What is Moji?

Moji is the management layer for AI inference. It cuts and controls your API spend as agentic workloads and token usage grow. Most routers pick one model per prompt; Moji manages every turn of a long task, routing across the providers you already use, holding quality above a floor and spend inside a budget you set. Your application keeps talking to those providers, and the router decides where each turn lands.

What is AI inference management?

AI inference management is treating what you spend on model inference like a cost of goods you control: routing each turn of a session to the model and provider that answer it best for the least cost, holding spend inside a budget and quality above a floor you set. Moji is the management layer that does this, as a drop-in over the providers you already use.

Who is it for?

Companies whose product runs on inference, where the model bill behaves like cost of goods. That covers coding agents with long sessions, agentic SaaS where inference is a large share of operating cost, long-session assistants with growing context, and retrieval-heavy products that resend tools and documents every turn. If your traffic is multi-turn or agentic, the saving is easiest to justify.

How do I cut my AI inference costs?

Route each call to the model that wins on price and quality instead of sending everything to one expensive model. Moji does this across the providers you already use: it scores every turn by cost and quality, holds the budget and quality floor you set, and cuts the bill while holding the floor. The same routing is how you reduce AI costs across the board: agent costs, LLM API costs and long-session assistant costs all come down to sending each call to the right model.

Does it help if my cost is retrieved documents, not chat history?

Yes. The router prices and routes each turn by the size and shape of what you send, whether that is a growing conversation or a fresh set of retrieved documents each turn. Either way it routes the turn to the model that handles that payload best for the least cost, inside your budget and quality floor. Repeated context also costs less to send, because providers cache it at a fraction of the price.

What makes Moji different from a simple router or gateway?

Most routers pick one model for one prompt and stop there. Moji reads the whole session and routes every turn; holds a budget and a quality floor you set, so the bill is a figure you fix in advance; holds quality to the floor you set, so a dip shows up as a number before your users see it; and calibrates to your workload as it routes more of it. It runs as a drop-in layer over the providers you already use, so you keep your stack and gain the control.

What is the best AI router in 2026?

The best router for an AI product is the one that cuts the bill without a quality cost. That is how Moji is built: it routes every turn across the providers you already use, holds a budget and a quality floor you set, and keeps quality above that floor. The best choice is the one that does that for your product, whatever a leaderboard says.

Is Moji the cheapest AI router?

The goal is the lowest cost at the quality you need, not the lowest number on a page. Moji finds the most cost-effective path that still clears the quality floor you set, so you cut spend without the answers getting worse. You choose where to sit on the cost-quality frontier: the cheapest path that clears your floor, the most quality per dollar, or the highest quality inside your budget.

Does Moji do more than routing?

Routing is the first layer. Moji is the management layer for AI inference, built by a research team. On top of routing we train our own models that scale intelligence continuously on demand, a dial the router sets per turn; and the same engine grows into agent routing, matching each sub-task of an autonomous system to the right model inside a cost envelope. Everything runs on the same drop-in layer and the same budget and quality floor, so the layer that cuts your bill today is the one that manages your inference as it scales.

Why not build this in-house?

Many teams start to. Doing it well is an ongoing research problem: reading the whole session, staying cache-aware, holding a budget and a quality floor, and recalibrating as your traffic shifts, all without a quality cost. That is the problem we work on full time, measured against public benchmarks. You point one workload at the router and keep your engineers on your product.

Will it change my model outputs or quality?

Routing chooses where a turn runs; it does not change what the model says. We keep the frontier model on the turns that need it and move the ones that do not, holding quality inside a floor you set. The router only considers models that can hold the full context of a turn, so a large or document-heavy turn is never sent to a model too small to take it. The cost-quality frontier shows the trade, so you choose the operating point yourself.

How would I know if quality slipped?

You would see it in the readout before your users do. The router holds the quality floor you set and runs an automated evaluation alongside every turn it routes, so a dip shows up as a number in the readout rather than as user feedback. If a turn is hard enough that no option comfortably clears your floor, the router sends it to the highest-quality option available and flags it in the readout, so a borderline turn is visible to you rather than a surprise for your users.

Does switching providers mid-session lose context?

No. Moji holds the session, so when a turn routes to a different provider the full context goes with it. Routing across providers changes where a turn runs, not what the model can see, and streaming is preserved: the router streams tokens through as the chosen provider produces them.

What does integration look like, and do I need more than one provider?

You point your existing model calls at the router, which is a base-URL change with an OpenAI-compatible client, or you use the SDK. The router runs in front of the providers you already use, over your existing endpoints, and your application code stays as it is. A single-provider team still benefits, because the router works across the whole session and holds it inside a budget you fix in advance, and the saving grows once you route across more than one provider.

The router sits in the request path. What about latency and its own availability?

The routing decision runs in single-digit milliseconds and is dwarfed by model inference time, so the router adds little to the path. We measure its own overhead and report the added latency at p50 and p95, so you see what it costs in time as well as what it saves in spend. It is a thin layer designed to stay out of the way, and availability terms are set in your agreement.

What happens if a provider degrades or goes down?

The router watches latency and error-rate signals on each turn. If a provider returns an error, times out, or degrades against those signals, the router detects it within the turn and re-routes to another provider in your pool before returning, so a single bad turn is caught rather than only the next one being protected. You set which providers the router may use and the order it prefers them in.

How is my data handled, and how do you approach security?

Routed traffic is encrypted in transit. For the traffic we route you are the data controller and we act as your processor under a data processing agreement; for your account and contact data we are the controller. We do not train on your content and we do not sell it. Data residency and retention are written into the agreement. See the privacy policy for the detail.

How does pricing work?

Pricing is set per customer and agreed with you before anything starts. Every figure on this site is illustrative.

How do I get started?

Email [email protected] with a line about your workload, and we will set up a pilot on one workload with you.