I Cut My AI Coding Costs by 60% — Here's the 7-Step System I Used

Chamath Palihapitiya just said his company's AI costs are trending to $10M/year. Dev Ed showed Opus 4.6 burning 100% of a session budget while GPT-5.4 used only 10% for better results.

If you're using AI coding tools in 2026 and not tracking what you spend per request, you're flying blind.

I'm a solo developer building two Mac apps. Last month, my AI API bill was embarrassing. This month, it's 60% lower — and I'm shipping faster. Here's exactly what changed.

1. I Started Tracking Per-Request Costs in Real Time

This was the single biggest unlock. I built TokenBar — a Mac menu bar app that shows me exactly what each API request costs as it happens. Before this, I had zero visibility. I'd check my dashboard at the end of the month and wince.

Seeing the cost of every request in real time changed my behavior immediately. When you watch $0.47 tick up for a simple "fix this typo" request, you start questioning your defaults.

Cost: $5 one-time (yeah, I sell it — because it genuinely solved my own problem first)

2. I Stopped Defaulting to the Most Expensive Model

Here's what my data revealed: 70% of my requests were simple enough for Sonnet or Haiku, but I was routing everything through Opus out of habit.

The breakdown:

• Architecture decisions, complex debugging → Opus ($2-4 per request)
• Code generation, refactoring, tests → Sonnet ($0.10-0.40 per request)
• Syntax fixes, formatting, simple Q&A → Haiku ($0.01-0.05 per request)

This single change cut my bill by ~40%.

3. I Reduced Context Window Bloat

Most people don't realize that context size is the #1 cost multiplier. A 200K token context window costs 10x more than a 20K one — for the same prompt.

What I do now:

• Start fresh conversations for new tasks
• Use .claudeignore / project-scoped context to exclude irrelevant files
• Summarize long conversations before continuing

4. I Blocked the Feeds That Were Eating My Focus

This one isn't about API costs — it's about the other cost. I was losing 2-3 hours daily to Twitter, Reddit, and YouTube rabbit holes between coding sessions.

I use Monk Mode on my Mac to block algorithmic feeds specifically — not entire websites. I can still search YouTube or check DMs. But the infinite scroll? Gone.

Result: My "context switching tax" dropped dramatically. I stopped making unfocused, rambling prompts born from distracted half-attention.

Cost: $15 one-time

5. I Batch Similar Tasks Together

• Morning: Architecture planning (Opus, worth the cost)
• Midday: Implementation sprint (Sonnet, 80% cheaper)
• Evening: Tests, docs, cleanup (Haiku, basically free)

6. I Write Better Prompts

A vague prompt burns 3-4x more tokens than a precise one:

❌ "Fix the bug in my auth system" → $3+

✅ "In auth/middleware.ts line 47, add exp claim validation after signature verify" → $0.15

7. I Track Everything Weekly

• Total spend by model
• Average cost per request
• Cost per feature shipped

Without measurement, you drift back to old habits within a week.

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Results After 30 Days

• Monthly AI spend: ~$480 → ~$190
• Avg cost per request: $0.87 → $0.31
• Features shipped: 12 → 19
• Focus time per day: ~3 hrs → ~6 hrs

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TL;DR

1. Track costs in real time — TokenBar ($5, Mac)
2. Match model to task — Don't use Opus for everything
3. Minimize context bloat — Fresh conversations, scoped context
4. Block algorithmic feeds — Monk Mode ($15, Mac)
5. Batch by complexity — Plan expensive, build cheap
6. Write precise prompts — Vague = expensive
7. Review weekly — What gets measured gets managed

The developers who learn to be cost-efficient now will have a massive advantage when the VC subsidies inevitably end.

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Building both tools as a solo dev. Find me on X @_brian_johnson.