Implement parallel provider benchmarking with latency and cost tracking — lab audio overview

Host: Welcome back to AI Solution Delivery. You're in the chapter on AI Use Case Discovery and Data Readiness Assessment — the discipline of figuring out, before you write a single line of production code, whether a use case is actually a good fit for generative AI. This matters because most AI projects don't fail in production. They fail at the discovery stage, when nobody asked the right questions up front. Expert: Right. So picture this. You're on a delivery team at a mid-sized consulting firm. Maybe fifteen engineers. A new client walks in — a logistics company — and says, "We want to use AI to summarize shipping documents." Great. But which AI? There are three serious contenders right now: OpenAI, which makes the well-known models behind ChatGPT; Google's Gemini family; and Anthropic's Claude models. Each one has different speeds, different prices, and different strengths. Your client is going to ask, on day one, "Which provider should we use, and what will it cost us per month?" If you can't answer that with real numbers — actual measurements from actual test runs — you're guessing. And guessing on pricing in front of a client is how deals get lost, or worse, how you sign up for a project that loses money. That's the gap this exercise fills. Host: So how does this build on what you did before? And what exactly are we building in this second exercise? Expert: In the previous exercise, you set up a single, unified way to talk to all three of those AI providers. You used an open-source tool called LiteLLM — think of it as a universal translator that lets your code send the same request to OpenAI, Google, or Anthropic without rewriting anything. You also set up a proxy, which is just a middleman server that forwards your requests and keeps your API keys safe in one place. Now in this exercise, you're going to build on that foundation to create a benchmarking engine. A benchmark, in this context, is a fair head-to-head test — you give every provider the exact same prompt, then you measure how they each perform. Your engine will send that identical prompt to all three providers at the same time — in parallel, meaning simultaneously rather than one after another — and for each response, it'll record three things. First, how long the response took, which we call latency. Second, how many tokens were used — a token is roughly a chunk of a word, and it's the unit that AI providers bill by. And third, the estimated cost in dollars, based on each provider's published per-token pricing. Finally, it'll bundle all of those measurements into a clean, structured report. Host: Let's pause on the key idea here. What's the conceptual shift the listener needs to have in their head before they sit down to code? Expert: The big shift is this: stop thinking sequentially, start thinking in parallel. If you send your prompt to OpenAI, wait for the answer, then send to Gemini, wait, then send to Anthropic — your total time is the sum of all three. But if you fire all three off at the same moment and wait for all of them together, your total time is just the slowest one. For a benchmarking tool, that's a huge deal, because you might be running hundreds of test prompts during a client workshop. The second piece of the key idea is structured reports. Instead of printing raw numbers to the screen, you're going to define a strict shape for your results — what fields exist, what their types are, what's required. You'll use a Python library called Pydantic for this. Pydantic is a tool that lets you describe the shape of your data once, and then it validates and enforces that shape automatically. Why does this matter? Because the next exercise is going to build an API on top of this, and that API needs predictable, trustworthy data coming out of your benchmark engine. Host: Before people start — what's the tricky part? Where do people typically get stuck? Expert: The trap is error handling in parallel calls. When you fire three requests at once, any one of them can fail — a provider might be down, a rate limit might hit, a network blip might time out one call. If you don't handle that carefully, a single failure in one provider crashes your whole benchmark, and you lose the data from the other two that succeeded. So here's the tip: treat each provider call as independent. If one fails, capture the failure in your report as a failed result for that provider, but let the others complete normally. Your report should always come back with an entry for every provider — some successful with latency and cost, some marked as failed with an error reason. Don't let one bad call poison the whole run. Host: And to close us out — what will the listener be able to do after this, and where does it lead? Expert: After this exercise, you'll be able to take any prompt, run it against multiple AI providers simultaneously, and produce a clean report showing exactly how fast each one responded, how many tokens it used, and what it cost. That's a building block your team can drop directly into client discovery workshops — instead of saying "OpenAI is probably faster," you'll show a table with real milliseconds and real dollar amounts. Next, in the third and final exercise of this sequence, you'll wrap this benchmarking engine inside a web API so that a client-facing tool can submit a use case description and get back a provider recommendation automatically. Thanks for listening, and good luck with the build.