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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesDoorDash’s GenAI platform story is less about choosing a single model or building a universal chatbot than about making useful AI workflows practical for teams across the company. In a QCon AI Boston 2026 presentation, Swaroop Chitlur and Siddharth Kodwani described how their platform evolved from a blank slate in 2023 toward shared services for model access, batch inference, agent workflows and common development patterns.
What the DoorDash presentation covers
Swaroop Chitlur and Siddharth Kodwani delivered “Building GenAI Platform at DoorDash” at QCon AI Boston on June 2, 2026, at 10:20 a.m. EDT, according to the QCon session listing. QCon frames the challenge as moving from a GenAI demo to a production product: teams need more than model access, including routing, tools, identity, evaluation, observability, cost attribution, governance and optimization.
The presenters describe starting with a new GenAI Platform team in 2023. Their initial principles were to stay close to customer teams and use cases, build complete workflows rather than isolated infrastructure, make good practices easy, and demonstrate value. The case study is useful because it shows those principles being revised as the intended users and workloads changed, not because it provides a universally applicable blueprint.
Why the platform shifted toward product teams
The speakers say the platform’s customers expanded from ML engineers to engineers across DoorDash. That change led them to make APIs and SDKs the primary interface rather than centering the experience on notebooks or direct infrastructure access. The goal was to give more teams a usable path from an idea to a working product feature.
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They also chose to prioritize business-impacting product use cases over building another general-purpose chatbot. The presentation groups early opportunities around automation, recommendations and personalization. Its value proposition is a practical product trade-off: help teams improve accuracy while managing latency and cost.
This orientation affects what a platform team should optimize. A technically sophisticated model integration is not enough if product teams cannot onboard, ship and operate it. Conversely, an abstraction that hides all model differences may become a constraint when providers impose quotas, deprecate models or offer different cost and performance characteristics.
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The platform components described at QCon
QCon’s session description identifies four components in DoorDash’s platform. The descriptions below reflect the conference materials; they do not disclose a complete implementation design.
| Component | Role described by QCon | Why it matters to teams |
|---|---|---|
| LLM Gateway | Request routing, observability and fallback handling | Provides a shared place to manage model calls and see how requests behave. |
| Batch Inference platform | Batch inference | Supports workloads that can be processed in batches rather than requiring an interactive response for every request. |
| Agentic Gateway | Multi-step LLM workflows | Provides platform support for workflows that require more than a single model call. |
| ADK templates | Templates for common patterns | Give teams a starting point for recurring development patterns rather than requiring every team to scaffold them from scratch. |
QCon also identifies issues the session addresses, including provider rate limits, cost attribution, prompt caching, scheduling against cost and service-level requirements, streaming protocols such as MCP, authentication, state management and scaffolding. The session description establishes these as design concerns; it does not publish a vendor-by-vendor scorecard or enough detail to reproduce DoorDash’s architecture.
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How model strategy changed as usage grew
The speakers describe beginning with a vendor-first approach to models. As use grew, cost, provider quotas and model deprecations made portability more important. In this context, portability is not an abstract preference: it can help a platform respond when a provider’s limits or model lineup no longer fit a workload.
Centralizing model access can help teams handle routing and shared operational concerns, but every shared layer brings maintenance obligations. A platform team must keep abstractions useful as providers and product requirements change. Chitlur’s closing advice, as reproduced in the presentation transcript, captures this tension: “The worst thing you can do now is make a decision and not reevaluate it.” The lesson is to revisit architecture choices as evidence changes, rather than assuming that the first successful integration will remain the right one.
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From agent experiments to shared support
The presentation describes a similar progression for agents. The team watched product groups experiment, supported MCP servers, and then broadened its direction toward an agent gateway and support for multiple protocols and agent experiences. That progression suggests a useful sequencing principle: observe real product-team needs before standardizing a shared interface.
Standardization can reduce duplicated scaffolding and make identity, state management, streaming and operations more consistent. But supporting multiple protocols also increases complexity. The QCon session frames the wider decision as determining what product teams should own, what should be centralized, and when to buy versus build; the right boundary depends on actual team needs and the ongoing cost of maintaining shared infrastructure.
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What adoption numbers the speakers reported
In the 2026 presentation transcript, Chitlur and Kodwani reported more than 5,000 internal users, 45 new users onboarding each day and 40% of users being non-engineers. They also recalled more than 25 agent projects in 2025. These are figures reported by the presenters, not independently audited statistics; the official QCon session page supports the talk’s framing and component list but does not independently confirm adoption metrics.
The numbers are relevant to the platform’s intended reach: the team describes serving a population broader than specialist ML engineers. They should not be treated as a current dashboard or projected forward beyond the timeframes given in the presentation.
Practical lessons for teams building an internal GenAI platform
- Start with users and use cases. Chitlur’s advice is direct: “Be customer obsessed. Focus on the teams and the use cases.” The platform’s interface and priorities should follow the work teams need to deliver.
- Optimize for a complete workflow. Model access is only one part of production use. Routing, fallback behavior, observability, identity, evaluation and cost visibility can determine whether a prototype can be operated as a product.
- Make the common path easier. APIs, SDKs and templates can help teams adopt good practices without requiring deep access to infrastructure or duplicated setup.
- Track several outcomes together. Accuracy, latency and cost are competing dimensions; optimizing one while ignoring the others can undermine the product goal.
- Build abstractions in response to real change. Provider limits, deprecations and new agent protocols can justify portability or shared gateway capabilities, but abstractions also create maintenance work.
- Reevaluate ownership boundaries. Decide deliberately which concerns belong in a shared platform and which are better left with product teams, then revisit that split as usage and requirements evolve.
What this case study does not establish
The conference materials and reproduced transcript offer a view into the presenters’ strategy and reported adoption, not a full technical design document. They do not provide benchmark results, independently verified usage metrics, or a comparative evaluation of providers and architectures. The case study is best read as an account of changing platform priorities and trade-offs, not proof that one specific stack or gateway design is right for every organization.
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