This article was originally published on aifoss.dev
TL;DR: The break-even for a used RTX 3090 against a $70/month SaaS AI stack is roughly 14 months. For developers also burning $50+ per month on API overages, it shrinks to around 8 months. The math only works if you're spending $60+ per month and can live with models that are capable but not GPT-5 quality.
| Full SaaS Stack | RTX 3090 Self-Host | RTX 4090 Self-Host | |
|---|---|---|---|
| Best for | Zero setup, frontier models | Heavy SaaS spenders, privacy | Speed-sensitive workloads |
| Upfront cost | $0 | ~$850 used | ~$2,300 used |
| Monthly (ongoing) | $70/mo | ~$8/mo electricity | ~$10/mo electricity |
| Break-even vs $70/mo SaaS | N/A | ~14 months | ~38 months |
| Data leaves your machine | Yes | No | No |
Honest take: If you're paying $70+/month on AI subscriptions, a used RTX 3090 breaks even in about 14 months and saves roughly $740/year after that. The RTX 4090 takes over three years to break even at the same spend level — it's a speed upgrade, not a capacity one, since both cards carry the same 24GB VRAM.
What the full SaaS stack costs today
Most developers paying the "self-host or not?" question are running 2–4 AI subscriptions simultaneously. They stacked them as each tool proved useful, then opened their credit card statement and did the math.
The four-subscription stack as of June 2026:
| Tool | Plan | Monthly | What you're paying for |
|---|---|---|---|
| ChatGPT Plus | Plus | $20 | GPT-5.4, Deep Research (10 runs/mo), Sora, Agent Mode |
| GitHub Copilot | Pro | $10 | Code completions + $10 AI credit pool (usage-based since June 1, 2026) |
| Claude Pro | Pro | $20 | Sonnet + Opus access, Claude Code CLI included |
| Cursor | Pro | $20 | IDE agent, Tab completions, all frontier models, $20 credit pool |
Total: $70/month. $840/year.
GitHub Copilot moved to usage-based billing on June 1, 2026 — the $10/month is now a credit allowance rather than a flat seat fee. Heavy Copilot agent usage can push you into overage territory and raise that $10 to $15–25 per month.
Three spending profiles
Not every developer runs all four. The break-even numbers diverge sharply by spend level.
Profile 1 — Light ($20/month)
ChatGPT Plus for general research and chat. Copilot Free tier for code completion. No dedicated AI coding IDE.
Profile 2 — Moderate ($30–50/month)
ChatGPT Plus plus GitHub Copilot Pro. Occasionally adds Claude Pro when working through complex codebases.
Profile 3 — Heavy ($70/month)
All four tools running simultaneously. Cursor for daily coding, Claude for architecture and code review, ChatGPT for research, Copilot as a fallback.
Profile 4 — API-heavy ($100–150/month)
Heavy profile plus direct API calls — running evaluations, building AI-integrated tools, testing agents. This describes most developers who are actively shipping products with LLM components.
What self-hosting actually costs
The open-source stack that replaces all four tools:
- Ollama — model runner, handles inference
- Open WebUI — browser-based chat interface, replaces ChatGPT Plus UI
- Continue.dev — IDE plugin for completions and chat, replaces Copilot and Cursor
- AnythingLLM — local RAG and document chat
Software cost: $0/month.
The only real costs are hardware and electricity.
GPU options in June 2026
| GPU | VRAM | Used price | TDP | Practical model ceiling |
|---|---|---|---|---|
| RTX 4060 Ti 16GB | 16GB | ~$380 | 165W | Qwen3-14B Q4, Qwen2.5-Coder-14B Q4 |
| RTX 3090 | 24GB | ~$850 | 350W | Qwen3-30B Q4, Devstral-Small-22B, Qwen2.5-Coder-32B Q4 |
| RTX 4090 | 24GB | ~$2,300 | 450W | Same models as 3090, ~60% faster inference |
The key detail: RTX 3090 and RTX 4090 have identical VRAM. The 4090 is not a capacity upgrade — it's a speed upgrade. Both cards run the same models. If you need Llama 3.3 70B at Q4 quality (~40GB), neither card fits. Both can run it at Q2 quantization (~22GB) with a noticeable quality trade-off.
For 24GB cards, the practical ceiling is around 30–34B at Q4. Qwen3-30B and Devstral-Small-22B are the standout performers in this range as of June 2026. For the 16GB RTX 4060 Ti, you're looking at 13–14B models — still useful for code completion, noticeably weaker for complex reasoning.
Electricity: the real numbers
US residential average: 18.2 cents/kWh (EIA May 2026 Short-Term Energy Outlook).
Assuming 4 active inference hours per day — realistic for a full-time developer:
RTX 3090 (350W) × 4h/day × 30 days = 42 kWh/month
42 kWh × $0.182 = $7.64/month → ~$8/month
RTX 4090 (450W) × 4h/day × 30 days = 54 kWh/month
54 kWh × $0.182 = $9.83/month → ~$10/month
RTX 4060 Ti (165W) × 4h/day × 30 days = 19.8 kWh/month
19.8 kWh × $0.182 = $3.60/month → ~$4/month
These figures are marginal cost — the incremental draw above a baseline desktop that's already on. Add $5–10 more if the machine stays on overnight.
The maintenance tax
This is the line item every forum thread omits. Plan for 2–5 hours per month:
- Pulling updated model weights when a new Qwen or Mistral release drops
- Updating Ollama, Open WebUI, and Continue.dev (all ship updates frequently)
- Troubleshooting IDE plugin disconnects after system or kernel updates
- Occasionally switching quantization levels or models as community benchmarks shift
At a conservative $75/hour opportunity cost, that's $150–$375/month — far larger than the subscriptions being replaced. This doesn't mean self-hosting is irrational; developers who enjoy the tinkering find the maintenance free. But calling it "free" in a cost model is wrong.
The break-even table
Cumulative cost at each time horizon, assuming you cancel all SaaS subscriptions on day one:
Heavy user ($70/month SaaS) vs. RTX 3090
| Milestone | SaaS stack cumulative | RTX 3090 cumulative |
|---|---|---|
| Month 1 | $70 | $858 ($850 GPU + $8 electricity) |
| Month 6 | $420 | $898 |
| Month 12 | $840 | $946 |
| Month 14 | $980 | $962 ← break-even |
| Month 24 | $1,680 | $1,042 |
| Month 36 | $2,520 | $1,138 |
Month 14 is when total spend flips. After 36 months, total self-host spend is $1,382 less than the SaaS equivalent.
Heavy user ($70/month) vs. RTX 4090
| Milestone | SaaS stack cumulative | RTX 4090 cumulative |
|---|---|---|
| Month 1 | $70 | $2,310 |
| Month 12 | $840 | $2,420 |
| Month 24 | $1,680 | $2,540 |
| Month 36 | $2,520 | $2,660 |
| Month 38 | $2,660 | $2,680 ← break-even |
The RTX 4090 breaks even at 38 months against a $70/month SaaS stack. That's a three-year horizon — longer than most hardware remains competitive, and longer than most developers' actual usage patterns hold constant.
API-heavy user (~$120/month total AI spend) vs. RTX 3090
| Milestone | SaaS + API cumulative | RTX 3090 cumulative |
|---|---|---|
| Month 1 | $120 | $858 |
| Month 6 | $720 | $898 |
| Month 8 | $960 | $914 ← break-even |
| Month 12 | $1,440 | $946 |
| Month 24 | $2,880 | $1,042 |
For developers spending $100+ per month including API costs, the RTX 3090 breaks even around month 8. Three-year savings: $1,838.
Light user ($20/month) — don't bother
$20/month SaaS is $240/year. Even a $380 RTX 4060 Ti takes 24 months to break even on electricity savings alone:
$380 / ($20/month - $4/month electricity) = 23.75 months
The margin is too thin. The setup complexity isn't worth it for financial reasons. Privacy is a separate argument — if that's the driver, the math changes, but the cost model doesn't.
What you actually give up
Self-hosting is not a lossless substitution. The gaps are real:
Model quality ceiling: The best 24GB local models (Qwen3-30B, Devstral-Small-22B) handle most coding tasks competently. They fall short on
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