Reduce AI Costs Without Changing Your Code

You can lower your LLM bill by changing one line of configuration — point your existing OpenAI-compatible client at NemoRouter, and each request is sent to the cheapest model that still meets your quality bar.

No SDK migration, no prompt rewrites, no second integration. If your code already calls the OpenAI API, it already speaks NemoRouter. The savings come from where requests land, not from changing how you write them.

The problem this solves

Most teams overpay for AI in the same quiet way: every call goes to one premium frontier model, including the calls that never needed it. Classifying a support ticket, extracting a date, reformatting JSON, drafting a first pass — these are cheap tasks billed at flagship prices.

The fix sounds simple — "use a smaller model for the easy stuff" — but in practice it means juggling several provider SDKs, several API keys, and per-model if statements scattered across your codebase. That work rarely gets prioritized, so the premium-everything default sticks. The result is a bill that grows linearly with traffic and a finance team that can't tell you which feature is responsible.

How it works

NemoRouter sits in front of every provider behind one OpenAI-compatible endpoint. You send a normal chat-completions request; a cost-optimized route picks the least expensive model that satisfies the request, and the answer comes back in the standard response shape. Two response headers tell you exactly what happened.

Because the contract is identical to the API you already use, "adopting" NemoRouter is a configuration change. You keep your streaming logic, your retries, your tool-calling code. What changes is that a routine request stops paying frontier prices by default.

A working example

Swap the base URL, keep everything else:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.nemorouter.com/v1",
    api_key=key,
)

resp = client.chat.completions.create(
    model="nemo/cost-optimized",          # route, not a single hard-coded model
    messages=[{"role": "user", "content": prompt}],
)

print(resp.choices[0].message.content)
# Which model actually served it, and what it cost:
print(resp.headers["x-nemo-routed-model"])
print(resp.headers["x-nemo-request-cost"])

That x-nemo-request-cost header is the part finance has been missing: a per-request dollar figure you can log, sum by feature, and watch fall as routing does its job.

The results

A worked example. Say you make 1,000,000 calls/month and today route 100% to a frontier model at an effective $9.00 per 1K calls — about $9,000/month. If 70% of those calls are routine and route to a capable smaller model at ~$1.20 per 1K calls, while 30% stay on the frontier model:

That's a ~61% reduction in this mix — and your real figure depends entirely on how many of your calls are routine. The point is you can see it move, per request, without touching application logic. (Illustrative rates; verify current model pricing for your providers. Verified June 2026.)

Two more levers compound on top, still with no code changes: prepay tiers lower the platform fee (0% annual / 2% monthly / 4% pay-as-you-go — and every feature is included at every tier, never gated), and consolidating providers behind one key means one bill instead of reconciling several. (AWS Bedrock is shipping next, joining the providers already available behind the same endpoint.)

Summary

The cheapest LLM call is the one you didn't overpay for — and you don't have to rebuild anything to stop overpaying. Point your existing OpenAI-compatible client at NemoRouter, route routine work to cheaper models automatically, and read the cost of every request from a header so the savings are measured, not promised. If you want to stack routing with prepay and provider reservations for even deeper cuts, see how we cut LLM costs 60%.