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 multi-step reasoning chains, large-codebase refactoring, and tasks where Claude Opus 4 or GPT-5 are clearly ahead. If your daily workflow leans on those capabilities, the local alternative will frustrate you.

Multimodal and agent features: ChatGPT’s Deep Research, Sora, and Codex agent aren’t replicable locally at the same quality. Open WebUI Pipelines cover some ground, but it’s a genuine feature reduction.

Code completion latency: Cloud Copilot completions are near-instant. Continue.dev on a 3090 has 1–3 second latency for multi-line suggestions. Tolerable for many, annoying for some. If your muscle memory relies on instant completions, the gap is noticeable in the first week.

Setup time: GitHub Copilot installs in two minutes. The local equivalent (Continue.dev + Ollama setup) takes 30–60 minutes the first time, then 10–15 minutes per major update cycle.

When self-hosting wins

Two or more of these need to be true:

You’re spending $60+ per month on AI subscriptions, or $80+ including API overages. Below this threshold the math doesn’t close in a reasonable timeframe.

Your data is sensitive — client codebases, unreleased products, code under NDA. The moment you paste proprietary code into a cloud tool, you’ve accepted a risk that your client contract may not permit. Local inference closes that entirely.

You have existing GPU-heavy workloads — you’re already running local image generation with ComfyUI or Forge and adding LLM inference is incremental to hardware you’ve already bought.

You genuinely enjoy the infrastructure layer — the maintenance overhead isn’t a cost if you’d spend the time on the system anyway. Some developers find tuning quantization levels and testing new model releases satisfying rather than taxing.

If you fit this bucket, the RTX 3090 is the specific card to buy. It hits the 24GB minimum for serious local LLM work at $850 versus $2,300 for the 4090. For full workstation build recommendations (CPU, PSU, cooling), see the AI build guides on runaihome.com.

For GPU-intensive workloads where you don’t want the upfront capital cost, RunPod rents RTX 4090 and A40 instances by the hour — a useful middle ground for burst workloads.

When to stay on SaaS

The math is clear in these cases:

You’re spending under $40/month. Break-even stretches past 24 months, and the 2–5 hours per month of maintenance time erases the financial gain.

Your work depends on frontier model quality. Qwen3-30B is good. It’s not Claude Opus 4. If complex reasoning, long-context analysis, or subtlety in code review is how you use AI daily, the self-hosted alternative will underdeliver on the tasks that matter most.

You need access from multiple devices or locations. Cloud subscriptions work from any device without configuration. Local inference requires either a machine that’s on 24/7 or a VPN/Tailscale tunnel — both add complexity that erodes the convenience argument.

Your time is billed at $100+/hour. The 2–5 monthly hours of maintenance cost more than the subscriptions you’re replacing. The numbers work only if you treat that time as not billed.

One common trap: developers self-host and keep their SaaS subscriptions “just in case.” If you’re paying for both simultaneously, you’ve increased total spend while adding complexity. The decision is binary — pick one stack and commit.

The hybrid that actually works

There’s a middle path most comparisons underrate: run open-source tools for code completion and RAG, keep one cloud subscription for tasks that actually need frontier-model quality.

Practical split that makes financial sense:

• Self-host: Continue.dev + Qwen2.5-Coder-32B for inline completions and quick queries (~$8/month electricity)
• Keep: Claude Pro at $20/month for architecture discussions, complex code review, and anything needing Opus-level reasoning

Total: ~$28/month. That’s 60% less than the four-subscription stack, and you’ve kept the one tool that matters most for high-stakes tasks.

For a comparison of cloud-based coding tools including Cursor and Copilot from a feature perspective, the reviews at aicoderscope.com cover the SaaS side in detail. For the complete open-source stack picture, the 2026 FOSS AI stack overview and the proprietary vs open-source cost breakdown have the supporting detail.

FAQ

Is ChatGPT Plus actually still $20/month in 2026?
Yes, as of June 2026 — same price since launch, significantly more features. The Plus plan now includes GPT-5.4, 10 Deep Research runs per month, Sora, and Agent Mode at $20/month.

Can a single RTX 3090 run a 70B model?
At Q4 quantization, a 70B model requires roughly 40GB VRAM — too large for a single 24GB card. At Q2 quantization (~22GB), it fits but with a noticeable quality drop. For 24GB single-GPU setups, 30–34B models at Q4 are the practical ceiling. Qwen3-30B and Devstral-Small-22B are the benchmarks to beat in that range.

GitHub Copilot switched to usage-based billing — does that affect the comparison?
For typical usage, the $10/month base covers normal completion workloads. The change matters if you’re running Copilot’s agentic features heavily — actual costs can rise to $15–25/month. That narrows the self-hosted break-even very slightly but doesn’t change the overall picture.

Do I need a dedicated machine, or can I add a GPU to my existing desktop?
A used RTX 3090 drops into any desktop with a PCIe x16 slot and a 750W+ PSU. If you’re already on a workstation, the $850 GPU is the only incremental cost. If you need a dedicated machine, add $300–500 for a used chassis, CPU, and RAM — which extends the break-even by 6–10 months.

What happens if GPU prices drop later in 2026?
RTX 4090 used prices are currently elevated at ~$2,300 partly because RTX 5090 supply remains constrained. If 5090 availability normalizes in Q3 2026, the 4090 market could drop 15–20% to around $1,900, shortening its break-even against a $70/month stack from 38 to ~30 months. The RTX 3090 is already near its floor and unlikely to move significantly.

Sources

• ChatGPT pricing — OpenAI (June 2026)
• GitHub Copilot usage-based billing announcement — GitHub Blog
• Plans for GitHub Copilot — GitHub Docs
• Claude pricing — Anthropic (June 2026)
• Cursor pricing (June 2026)
• EIA May 2026 Short-Term Energy Outlook — U.S. Energy Information Administration
• RTX 3090 price tracker — Best Value GPU (June 2026)
• RTX 4090 price tracker — Best Value GPU (June 2026)

Recommended Gear

• NVIDIA RTX 4060 Ti 16GB — entry 16GB GPU for lighter local LLM and code completion workloads
• NVIDIA RTX 3090 24GB — best value 24GB GPU for serious local LLM work; the break-even sweet spot
• NVIDIA RTX 4090 24GB — fastest consumer GPU for local inference; same model capacity as 3090, ~60% faster