“AceCloud has significantly contributed to AI-focused startups by providing cutting-edge cloud solutions to empower them to achieve their goals efficiently” – Mr. Vinay Chhabra, Co-Founder & Managing Director, AceCloud, a brand of Real Time Data Services

How is AceCloud positioning itself to support the evolving needs of AI-driven startups in India’s rapidly expanding cloud ecosystem?

AceCloud is strategically positioning itself as a key enabler for AI-driven startups in India by offering a comprehensive suite of GPU-powered cloud solutions designed to meet the growing demands of AI, machine learning, and data-intensive applications. Recognizing the critical role of GPU computing in driving innovation, AceCloud offers high-performance cloud platform powered by cutting-edge GPUs such as NVIDIA’s H100, A100, and L40S—tailored for everything from deep learning and data analytics to creative workflows and scientific simulations.

To cater to startups’ dynamic needs, AceCloud provides a flexible and scalable platform, allowing businesses to adjust GPU resources up or down with ease as per evolving business requirements. This ensures cost-efficiency and operational agility as startups evolve. Additionally, with multi-region data centers, AceCloud ensures low-latency access, enabling faster AI processing and a better user experience across geographies.

Further, AceCloud’s dedicated AI and machine learning support team provides expert guidance for optimizing workloads, along with flexible deployment models—including dedicated, shared, and hybrid configurations—so startups can tailor offerings to their specific use cases and budget.

Combined with specialized cloud platform for high-performance computing and a robust portfolio of GPU options across price-performance tiers, AceCloud is empowering the next generation of AI startups in India to innovate faster, scale smarter, and remain competitive in an increasingly cloud-centric world.

What specific features or offerings make AceCloud’s platform particularly suitable for generative AI workloads and innovation at scale?

AceCloud’s cloud platform is purpose-built to meet the demands of generative AI workloads at scale, combining high-performance computing with enterprise-grade simplicity and cost-efficiency. High-performance GPU options, like 80 GB Vram with upto 2 TB/s memory bandwidth/ 48 GB Vram with upto 1TB/s memory bandwidth, along with workload specific compute optimized instances, provide the raw horsepower required to run complex AI and ML models while optimizing cost through intelligent right-sizing and advanced cloud cost management tools.

AceCloud empowers startups through GPU fractioning support which in turn allows them to optimize their AI/ML workloads by enabling multiple users or processes to share a single GPU securely and efficiently, thereby maximizing utilization and reducing idle resources. This leads to better cost-efficiency, especially for varied workloads that don’t require full GPU power and allows parallel model training and inferencing without compromising performance. By tailoring compute resources to workload needs, we enable businesses to accelerate development cycles, improve ROI on GPU compute investments, and scale AI/ML operations more effectively across teams or services on AceCloud.

Additionally, our single, unified cloud platform supports unlimited scalability, backed by 99.95% uptime, robust cybersecurity practices, and support for India’s data residency regulations, to enable secure, seamless, and efficient operations round the clock.

We simplify complex AI workflows for businesses by offering capabilities such as real-time usage tracking, automated disaster and backup recovery, 24×7 human assistance, and intuitive interfaces that make AI experimentation seamless. Whether businesses are training foundational models or running large-scale inference, AceCloud’s platform provides the compute power needed for fast processing, ensures compliance with data regulations, and simplifies day-to-day operations required for dependable AI deployment.

Further, we offer spot pricing instances for the GPU computes to enable businesses to run short-term and fine-tune workloads at upto 50-70% lower cost than on-demand pricing.

AceCloud empowers startups and enterprises alike to push boundaries in GenAI—offering the speed, control, and flexibility needed to turn ambitious ideas into deployed solutions at scale.

Can you share examples of how AceCloud has helped AI-focused startups accelerate their go-to-market journey through cloud integration?

AceCloud has significantly contributed to AI-focused startups by providing cutting-edge cloud solutions to empower them to achieve their goals efficiently. Our expertise in GPU computing allows us to provide unparalleled support for resource-intensive applications, ensuring that customers can harness the full potential of their data.

One key example is a leader in advanced workforce training who was facing challenges in hosting AR/VR simulation environments. These simulations were integral for training workers in industries requiring precision and safety, such as manufacturing, healthcare, and construction. However, their reliance on legacy hardware severely hindered scalability, efficiency, and delivery timelines. The lack of automation and modern infrastructure often caused delays in launching simulation environments, slowing down their operations and reducing their competitive edge.

AceCloud partnered with the customer to address these issues by leveraging cutting-edge GPU compute over the cloud, offering a transformative solution that empowered them with scalability, automation, and real-time delivery of AR/VR experiences.

Key Features that were implemented:

Automated Workflow Pipeline: AceCloud designed and implemented an automated pipeline to ingest AR/VR data streams, process them using GPU compute, and deliver real-time simulations to end users. Automation eliminated manual intervention by 80%, reduced go live time by 60%.

Dynamic Scalability: The cloud infrastructure offered on-demand scalability, allowing the customer to dynamically allocate resources based on user demand, optimizing both performance and cost.

GPU-powered platform for cost efficiency: By moving from legacy hardware to AceCloud’s GPU-powered platform, the customer reduced capital expenditure and operational costs, paying only for the resources they consumed.

Another startup working at the forefront of AI innovation partnered with AceCloud to overcome the complexities of building and deploying Large Language Models (LLMs) from scratch for advanced applications such as text-to-code generation and text-to-image generation. Their challenges included training and running LLMs from scratch. In order to address this, they required sustained access to high-performance GPU resources, resulting in substantial capital expenditure given the usage of conventional solutions. Additionally, the traditional cloud block storage services were not designed for the I/O demands of large-scale training datasets, causing latency and throughput issues that hampered model development.

AceCloud’s team collaborated closely with the customer to design a performance-driven, cost-effective workflow that supported their end-to-end MLOps lifecycle.

Key features that were implemented: 

The customer was able to train LLMs on high vRAM GPU instances such as 80 GB and serve the same models using lower vRAM GPU instances 48 GB or 24 GB. This flexible compute model ensured efficient training while reducing 75% of their inference costs.

Custom-engineered block storage volumes were provisioned specifically for large AI/ML workloads. These volumes were enhanced with caching, parallelism, and bandwidth tuning to support high-performance data access, which is essential for LLM training.

AceCloud’s DevOps and Platform teams ensured smooth deployment, scalability, and real-time tracking across compute and storage resources. The team provided proactive monitoring and personalized support to maintain peak workload performance.

As enterprise investment in generative AI grows, how do you foresee the role of Indian startups evolving in enabling secure and scalable AI innovation?

As investment in generative AI accelerates, Indian startups are poised to play a pivotal role in enabling secure and scalable AI innovation. With DeepTech startups receiving $1.6 billion in funding in 2024—87% of it AI-led—India’s startup ecosystem is clearly shifting toward foundational tech development. These startups are increasingly focused on enterprise-grade AI solutions, aligning with the growing demand for secure, compliant, and scalable technologies. The surge in corporate M&A, IPOs, and early-stage funding reflects renewed confidence in their capabilities. Startups are also strengthening their fundamentals, with rising revenues and profitability, making them strategic partners for enterprises. Moreover, with 44% of new startups coming from Tier 2 and 3 cities, innovation is becoming more democratized. As challenges like talent availability and regulatory clarity are addressed, Indian startups will be instrumental in developing trustworthy AI frameworks, advancing responsible innovation, and contributing to the global AI value chain from both technological and ethical standpoints.

What emerging trends do you anticipate shaping the startup tech landscape in the near future?

The startup tech landscape is entering a transformative era, driven by the rapid adoption of artificial intelligence and the need for resilience amid global disruptions. In 2025 and beyond, we’ll witness a surge of vertical or industry-specific AI delivering domain‑specific solutions—from predictive diagnostics in healthcare to precision agriculture systems—alongside infrastructure builders creating no‑code platforms, middleware, and optimization frameworks that democratize AI development. Fuelling this momentum is record early‑stage funding, as global hubs pour capital into edge AI, multimodal learning, and quantum machine‑learning breakthroughs.

At the same time, hyper‑personalization will redefine customer engagement. AI‑driven platforms will anticipate individual needs in real time. Logistics and manufacturing startups will leverage AI and IoT to build autonomous supply chains and smart factories and climate‑tech ventures will spearhead clean‑energy innovations, circular‑economy models, and transparent, blockchain‑backed sourcing.

Success will depend on navigating regulatory complexity and embedding ethical, privacy, and sustainability practices at every level, ensuring that startups not only innovate but also uplift society.