If vLLM already solved LLM serving, why did SGLang appear?
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AI Inference Market in India: The Capital, the Gaps, and What Comes Next
The real bottleneck in India’s AI story isn’t model development. It’s inference – the continuous, compounding cost of running AI at scale once the models are built. We unpack what happens when models run at scale, across a billion users, in dozens of languages, every single day.
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Stop Thinking in Models. Start Thinking in Stacks.
The AI stack comprises multiple layers: infrastructure, data handling, models, orchestration, inference, and governance. Efficient integration of these layers is crucial for performance, cost management, and reliability in real-world applications.
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The Infrastructure Debt Every AI Team Eventually Pays
AI deployment challenges shift from model development to infrastructure management at scale, affecting latency, costs, and reliability. Dedicated environments ensure consistent performance and protect proprietary models.
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The Infrastructure Gap Stalling BFSI
Fraud detection at most banks still happens after the transaction completes. Not because the models are slow. It’s because the infrastructure running those models can’t respond fast enough to catch fraud while the money is still moving.
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The Data You Ignore is the Data That Costs You the Most
Most modern systems are designed around structured data because it is easy to work with. Clicks can be counted, conversion rates can be calculated, and funnels can be optimized. These signals are clean, predictable, and easy to plug into decision-making frameworks. But structured data only tells you what happened and not why.
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Top 10 GPU Cloud Providers in India
Comparing providers only on hardware specifications misses these realities. This guide looks at the Top 10 GPU Cloud Providers in India with that context in mind. The focus is on how these platforms behave when workloads are real, continuous, and growing.
