This article was originally published on aifoss.dev
The subscription stack snuck up on most developers. ChatGPT Plus here, GitHub Copilot there, Cursor Pro because it genuinely handles multi-file edits better than anything else — and suddenly you're at $50/month before you've thought about image generation. The open-source path looks free until you account for the GPU that makes it actually useful.
Both arguments are honest. They're just measuring different things. Here are the real numbers for a solo developer in May 2026.
The Proprietary Stack: What You're Actually Paying
Subscriptions are deceptively legible. You see the number, you pay it, done. The friction is in how they compound.
Chat and general AI:
- ChatGPT Plus: $20/month — GPT-4o, o3-mini, browser-based Codex coding
- Claude Pro: $20/month — Claude Sonnet 4.6, access to Claude Code CLI, higher rate limits than free tier
Most solo developers pick one and stick with it. Using both is $40/month, which is harder to justify unless you're actively evaluating models.
Coding assistants:
- GitHub Copilot Pro: $10/month — inline completions, Copilot Chat in VS Code and JetBrains. Note: new Pro and Pro+ signups were temporarily paused as of April 20, 2026
- GitHub Copilot Pro+: $39/month — adds Claude Opus 4, o3, and all premium models
- Cursor Pro: $20/month — Composer for multi-file agent edits, chat sidebar, solid autocomplete
- Cursor Pro+: $60/month — higher usage caps; Cursor's own documentation acknowledges that "daily Agent users usually spend closer to $60-100/month" than the advertised $20
Image generation:
- Midjourney Basic: $10/month (200 images/month)
- DALL-E via ChatGPT Plus: included at $20/month with usage caps
Typical solo developer power-user stack: Claude Pro ($20) + Cursor Pro ($20) + Copilot Pro ($10) = $50/month.
A heavier stack — coding daily with agent mode, generating images, using API access for personal projects:
Claude Pro ($20) + Cursor Pro+ ($60) + Copilot Pro+ ($39) = $119/month, before any API charges.
API usage (for developers building tools):
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| GPT-4o | $2.50 | $10.00 |
| GPT-4o-mini | $0.15 | $0.60 |
| Claude Opus 4.7 | $5.00 | $25.00 |
| Claude Sonnet 4.6 | $3.00 | $15.00 |
| Claude Haiku 4.5 | $1.00 | $5.00 |
At moderate usage — say 5M input tokens and 500K output tokens per month with GPT-4o — that's $12.50 + $5.00 = $17.50/month. At serious scale (100M input tokens), it's $250/month, and the economics of self-hosting start to matter.
The Open-Source Hardware Path
Running local AI requires a GPU. The GPU market in 2026 is not kind to buyers.
RTX 40-series cards are out of production. RTX 50-series prices are inflated well above MSRP due to GDDR7 supply constraints — the planned SUPER refresh was canceled because high-density memory modules were prioritized for enterprise AI hardware. Current street prices:
| GPU | VRAM | May 2026 Street Price | What you can run |
|---|---|---|---|
| RTX 5070 | 12GB GDDR7 | ~$629 (MSRP $549) | Qwen2.5-Coder 14B Q4, Llama 3.2 8B, Mistral 7B |
| RTX 4080 SUPER (used, eBay) | 16GB | ~$870 | Llama 3.3 70B Q4, Qwen2.5 32B |
| RTX 5080 | 16GB GDDR7 | ~$1,400 | Same tier as 4080 SUPER, faster generation |
| RTX 4090 (used, eBay) | 24GB | ~$1,200–1,500 | 34B–70B at Q4, most SDXL/Flux workflows |
For image generation, a 12GB card runs SDXL at full resolution and Flux Schnell at 768px — practical but not fast. For the full rundown on what VRAM tier gets you with image models, see Stable Diffusion on 8GB VRAM 2026.
Electricity is the recurring cost that surprises people. An RTX 4090 draws ~450W under load; with CPU, RAM, and fans, total system draw sits around 550W. At the US average of $0.16/kWh:
# Estimate monthly electricity cost for local AI use
# Adjust these numbers for your setup and region
GPU_WATTS=450
SYSTEM_OVERHEAD=100 # CPU, RAM, fans — check at eia.gov for your rate
KWH_RATE=0.16 # US average; California is ~$0.30, Pacific NW ~$0.12
TOTAL_WATTS=$((GPU_WATTS + SYSTEM_OVERHEAD))
# 4 hours/day (focused work sessions)
echo "4 hrs/day: $(echo "scale=2; $TOTAL_WATTS * 4 * 30 / 1000 * $KWH_RATE" | bc)/month"
# 24/7 always-on home server
echo "24/7: $(echo "scale=2; $TOTAL_WATTS * 24 * 30 / 1000 * $KWH_RATE" | bc)/month"
Running the numbers: ~$11/month for 4 hours/day usage; ~$63/month for 24/7 operation. If you're in California, add 40–60% to those figures.
The RTX 5070 draws less — around 200–250W under typical inference loads — so electricity for focused daily use drops to $6–8/month.
Break-Even Analysis
Four scenarios, real numbers:
Scenario 1: You're paying $50/month for Claude Pro + Cursor Pro + Copilot Pro
Buy an RTX 5070 ($629) and run Ollama locally for your daily coding queries. Continue.dev replaces Cursor for $0/month. Keep one cloud subscription (Claude Pro) for complex reasoning tasks.
- Hardware: $629 one-time
- New monthly: Claude Pro ($20) + electricity ($8) = $28/month
- Monthly savings: $50 − $28 = $22
- Hardware break-even: $629 ÷ $22 = 28 months
- Year 3 savings: ~$500
That's a long break-even for the RTX 5070 because you're only replacing $30/month of subscriptions (not Claude Pro). If you drop all cloud subscriptions:
- Monthly savings: $50 − $8 = $42
- Break-even: $629 ÷ $42 = 15 months
- Year 3 savings: ~$900
Scenario 2: Heavier stack, $100+/month
At $119/month, the math gets more favorable:
- Buy RTX 4090 used ($1,300) + keep Claude Pro ($20) + electricity ($11) = $31/month
- Monthly savings: $119 − $31 = $88
- Break-even: $1,300 ÷ $88 = 15 months
- Year 3 savings: ~$2,300
Scenario 3: Privacy-critical work
The break-even calculation is irrelevant. If your documents, codebase, or data can't leave your machine — medical records, legal work, proprietary code, personal data — you buy the GPU regardless of cost. The RTX 4090 at 24GB is the right call here: it runs 70B models at Q4 quantization, nothing phones home, and the $1,300–1,500 used price is a one-time compliance cost.
The Hidden Costs That Don't Show Up in the Table
Open-source: time and quality ceiling
Setup time is overrated as an objection. Ollama + Open WebUI on a machine with an NVIDIA GPU: 30 minutes, including model downloads. That's genuinely fine.
Ongoing maintenance is where the hours accumulate. Model updates, context window config drift, broken ComfyUI nodes after a custom pack update, embedding model mismatches in RAG pipelines. Budget 2–3 hours/month if you want the stack to stay current and working. If you just want it to work, update less often.
The quality ceiling is real. A 12GB card runs 14B models at Q4 — good enough for autocomplete, small refactors, and document Q&A. It is not GPT-4o on complex multi-step reasoning tasks. That gap has narrowed significantly in 2026 (Qwen2.5-Coder 14B is genuinely competitive on routine coding tasks), but for long-context architectural reasoning, frontier cloud models still have an edge.
Hardware lifecycle: The GPU you buy today is mid-tier in 24 months. Not obsolete — a 12GB card in 2028 still runs the Q4 quantized models you'll want — but you won't be on the bleeding edge.
Proprietary: data, rate limits, and price drift
Your data leaves your machine. ChatGPT Plus does not opt you out of OpenAI's model training by default; you have to disable this in settings. GitHub Copilot Pro (personal plan) uses your prompts for model improvement unless y
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