How Intelligent Routing Cuts LLM Costs by 60%

When teams integrate LLM APIs, they almost always hardcode a single model. Usually GPT-5.5 or Claude Opus 4.7. It works, the output quality is solid, and nobody thinks twice about it until the bill shows up.

Here's the thing though. About 70% of production LLM requests don't actually need that level of capability. Classification, simple extraction, summarization, template-based generation. These all run perfectly fine on models that cost 10 to 20x less.

Let's do the math without pretending provider prices stay still. If most of your traffic goes to a flagship model, your bill scales fast. Move the simple 70% of requests to efficient models and the cost curve changes immediately, usually without users noticing anything different.

Prismo sits between your app and the LLM provider as a drop-in proxy. You change one line, your base URL, and every request flows through our routing layer before it hits the model API.

For each request that comes in, Prismo looks at the prompt complexity, what capability tier it needs, and your routing policy. Short prompts and classification tasks get sent to smaller, cheaper models. Multi-step reasoning, code generation, and complex analysis stays on the frontier model.

The whole routing decision takes under 15ms. That's basically nothing compared to how long the model itself takes to respond. And your app gets the same response format back no matter which model handled the request.

We've been looking at the data across teams running Prismo in production and the patterns are pretty consistent. Customer support bots, which tend to be the highest volume LLM workload, route 75 to 85% of requests to smaller models. End users can't tell the difference.

Code generation workloads are more conservative. Around 40 to 50% gets routed to smaller models because the correctness bar is higher. But even at that rate, teams are saving 30 to 40% on those workloads.

Blended across everything, the average savings land between 55 and 65%. For a team spending $3,000 a month on LLM APIs, that's somewhere around $1,650 to $1,950 back. Every single month. And it compounds as usage grows.

You totally could. But quality tanks on the 30% of requests that genuinely need frontier capability. Users notice when complex queries come back worse, code breaks more often, and the time your team spends debugging bad outputs ends up costing more than whatever you saved on the model.

Routing is the middle ground. Cheap where it doesn't matter, expensive only where it actually does. And Prismo makes that call per request, not per feature, so you don't have to manually categorize all your traffic.

Prismo supports OpenAI and Anthropic today through a drop-in proxy. If your code uses the OpenAI SDK, you change the base_url to api.getprismo.dev/v1 and add your Prismo API key. If you use Anthropic, you route those requests through Prismo the same way. No prompt rewrite, no new workflow, nothing to deploy.

Google Gemini support is coming soon. The goal is one endpoint for your model providers, with routing handled automatically based on whatever policy you set.