Can Indian Startups Win The AI Inference Race?

Nations worldwide have spent the last few years obsessing over bigger models and the compute power needed to train them. However, as models move into everyday products, it is becoming difficult to run AI models faster, cheaper and reliably at scale.

This is where inference platforms are emerging as saviours. Now, before we dive into the topic, let’s first understand what inference actually means.

Simply put, inference is the process through which a trained AI model uses what it has learned to respond to new inputs, such as questions, images or instructions.

For context, when you ask an AI model to summarise a health report or a business document, it uses its existing capabilities to process your request and generate a response. But achieving this is not as simple as it reads. 

Now, the challenge is that every interaction with AI requires computing resources, whose costs can quickly add up as millions of users interact with AI-powered products every day. This is where inference platforms have found their niche. These platforms deploy, manage and optimise AI models without having to build and maintain the entire infrastructure themselves.

In India, companies such as Simplismart, Pipeshift and E2E Networks are building different layers of the inference stack, betting that businesses will increasingly pay for faster, cheaper and more reliable AI.

But with global cloud giants and AI providers competing for a share of this growing market, where do Indian startups stand? And what will it take for them to build sustainable businesses? Let’s explore in today’s edition of The AI Shift…

Why Inference Became A Business Problem

The need for inference platforms becomes clearer when we look at how businesses are using AI in their everyday operations. After integrating an AI model into their product, businesses want to make sure that it is delivering reliable results at the right speed and cost.

Take Apna, a jobs and professional networking platform and a customer of Indian inference startup Simplismart. The company uses AI for tasks such as improving candidate profiles and onboarding employers.

The challenge for Apna’s CTO, Abhishek Ranjan, is to make its AI faster, more efficient, and economically viable. Apna uses inference platforms to run smaller open-source and proprietary models for relatively simple tasks.

As the number of AI interactions grows, even small differences in cost and response time can add up across millions of requests. 

“But this does not mean businesses need to depend on external inference platforms for every task,” said Ranjan, who is also the CTO of Blue Machines AI, a subsidiary of Apna that has built its own inference platform for conversational AI applications.

Unlike some recruitment-related tasks, conversational AI needs much faster responses and must be able to handle many users at the same time. Even a small delay can affect the experience of someone interacting with a voice AI assistant.

This is why Blue Machines has built its own inference platform, allowing it greater control over speed and performance. Depending on the task, it can use advanced AI models, open-source models or models it has fine-tuned itself.

Apna uses an external inference platform when it wants flexibility across models without having to manage the entire serving infrastructure. Blue Machines, meanwhile, offers more specialised requirements around response times and concurrent workloads. 

Simplismart’s examples show how the problem changes by workload. 

The startup claims it has reduced inference costs by 53% while retaining 99.99% accuracy through model quantisation and latency optimisation for voice AI platform Sanas. 

When it comes to customer support platform Exotel, Simplismart claims to have reduced inference costs to ₹0.90–₹1.30 per minute from ₹2.10 for a closed-model API, while cutting latency from 1.1 seconds to under 300 milliseconds.

The common thread is that inference is not about finding the cheapest or most powerful model but finding the combination of model, hardware and serving configuration that fits the business. 

Inference Comes In Different Flavours

The term “inference platform” can sound like one product, but businesses can consume inference in very different ways. The choice depends on how much control they need, how much traffic they handle and how much of the infrastructure they want to manage themselves.

“Inference platform is too generic a term,” said Vishnu Subramanian, founder at JarvisLabs, an AI infrastructure company, acquired by E2E Networks. 

He pointed to four broad ways businesses can consume inference:

• Shared model APIs: Ready-to-use models accessed through an API, with the infrastructure managed by the provider.
• Managed inference: Dedicated model deployments where the provider handles much of the technical setup.
• Self-managed inference: Businesses bring their own models and decide how those models are deployed and scaled.
• Dedicated infrastructure: Reserved capacity for workloads that need predictable performance, latency or reliability.

The simplest option is a shared model API, according to Subramanian. JarvisLabs’ Token Factory, for instance, lets businesses access hosted open models through an API and pay for the tokens they consume. The GPU capacity is managed by the platform, allowing a company to start using a model without building its own serving infrastructure.

For businesses with larger or more specific workloads, managed inference offers more control without requiring them to manage every technical layer. 

Therefore, a startup experimenting with an open model may be comfortable with a shared API, while a high-volume voice application may need dedicated capacity. Similarly, a regulated enterprise may want greater control over its models, data and deployment environment.

Why Are Startups Betting On Inference?

It is a no-brainer that inference has created infrastructure opportunity, with startups building different layers of the inference stack rather than simply competing to offer more GPU capacity.

In India, Simplismart is one of the companies trying to capture this opportunity. The startup raised $14 Mn in Series B funding in October, with participation from NVIDIA, Accel, Shastra VC and Micromax. Its software helps application companies run AI within their cost, performance and deployment requirements, while also allowing cloud providers to turn GPU capacity into managed AI services.

Listed company E2E Networks is approaching inference through both managed and self-managed offerings, while startups like Pipeshift are focusing on production inference for open-source models, particularly workloads where latency matters. 

Pipeshift recently partnered with Neysa to bring its managed serving layer together with Neysa’s sovereign AI cloud for Indian businesses, targeting use cases such as voice agents, coding agents and enterprise copilots.

The same fragmentation is visible globally. YC-backed startups are building businesses around increasingly specific inference problems. 

Hopper, an AI inference platform for voice agents, for instance, focuses on optimising inference for real-time agents and post-training open models on production traces, while RouterPlus is building an inference marketplace that selects models and providers based on a company’s actual traffic, cost and capacity requirements. 

As of now, the inference opportunity is unlikely to settle into a single category. For Indian startups, it could mean opportunities across low-latency voice AI, open-model serving, sovereign inference, agent workloads and specialised enterprise deployments.

Therefore, the white space for the next big opportunity is right there — make that intelligence faster, cheaper and easier to scale. 

Top Stories From India & Around The World

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• Sarvam Enters The Decision Model Race: Sarvam AI has launched Prism, an open-weight decision model designed to make fast, structured decisions across enterprise workflows. The move puts the Indian AI startup alongside emerging players building specialised models for routing, security checks and agent evaluation.
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The Weekly Buzz: OpenAI Builds An Emergency Kill Switch 

OpenAI introduced a new internal safety system this week called Control Room. Think of it as a master emergency brake for every experimental AI project running inside the company.

As researchers test increasingly powerful models and let groups of AI agents work on their own for hours or days, the risk of something unexpected happening grows. 

Control Room keeps a constant watch on all of these experiments. The moment staff spot unusual or potentially dangerous behaviour, they can hit a single stop button and shut the entire job down in roughly 35 seconds.

This is not a public product for everyday users. It is an internal tool designed to give OpenAI tighter real-time control over its own most advanced research. In an era when AI systems are becoming more independent and capable of long stretches of unsupervised work, even the labs building them need reliable ways to stay in charge.

Control Room is OpenAI’s latest answer to that challenge — a fast, company-wide kill switch that keeps humans firmly in the driver’s seat.

Startup In The Spotlight: Vecton AI

Founded in 2025 by Himanshu Goyal and Gaurav Mandlecha, Bengaluru-based Vecton AI is building an AI transformation platform for banks, financial services and insurance companies, helping them move AI projects from experimentation to production.

Financial institutions handle large volumes of structured data and repetitive workflows, but deploying AI at scale can be difficult because of regulatory requirements, legacy systems and the cost of integrating new technology into existing infrastructure. Vecton AI aims to address these challenges with AI consulting and strategy, process automation, custom-trained models and autonomous agent development.

The startup uses a forward-deployed engineering model, with its teams working alongside financial institutions through implementation rather than limiting its role to consulting. It builds production-ready AI agents and machine learning workflows designed to meet BFSI regulatory requirements, with applications across sales acceleration, customer engagement, document processing and intelligent copilots.

Vecton AI is targeting India’s growing ‘AI in BFSI’ market, which is projected to reach $9.4 Bn by 2034. The startup’s focus is on helping financial institutions operationalise AI across high-volume processes while navigating the compliance and infrastructure constraints of the sector.

Prompt Of The Week

What prompts and hacks are CTOs, CEOs and cofounders using these days to streamline their work? 

Here’s Praveen Ojha, chief technologist at EPAM India, with a prompt he relies on to assess a client’s AI maturity before recommending an agentic AI modernisation roadmap.

“I am the EPAM India CTO, working across AI delivery, consulting and presales. I serve as a bridge between our global technology teams and the India tech center, and I lead a team of senior architects to support delivery and consulting.

For this, think like an AI modernisation expert.

Here is the problem statement: Enterprise clients want to adopt agentic AI, but most don’t have a clear view of how ready they are. Before I recommend a modernisation roadmap, I need an honest assessment of the client’s AI maturity.

Here is the client as I see it: [client name, industry, geography, business goals].

These are the inputs I have: [process maps, data landscape, current tech stack, operating model, existing AI initiatives].

These are the rules the client must comply with: [data governance policies, regulations].

First, assess the client’s AI maturity across five areas: business process, data, technology, operating model and governance. Score each one and explain in detail.

Then recommend the agentic use cases that should go on the modernisation roadmap. For each one, name the human-oversight point, lay out the ROI logic and show how it aligns with the client’s governance and regulatory rules.

Next, separate what stays constant in every assessment from what changes for this client, and turn the constant parts into a reusable Skill with its own instructions, templates and checklists.

Finally, list the checks that should run automatically every time the Skill is used.”

Editor’s Note: Some prompts may need to be adjusted by users for best results or may not work as intended for certain users.

[Edited by: Shishir Parasher]
[Creatives by: Varshita Srivastava] 

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