The Real Cost of Running AI Agents 24/7

People ask me what it costs to run AI agents full-time. The answer ranges from “surprisingly cheap” to “shockingly expensive” depending on one variable: model selection discipline.

Cost Factors

I run agents through OpenClaw across personal productivity, content creation, monitoring, and development. The key cost factors:

Agent Type	Model	Relative Cost
CEO orchestrator	Opus	Highest — runs infrequently but processes the most complex tasks
Coding agents	Haiku/Sonnet	Moderate — depends on session length and complexity
Monitors	Haiku	Very low — simple checks, high frequency
Writers	Sonnet	Moderate — needs quality prose but limited runs
Research	Haiku	Low — pattern matching and extraction
Heartbeats/cron	Mixed	Very low — lightweight periodic checks

The 80/20 Rule of Agent Costs

80% of your spend comes from 20% of your agents. The CEO orchestrator and coding agents dominate costs because they process the most tokens.

The monitors? Almost free. A heartbeat agent that checks email every 30 minutes costs pennies. A cron job that scans Twitter once an hour — negligible.

The expensive operations:

Long coding sessions — building features requires many turns of context
Orchestrator overhead — the CEO reading/synthesizing sub-agent results
Memory loading — stuffing context windows with history
Retries — failed verification loops that require re-execution

Model Selection: The Biggest Lever

Smart model routing is the single biggest cost optimization. The rule is simple:

Opus:    Strategic decisions, complex reasoning    ($15/M in, $75/M out)
Sonnet:  Creative writing, nuanced analysis        ($3/M in, $15/M out)
Haiku:   Mechanical tasks, pattern matching         ($0.80/M in, $4/M out)

A coding agent doesn’t need Opus. It needs to follow instructions and write correct syntax — that’s Haiku territory. A content writer needs voice and nuance — Sonnet. Only the orchestrator needs the full reasoning power of Opus.

Optimization Strategies

1. Minimize Context Loading

Don’t pass your entire MEMORY.md to sub-agents. Give them only the context they need for their specific task. A 10K token context reduction across 100 daily sub-agent calls saves real money.

2. Set Aggressive Timeouts

A stuck agent spinning for 30 minutes burns 10x what the task should cost. Set timeouts: 15 min for coding, 5 min for research, 3 min for writing. Kill and retry.

3. Cache Expensive Results

If a research agent finds competitor data, write it to a file. Don’t re-research tomorrow. Memory files are free; API calls aren’t.

4. Batch Heartbeat Checks

Instead of separate cron jobs for email, calendar, and notifications, batch them into a single heartbeat that runs every 30 minutes. One API call instead of three.

5. Use Streaming for Long Tasks

Streaming responses let you detect failures early. If a coding agent starts generating nonsense at line 50, you can kill it immediately instead of waiting for 500 lines of garbage.

When It’s Worth It

Well-routed agents can replace significant administrative work at a fraction of the cost of human labor.

For context:

A virtual assistant costs $15-40/hour
Your own time (opportunity cost) is worth much more
The agents work 24/7, weekends included

The question isn’t whether agents are cost-effective. It’s whether you can afford not to use them.

The Cost Trajectory

AI model pricing drops rapidly. The architecture you build now — the memory systems, the orchestration patterns — will only get cheaper to run over time.

Build the system. The economics are already good and only getting better.

About the author: JD Davenport builds AI agent systems at OpenClaw. Follow on LinkedIn for updates on building AI agents for business.