The Hidden Infrastructure Powering Scalable Enterprise AI: Tesseract Talks with Abhishek Chaudhary

In the latest episode of Tesseract Talks, host Oliver Rochford sat down with Abhishek Choudhary, Co-Founder and CTO of TrueFoundry, to unpack one of the biggest challenges facing enterprises today: how to architect AI systems that are both cutting-edge and secure without collapsing under operational complexity.

From model sprawl and agent frameworks to governance and cost control, the conversation made one thing clear: enterprise AI is no longer just about choosing the best model. It is about building the right infrastructure around it so that experimentation can turn into reliable, scalable production systems.

Here are the key themes and insights from the discussion.

The Reality of Building AI in a Rapidly Moving Ecosystem

Enterprises are operating in an AI environment that is changing at an unprecedented pace. New models and providers appear constantly, and each comes with different strengths, weaknesses, and APIs. At the same time, protocols like MCP (Model Context Protocol) are still evolving, while agent frameworks such as LangGraph, Google ADK, AWS frameworks, and others continue to multiply.

Before teams even begin building meaningful applications, they must make foundational decisions about which models to use, how to manage prompts and versions, which frameworks to standardize on, and how to eventually deploy and scale what they build. And just as teams start getting comfortable with one stack, the ecosystem shifts again, with voice agents and multimodal systems introducing entirely new technical requirements.

Why Most AI Projects Struggle in Production

As Abhishek explains, “building a demo that works for 80% of the cases is really easy. The problem is when you start scaling it out.” Once real users interact with systems in unpredictable ways, edge cases, failures, and reliability gaps quickly surface. Once systems are exposed to real customers, unexpected prompts and edge cases quickly surface gaps in reliability.

Another major bottleneck is model availability and performance. “If the model provider is down, your application goes down,” Abhishek noted. Even leading model providers experience outages, slowdowns, and regional disruptions. When an application depends directly on a single external model endpoint, any instability immediately becomes customer-facing downtime, which can damage trust in the product.

Cost is the third critical factor. Unlike traditional software, AI systems carry continuous inference costs that scale directly with usage. Several enterprises initially adopt closed-source hosted models for speed, only to later realize that token costs make their use cases economically unsustainable. In response, some organizations invest in their own GPU infrastructure and fine-tune smaller open-source models, trading short-term convenience for long-term cost control and predictable ROI.

Why AI Gateways Are Becoming Core Infrastructure

A year ago, few teams talked about AI gateways as a distinct architectural component. Today, they are quickly becoming standard practice for any organization serious about running AI in production.

According to Abhishek, AI gateways emerged to solve three fundamental enterprise problems:

• API standardization: An AI gateway abstracts differences in interfaces of model providers, allowing teams to switch or route across models without rewriting application code.
• Security and key management: With an AI gateway, developers authenticate against internal systems while provider credentials remain centrally managed, rotated, and protected.
• Governance and observability: Guardrails, budget limits, audit logs, and compliance checks can all be enforced consistently, rather than relying on each application team to implement best practices on their own. In some cases, Abhishek noted, once agents are validated, “going to production is literally one click.”

Why AI Gateways Are Not Just API Gateways

Although the term “gateway” may sound familiar, AI gateways differ significantly from traditional API gateways. Conventional gateways were designed around short-lived request–response patterns and simple authentication flows. They also measure usage in terms of requests, not the token-based economics that drive AI costs.

AI workloads are fundamentally different. Responses are often streamed, interactions can be long-running, and voice-based systems introduce persistent connections and real-time constraints. In addition, many AI-related risks are semantic rather than syntactic, meaning that policy enforcement must operate at the level of meaning, not just keywords or schemas.

While it is technically possible to extend existing API gateways to support AI use cases, purpose-built AI gateways are designed from the ground up to handle these patterns natively.

The Future: AI Gateways as Enterprise AI Orchestrators

Looking ahead, the role of the AI gateway is likely to expand well beyond request routing. Abhishek described a future where the gateway becomes a central registry for models, tools, MCP servers, and even agents themselves.

In such an environment, enterprise systems like Slack, GitHub, Confluence, and internal databases could all be exposed as discoverable AI services. When users ask complex business questions, the gateway could dynamically orchestrate multiple agents and tools to assemble answers, rather than relying on single-purpose applications.

Instead of building isolated AI features, organizations would compose intelligent workflows from reusable components. This approach mirrors how modern software platforms evolved, shifting from monolithic applications to ecosystems of interoperable services.

A dedicated AI gateway like TrueFoundry provides the foundation needed to move beyond experimentation. It enables consistent governance, reliable routing, cost controls, and deep observability across the entire AI stack. More importantly, it allows organizations to scale innovation without sacrificing security or compliance.

Watch the previous episode of Tesseract Talks with Nikunj Bajaj here Turning AI Chaos into Control: A Conversation on Agentic AI with Tesseract Talks