Frontier AI performance at a fraction of the cost.
Moji Router gives you full control over your inference cost and model performance as your agentic workloads and token usage scale.
It sits over the providers and infrastructure you already use, and routes every turn of long agentic tasks to the best model on cost and performance, cutting and controlling your bill inside a budget and quality floor you set.
A lower bill you set in advance.
The readout tracks quality against your floor every turn, so you see a dip before your users do.
Watch it route every turn across your providers.
Every turn is routed to the model and provider that answer it best for the least cost.
A thin layer you can put in the path.
- Failover when a provider degrades. If a provider slows or errors, the turn re-routes to another provider in your pool and the session carries on.
- A thin layer. The router's own overhead is reported alongside the saving, so you see what it adds in time as well as what it cuts in spend.
from openai import OpenAI
# point an existing client at the router:
# only the base URL and key change.
client = OpenAI(
base_url="https://api.mojirouter.com/v1",
api_key="MOJI_ROUTER_KEY",
)
resp = client.chat.completions.create(
model="auto", # the router picks, turn by turn
messages=messages,
extra_body={
"operating_point": "balanced",
"monthly_budget_usd": 20000,
"quality_floor": 0.9,
},
)Built for heavy AI workloads. Drops in as a layer.
Coding agents
loop over a codebase, resending files and history each turn.
Agentic SaaS
call a model at every stage, and the bill scales with usage.
Long-session assistants
carry a growing history that is resent on every reply.
Retrieval-heavy products
prepend retrieved documents to the prompt on each request.
- Point your calls at ita base-URL change with an OpenAI-compatible client, or the SDK. Your application code stays as it is.
- Run a plug-and-play pilotthe router routes your traffic and holds it to the budget and quality floor you set.
- A contract follows the pilotterms sized to your own AI workload, agreed once the pilot has run.
More than a router: the management layer for your inference.
Routing every turn across your providers is live today. Our research takes it further.
Session routing
Pricing the whole session to keep context warm across every turn and every provider.
Model training
We train models that scale intelligence continuously on demand. Turned down, the dial cuts the bill at equal quality; turned up, it buys more quality for the same spend.
Agent routing
As you run fleets of agents, the same layer routes each sub-task to the model that fits it, inside a cost envelope you set.
From the blog
Inference is the new cost of goods
When agents loop and sessions grow, the model API bill starts to behave like a cost of goods sold. Here is why the bill has that shape, and what a company can do to hold it down.
Read →One model per prompt no longer fits
Picking a model for a single prompt made sense when work was one message. Agentic work is a session, so you route the whole session and choose where it sits on the cost-quality frontier.
Read →The shape of the AI bill
Agents loop, assistants hold long conversations, and every turn resends the same context. Most of a modern AI bill is the same tokens, paid for again and again.
Read →See what you would save.
One workload and a base-URL change puts the router in your request path.