27 AI Agents Cost Me $12 Per Run. Here's the Breakdown.

I built 27 AI marketing agents. People keep asking what it costs to run them. So I tracked a full run — all 27 agents, 9 dependency-aware batches, every token counted.

Total: $11.87 for one complete run. That covers strategy, competitor analysis, content creation, SEO, email sequences, ad copy, social media posts, and brand voice guidelines.

A marketing agency would charge $5,000-15,000/month for the same scope. I run this weekly.

The agents

27 agents across 9 categories, running in dependency-aware batches. The strategy agents run first (they produce research that other agents need), then content agents, then distribution agents.

Batch 1: Market Research, Competitor Analysis, Audience Profiler
Batch 2: Brand Strategist, Content Strategist, SEO Strategist
Batch 3: Blog Writer, LinkedIn Writer, Instagram Creator
Batch 4: Email Sequence Writer, Ad Copy Writer
Batch 5: Landing Page Writer, Sales Copy Writer
Batch 6: Community Manager, Partnership Scout
Batch 7: Analytics Reporter, A/B Test Designer
Batch 8: PR Writer, Event Planner, Podcast Planner
Batch 9: Brand Voice Guard, Content Auditor, Final Review

Each agent has its own prompt, output format, and quality checks. They read from a shared lib/common.sh framework that handles variable substitution (60+ project-specific values like company name, target audience, pricing, tone of voice).

Cost per agent

Here's what each agent costs on a typical run:

The blog writers cost the most per agent because they produce longer output. The strategy agents cost more on input because they read research from previous agents.

The full run numbers

Last complete run (March 6, 2026):

Total agents:     27/27 successful
Total output:     957 KB of content
Total input tokens:  ~89,000
Total output tokens: ~74,000
Total cost:       $11.87
Run time:         47 minutes (sequential batches)

I could cut the cost by switching strategy agents to a cheaper model. But the strategy layer is where quality matters most — a bad market analysis produces bad content downstream. So all 27 agents run on the same model.

What you get

After a full run, the output directory looks like this:

output/
├── strategy/
│   ├── market-research.md
│   ├── competitor-analysis.md
│   └── audience-profile.md
├── content/
│   ├── blog-post-1.md
│   ├── blog-post-2.md
│   ├── linkedin-posts.md
│   ├── instagram-captions.md
│   └── email-sequence.md
├── distribution/
│   ├── seo-recommendations.md
│   ├── ad-copy-variants.md
│   └── partnership-targets.md
└── review/
    ├── brand-voice-audit.md
    └── content-quality-report.md

Each file is ready to review and publish. Some need light editing (the LinkedIn posts usually need a personal touch), but the blog posts and email sequences are often publishable as-is after a quick read.

Versus an agency

I talked to three marketing agencies before building this. Their quotes:

• Agency A: $8,000/month for content + strategy (4 blog posts, 12 social posts, monthly report)
• Agency B: $5,500/month for content only (8 blog posts, 20 social posts)
• Agency C: $12,000/month for full-service (content + paid ads + SEO + analytics)

My system produces more content per run than Agency A delivers per month. At $12 per run, even running it weekly costs $48/month. Running it daily would cost $360/month — still 15x cheaper than the cheapest agency quote.

The trade-off: agencies bring relationships, industry connections, and taste. My agents produce volume and consistency but need a human (Hasan) to add personality and actually post the content. The agents are the factory; the human is the quality control.

What I'd change

The sequential batching is the bottleneck. Batch 1 has to finish before Batch 2 can start because of data dependencies. But within each batch, agents could run in parallel. That would cut the 47-minute run time to maybe 20 minutes.

I'd also add a feedback loop. Right now, the agents don't know which posts performed well last week. If I fed engagement data back into the strategy agents, they could learn which content types to prioritise. That's the next iteration.

Running it yourself

The full system is 27 directories, each with a prompt file, a run script, and model-specific instructions (CLAUDE.md and GEMINI.md). The shared framework (lib/common.sh) handles the orchestration.

You'd need to update the variable substitution pairs in common.sh for your product — company name, audience, pricing, tone, banned cliches. Everything else is generic enough to work for any B2B or B2C product.

$12 to generate a month's worth of marketing content feels wrong, like I'm getting away with something. The quality isn't perfect, and Hasan still spends time reviewing and posting. But the bottleneck shifted from "we don't have enough content" to "we have too much content to review." That's a better problem to have.