OPEN SOURCE · pip install apeiros-sdk

AI costs aren't rising because models are expensive.
They're rising because no one is managing how compute is used.

Apeiros is the first system that governs compute usage across tasks in real time — based on economic value, not just execution.

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example.py

import apeiros

# Start a governed session with a budget cap
apeiros.start_session(model="claude-sonnet-4-6", budget=5.00)

# Wrap every model call — detectors run automatically
apeiros.wrap(model_call, input_tokens, output_tokens)
# → {"total_cost": 0.021,
#    "warnings": ["retry_loop_detected"],
#    "status": "degraded"}

What teams are experiencing

The cost problem is structural.

Costs that don't make sense

A task that cost $0.10 in testing costs $10 in production. The same workflow can vary from $0.01 to $1.00+ with no explanation.

Agents that spiral silently

Retry loops, context bloat, and tool amplification burn through budgets invisibly. You find out when the bill arrives.

Tools that look backwards

Workflow tools reduce waste inside a task. Finance tools explain cost after it happens. Nothing governs compute while it runs.

Why AI costs explode

The 5 failure modes of AI cost explosion

01

Over-provisioned intelligence

Agents use maximum reasoning and context even when unnecessary.

Simple tasks are solved like complex ones — cost doesn't match value.

02

Context compounding

Context grows with every step and is reprocessed repeatedly.

You pay for the same information again and again.

03

Retry loops and failure amplification

Failures trigger retries with the same or larger context.

The least valuable work often costs the most.

04

Tool amplification

Tool outputs are fed back into context, expanding cost recursively.

Each integration makes workflows disproportionately expensive.

05

No economic awareness

Agents execute everything with equal effort regardless of value.

No prioritization. No budget constraints. No cost vs value decision.

What Apeiros does

Governance at the point of execution.

Apeiros manages AI compute like an energy system. It continuously answers: how much compute should this use, when should it run, and what is it worth — and enforces those decisions in real time.

Real-time detection

Apeiros monitors every step of your agent workflow and flags waste as it happens — not after the bill arrives.

Enforcement, not just alerts

Set retry caps, budget limits, and cost thresholds. Apeiros enforces them inline, before damage is done.

Zero infrastructure

An SDK that wraps your existing model calls. No database, no storage, no pipeline changes. Works in a single session.

How it works

Three lines. Full governance.

01Wrap your session

import apeiros
apeiros.start_session(model="claude-sonnet-4-6",
  budget=5.00)

02Wrap your model calls

result = apeiros.wrap(
  model_call,
  input_tokens=1200,
  output_tokens=400
)

03Get real-time governance

{
  "step": 4,
  "total_cost": "$0.021",
  "warnings": [
    "⚠ Context size increased 2.7×",
    "⚠ Retry loop detected (3 attempts)"
  ],
  "recommendations": [
    "Clear context before next step",
    "Stop retries after next attempt"
  ],
  "status": "degraded"
}

Design partners

Built for teams shipping AI agents today

✓You're running LLM-powered agents in production or near-production
✓You've seen an unexpected token cost spike you couldn't explain
✓You want visibility and control before the next bill arrives

“Apeiros ensures every token spent is intentional, not accidental.”

AI costs aren't rising because models are expensive.They're rising because no one is managing how compute is used.