite constellation, said the growing volume of data generated in orbit is already straining downlink capacity, making selective, in-orbit decision-making increasingly important.
“As earth observation systems scale, space-based processing will become increasingly important… The real value is not in processing everything onboard, but in making smarter decisions in orbit, whether through filtering, intelligent compression, or prioritising what to transmit first,” Ahmed said, highlighting the unique challenges of hyperspectral imaging, where each scene carries dense spectral data.
He said that for companies like Pixxel, there is a clear opportunity to move certain forms of intelligence closer to the source to improve responsiveness and efficiency, while still relying on ground infrastructure for deeper analysis and large-scale model execution. Ahmed also pointed to the constraints of operating AI systems in orbit. “Real-time AI inference in orbit must operate within very tight power, compute and thermal constraints… the strongest use cases are likely to be around triage, prioritisation and selective processing of the highest-value data,” he said, adding that Pixxel already uses such techniques for compression and cloud detection to optimise data transmission.
Krishna Teja Penamakuru, chief operations officer and cofounder of Dhruva Space, a Hyderabad-based, full-stack space engineering company providing end-to-end satellite solutions, including design, manufacturing, launch and operations, said space-based computing must be designed as part of the broader mission architecture rather than in isolation.
“For enterprise and government missions, we see space-based computing as an enabler. The real value lies in making first-order decisions in orbit: filtering, prioritising and compressing data, while leveraging ground systems for deeper analytics,” he said.
Penamakuru said that in full-stack missions, compute architecture is a strategic decision tied to the entire data pipeline, from onboard systems to ground infrastructure, playing a key role in ensuring performance, reliability and data sovereignty.
“Processing selectively in orbit improves latency and reduces bandwidth dependency, but control over the end-to-end data pipeline is what ultimately enables true data sovereignty,” he said, adding that in small satellite platforms, every compute decision involves trade-offs against power, thermal and reliability constraints.
While in-orbit computing can reduce dependence on ground stations in low-latency scenarios, D’Souza said it is unlikely to replace earth-based infrastructure entirely. Applications such as large-scale remote sensing and disaster management will still require data aggregation and coordinated response on the ground.
