Send every request to the model that fits the task best, without overpaying for simple work and compromising on complex-work quality.
Chosen by leading engineering teams
Smart LLM routing replaces a single default model – cutting your costs, reducing failed requests, and eliminating the engineering overhead of hand-built routing logic.
Many LLM workloads vary in complexity – some requests genuinely need a frontier model, and the rest can be handled fine on a lower-cost one. Smart routing draws that line automatically, so you stop paying premium prices for routine work.
Smart LLM routing directs requests based on model performance and availability. If a model slows down or becomes unavailable, traffic can shift to another option, helping maintain faster responses and fewer failed requests.
Connect to the NexEco AI Gateway and routing is handled for you. No custom logic to write, test, or keep in sync across multiple providers. As your model stack grows, Smart Routing scales with it automatically.
The routing system gets smarter over time, learning from new patterns and adapting as tasks, workloads, and model capabilities evolve. This helps keep routing decisions effective as new models emerge and performance changes, without constant manual tuning.
84%
of coding traffic can be routed to an open-weight models, while frontier models stay ready for the hard calls.
Routing on price alone creates trade-offs. A cheaper model can lose quality on complex tasks, and switching models unnecessarily breaks prompt caching, forcing the same context to be reprocessed from scratch.
Smart LLM routing accounts for the whole request – task complexity, model capability, cost, performance, and cache efficiency together, not cost in isolation.
Smart LLM routing is built directly into the NexEco AI AI Gateway, giving you 200+ large language models (LLMs) through one connection. It works with any coding agent or harness across completions, responses, and messages endpoints. No plugins, no separate router, no changes to your tooling.
01
Your request hits the gateway.
02
The router reads the query itself – metadata, tags, type, complexity, even intent and sentiment – to work out what the request actually needs.
03
It picks the best-fit LLM based on request structure, accuracy, latency, and cost.
04
If the selected model fails, times out, or comes back low-confidence, the router redirects to a backup model automatically – no dropped requests.
Here's where smart routing makes the biggest difference – the patterns teams put into production first.
Coding agents generate a huge request volume, ranging from routine lookups to complex reasoning. Smart routing matches each task to the right model tier – without the unnecessary model switching that can hurt prompt caching efficiency – choosing the most cost-optimized model for each request without sacrificing quality.
Support requests range from simple lookups to complex troubleshooting. Smart routing sends routine questions to fast, cost-efficient models and reserves stronger models for requests that need deeper reasoning. Response times drop on the easy questions, and quality holds where it matters.
A single AI workflow can involve many steps, from extracting data and calling tools to planning and complex reasoning. Smart routing matches each step with the right level of model capability, so you don't run the entire workflow on your most expensive model.
Apply routing rules to keep sensitive requests within approved models, providers, or regions, while everything else routes freely on cost, task complexity, quality, or speed.
AI requests are processed through EU-hosted infrastructure.
Supported models do not retain prompts or responses after delivery.
Switch in minutes with minimal code changes.
One provider goes down, traffic reroutes automatically.
Running every request through a single model is the simplest setup – but it means paying the same price whether the task needs it or not.
| NexEco AI Smart LLM routing | Single default model | |
|---|---|---|
| Model selection | Best-fit model selected per request | One model for every request |
| Cost efficiency | Reduced costs by using mix of the frontier and lower-cost models | Cost tide to selected model |
| Response quality | Quality matched to task complexity | Limited to capabilities of one model |
| Performance | Model matched by task complexity | Dependent on one model |
| Adapting to change | Adapts as tasks, workloads, and models evolve | Requires manual re-evaluation |