Toto.
Route frontier. Build local.
Stop paying frontier prices for local work.
Toto routes each task to the cheapest capable model — frontier APIs when quality demands it, fine-tuned local models when it doesn't. We build the local models.
01 / 04 · San Francisco
Debug the risk-engine refresh path
deep reasoning · wide context
Summarize 200 counterparty emails
long context
Generate data-pipeline boilerplate
routine codegen
Classify every research note
domain-specific · high volume
Pre-screen comms for compliance
data never leaves perimeter
Toto Router
every decision logged, priced, auditable
Claude Opus$0.150/task
frontier reasoning · judgment-heavy work
Gemini 2.5 Pro$0.025/task
long-context summarization
DeepSeek V3$0.003/task
routine codegen at near-zero cost
Fine-tuned local≈$0.0004/task
your GPUs · your data never leaves
375× cheaper than frontier
Per run
Before $1.05
After $0.39
Saved $0.66 (63%)
Per year
Before $1.05M
After $390K
Saved $660K/yr (63%)
02 / 04
SSE · API · MCP · CLI Beta in production
03 / 04
04 / 04
FAQ · For teams cutting AI spend
Routing, local models, and your token bill.
What is an AI smart router?
An AI smart router sits between your tasks and the model market. It scores each incoming task and sends it to the cheapest model capable of doing the job — a frontier API for hard reasoning, a fine-tuned local model for routine patterns — instead of sending everything to one expensive model.
How much can routing cut our AI token spend?
Most teams send nearly every task to a frontier model by default and overpay for the routine ones. On our benchmark workload, Toto's routing cuts token cost about 63% with no loss in output quality — and the savings scale with task volume.
When does a task go to a local model instead of a frontier API?
When it's a pattern your workload repeats: classification, extraction, enrichment, templated drafting. Toto fine-tunes local models on those patterns. High-novelty or high-stakes tasks still escalate to frontier models.
Does Toto build the local models for us?
Yes. Toto builds, fine-tunes, evaluates, and maintains local models for your specific use cases from your task history. Your code and prompts never touch Toto's cloud — the models deploy where you control them.
How do we get started?
Toto is in private pilot. Drop your email on toto.tech or write to hello@toto.tech and we'll reach out.