AI cost routing for production pipelines

Your OpenAI bill is
still running gpt-4o
on simple prompts.

ConductorOS routes each request to the cheapest model that can handle it — no tokens spent deciding, no code changes required. Your $40k/month bill becomes $6k.

Before routing
gpt-4o
$15.00/1M tokens
500-token request: $0.01250
After ConductorOS
gpt-4o-mini
$0.75/1M tokens
Same 500-token request: $0.00075
93% cheaper on simple requests
0 token cost for routing
$34k monthly savings on a $40k bill
Try the demo See how it works →
The Problem

Your cheapest model is
still 20x more expensive
than it needs to be.

Model Use case Cost / 1M input tokens Your bill, 500k req/mo
gpt-4o Everything — including FAQs, confirmations, simple lookups $15.00 $37,500
gpt-4o-mini Simple prompts, classification, extraction, short replies $0.75 $1,875
gpt-4o Complex reasoning, code generation, multi-step analysis $15.00 $2,500
Typical split: 70% of your requests can use gpt-4o-mini. That's $24,375/mo going straight to the bottom line.

Routing overhead

Classifier models spend tokens deciding what runs next — before the first real answer. On 500k requests, that overhead is real money.

Opaque workflows

You can’t see where a message goes until after it goes there. Debugging means watching logs and guessing, not reading a workflow definition.

No version control

When routing logic lives in code, it changes silently between deploys. Rollbacks mean git archaeology and fragile migration scripts.

Scale hides the math

At 1M requests/month, a $0.005/request overcharge is $5,000/mo — invisible until you're already paying it.

How It Works

Route once. Define forever.

ConductorOS uses declarative routing — the workflow topology is fixed at definition time, not discovered at runtime. No LLM in the orchestration loop. No tokens spent deciding what runs next.

01

Declare your routing map

A YAML file defines your agents, their models, costs, and conditions. Source-controlled. Diff-able. Deployed like any other config.

agents:
  - name: fast-tier
    model: gpt-4o-mini      # $0.75 / 1M tokens
    capabilities: [qa, classification, extraction]
  - name: full-tier
    model: gpt-4o           # $15.00 / 1M tokens
    capabilities: [reasoning, code, analysis]

routing:
  rules:
    - condition: "tokens < 512 && !complex"
      route_to: fast-tier
    - condition: "analyze || debug || design"
      route_to: full-tier
  default_agent: fast-tier
02

Inspect before you run

See the full routing graph before anything executes. Every decision path is visible in advance — which model handles which input, how conditions branch, where human gates sit.

prompt
→
fast-tier
gpt-4o-mini · $0.75/M
→
response
<512 tokens
+ !complex
else
full-tier
gpt-4o · $15/M
03

Execute. No tokens burned on routing.

Routing decisions take under 1ms — pure code, no model call. Your prompts go to the right model, every time.

93%
cheaper on 70% of requests that are simple prompts
<1ms
routing decision latency — pure code, no model call
Example: 500k requests/month, avg 1,000 tokens
All gpt-4o (no routing) $40,000
With ConductorOS (70% fast-tier) $6,875
Monthly savings $33,125
Architecture
API
Your App
CLI
SDK
ConductorOS
Routing Engine
zero token overhead
Models
SLM
fast · cheap
LLM
capable · precise
Reasoner
chain-of-thought

Built for production.
Visible before deployment.

ConductorOS separates orchestration from execution. The routing graph is a JSON/YAML file — deterministic, auditable, source-controlled. Your workers are plain code in any language. No SDK ritual, no determinism constraints.

  • Source-controlled workflows you can diff and revert
  • Human oversight as a first-class workflow step
  • Retry logic, timeouts, compensating tasks
  • OpenAI-compatible API — drop in anywhere
Features

Everything you need.
Nothing you don’t.

Model cost comparison (input tokens)
Model
Use case
Per 1M tokens
500k req/mo*
gpt-4o
Everything
$15.00
$40,000
gpt-4o-mini
Simple prompts
$0.75
$2,000
gpt-4o
Complex tasks
$15.00
$5,000
* Assuming 1,000 input tokens per request. ConductorOS routes 70% to gpt-4o-mini automatically.

Declarative Routing

YAML-defined workflow topology. Fixed at definition, not discovered at runtime. Route decisions cost zero tokens.

Sub-second Latency

Routing decisions resolve in under 1ms. No classifier model to invoke. Your latency budget stays intact.

Multi-Provider Support

OpenAI-compatible endpoint. Swap providers without rewriting workflows. Per-agent model overrides.

Human-in-the-Loop

Approval gates and review checkpoints built into the workflow graph. Not bolted on later — part of the declaration.

Durable Execution

Every task executes to completion, or every failure is explicitly handled. State persists. Restarts pick up where you left off.

Any Language Workers

Workers in Python, Go, JavaScript, Java, C#, Ruby, or Rust. No framework rules. Plain code, any library.

Routing is infrastructure, not intelligence. The moment your orchestration layer starts making decisions for you — instead of executing decisions you’ve already made — you’ve built a second AI system inside your AI system. You pay for it twice.

ConductorOS is the operating system for AI cost optimization. Define your routing once. Run it forever. Pay only for the work that matters.

Try the /conductor demo