Every week I get the same question. Should I keep paying for Copilot, jump to Cursor, or set up Continue.dev with local Ollama and stop paying anyone? I have run all three setups for real client work, and the pricing math is more interesting than most YouTube videos make it out to be. In this post I will break down what each option actually costs over 1 to 3 years, where local AI coding genuinely wins, and where the cloud subscriptions still earn their keep.

I am going to keep this practical. No theory, no benchmarks lifted from marketing pages. Just the numbers I see when I run these tools on my own hardware against my own repositories.

What does Continue.dev with local Ollama actually cost?

The headline answer is zero per month after you own the hardware. That is the appeal. You install Ollama, you pull a model like Qwen 3 Coder or one of the 30 billion parameter mixture of expert models, you point Continue.dev at the local endpoint, and you code. No API keys, no per token billing, no monthly subscription.

The real cost lives in the hardware. To get usable speeds for agentic coding, you need to fit the entire model into GPU VRAM. The moment any parameters spill into system RAM, performance collapses. I have seen this happen on my own RTX 5090 with 32 GB of VRAM. A model that almost fits will run at maybe 10 tokens per second, while a model that fits cleanly will hit 100 to 140 tokens per second on the same machine. That difference is the gap between a tool you actually use and a tool you abandon after a week.

So what hardware tier do you actually need? For serious local AI coding in 2026, I would budget for one of three setups. A used RTX 3090 with 24 GB of VRAM lands around 700 to 900 dollars and runs the smaller coding models well. An RTX 4090 or 5090 with 24 to 32 GB of VRAM sits between 1,800 and 2,500 dollars and gives you headroom for the bigger mixture of expert models. A Mac Studio with 64 to 128 GB of unified memory runs 4,000 to 6,000 dollars but uses dramatically less power and runs quietly on your desk.

If you want to see exactly which open source projects I run on my own local stack, I keep a running list at my open source projects page along with the starter repos I hand my community.

How much does GitHub Copilot really cost over 3 years?

Copilot Individual sits at 10 dollars per month, which is 120 dollars per year, or 360 dollars over 3 years. Copilot Pro recently moved to 19 dollars per month, which is 228 dollars per year, or 684 dollars over 3 years. Copilot Business and Enterprise tiers reach 19 and 39 dollars per seat per month, which scale up fast for teams.

Those numbers look small compared to a 2,000 dollar GPU. That is the seductive part of the cloud pricing model. You never feel the bill the way you feel a hardware purchase. But there are three costs that the headline price hides.

First, the subscription is recurring forever. After 5 years of Copilot Pro, you have spent 1,140 dollars and you own nothing. After 10 years, that is 2,280 dollars. The hardware path inverts this. You spend more on day one, but the marginal cost of every additional month is approximately zero, minus electricity.

Second, Copilot Pro has usage caps on the premium models. The unlimited tier only applies to the base completion model. Once you start using Claude or GPT-5 class models inside Copilot for agentic edits, you burn through your monthly allowance fast. Power users routinely hit those caps in the first two weeks of the month.

Third, Copilot still phones home. For regulated industries, healthcare, finance, defense contractors, that is a non-starter regardless of price. Local models sidestep that conversation entirely.

If you want the honest unvarnished version of where local actually competes, I wrote a reality check on local AI coding that goes deeper than any benchmark.

What about Cursor pricing tiers in 2026?

Cursor sits in an interesting middle ground. The Free tier gives you limited slow requests. Cursor Pro at 20 dollars per month is the standard tier most developers actually use, which works out to 240 dollars per year or 720 dollars over 3 years. Cursor Business at 40 dollars per seat per month is 480 dollars per seat per year, or 1,440 dollars per seat over 3 years. The new Ultra tier at 200 dollars per month exists for people doing massive agent runs, and it adds up to 2,400 dollars per year, or 7,200 dollars over 3 years.

That last number is the one that makes local AI start to look obvious. If you are the kind of developer who would actually use the Ultra tier, you would pay for an RTX 5090 setup in less than 12 months and own the hardware forever after. The math flips hard at the high end of cloud usage.

But Cursor Pro at 20 dollars per month is genuinely good value if you are not running into rate limits. The product is polished, the model routing is thoughtful, and the autocomplete is still ahead of what local models give you for inline suggestions. I do not pretend that Continue.dev with a local Qwen model has the same fit and finish as Cursor for casual coding. It does not.

What is the real total cost of ownership over 1 year, 2 years, and 3 years?

Let me put numbers next to numbers. I will assume an RTX 4090 setup at 2,200 dollars all in, including the rest of the build, and I will assume Copilot Pro at 19 dollars per month and Cursor Pro at 20 dollars per month. I will add a generous 200 dollars per year for electricity on the local rig, since you only burn full power when you are actively generating tokens.

After 1 year, the local setup costs 2,400 dollars, Copilot Pro costs 228 dollars, and Cursor Pro costs 240 dollars. Cloud wins by a mile.

After 2 years, the local setup costs 2,600 dollars, Copilot Pro costs 456 dollars, and Cursor Pro costs 480 dollars. Cloud still wins, but the gap is closing.

After 3 years, the local setup costs 2,800 dollars, Copilot Pro costs 684 dollars, and Cursor Pro costs 720 dollars. Cloud still wins on raw dollars.

Pure dollar math at the individual tier favors cloud subscriptions for at least 5 to 7 years on a 2,200 dollar build. That is the honest answer. If you want to convince yourself that local AI is cheaper than Copilot Individual, the math will not back you up unless you stay on the same hardware for nearly a decade.

So why would anyone go local? Because the math changes completely once you account for what local actually unlocks. If you currently pay for Cursor Ultra at 200 dollars per month, your break even on a 2,200 dollar local rig is 11 months. If you run multiple Claude API subscriptions or pay per token for production code generation, your break even is even faster. And if you need to keep your code off third party servers, the cloud option is not on the menu at any price.

When does Continue.dev with local Ollama actually beat Copilot pricing?

There are four scenarios where local wins on real economics, not on principle.

The first is high volume agentic coding. If you run agents that crank out thousands of edits per day, you will exhaust any subscription cap and start paying overage fees or moving to enterprise tiers. Local models give you unlimited AI coding sessions without metering, which is the single biggest psychological unlock for actually using AI coding the way it should be used.

The second is privacy and compliance. Healthcare, finance, defense, legal. Any context where your code or your data cannot leave your machine. Continue.dev with local Ollama is the only option here, and the comparison to Copilot pricing is irrelevant because Copilot is not allowed in the room.

The third is multi developer teams who already own GPUs. If you have a small team and one strong workstation, you can use LM Studio’s link feature to expose that workstation as a local model server to every laptop on the team. I demonstrated exactly this in my recent video where I run a Qwen 3 Coder model on a Linux box with an RTX 5090 and consume it from my MacBook over an encrypted link. One GPU, multiple developers, zero monthly cost per seat.

The fourth is learning. If you want to actually understand how these models work, where they fail, and how to engineer around their limits, running them locally teaches you in three months what cloud subscriptions will never teach you. I cannot overstate how much my mental model of LLMs sharpened the day I started running them myself.

If you are genuinely trying to decide between these tools, I built a full AI coding tools decision framework that walks through the questions in order.

What about the hidden gotchas of running local models?

I want to be honest about where the local setup hurts. Three things.

First, agentic CLI tools like Claude Code inject massive system prompts into every request. When I connect Claude Code to my local Qwen model, the system prompt alone is 3,000 tokens before I have typed a single character. If your local model is configured for a 4,000 token context window, you will hit the limit immediately and the request will silently hang. You need to configure your local context window for at least 80,000 tokens, ideally 200,000, and you need a model that handles long context well. Most YouTube videos showing Claude Code with local models gloss over this completely.

Second, the smaller local coding models hallucinate more aggressively than Claude or GPT-5. When I built a Next.js dashboard using my local Qwen model through Claude Code, it invented an Nvidia RTX 3080 reference that did not exist in my codebase. State of the art cloud models do this less. You compensate by giving the model better grounding, real API documentation pasted into the prompt, sub agents with fresh context windows, and the ability to call APIs directly to self verify. It works, but it takes more discipline.

Third, your laptop is not enough. Running these models on a MacBook Air or a 16 GB MacBook Pro is theoretically possible and practically miserable. Either invest in a real GPU workstation or use LM Studio link to consume a model from a beefier machine on your network. There is no shortcut.

If you want the full step by step on getting Ollama running productively, my Ollama local development guide is the cleanest starting point I have written.

So which one should you actually pick?

If you code casually a few hours a week, stay on Copilot Individual at 10 dollars per month. The hardware investment will not pay off.

If you code professionally and you are not hitting rate limits, Cursor Pro at 20 dollars per month is the best dollar for dollar tool on the market right now. I still recommend it to most of my students.

If you are running agents constantly, paying for premium tiers, or you have any privacy or compliance constraint, Continue.dev with local Ollama on a serious GPU is the right answer. The total cost of ownership crosses over within 12 to 24 months at high usage, and you stop renting your tools.

If you are not sure which camp you are in, run both for a month. Time how often you hit a Cursor rate limit. Count how many requests you would have made with no cap. That is your real answer.

Want to go deeper?

The full local AI coding workflow I run, including LM Studio link, Claude Code routing, and the Qwen model setup, is in my YouTube video here, Unbeatable Local AI Coding Workflow Full 2026 Setup. If you want to talk through your own setup with other AI engineers, join my community at aiengineer.community. That is where I help people work out exactly which tier of hardware and which workflow makes sense for their situation, instead of guessing from a YouTube comments section.