The Bill Finally Arrived

Picture this: you sit down on a Monday morning, open your IDE, and start coding like you always do. By Tuesday afternoon, your entire month of AI credits is gone. That is exactly what thousands of GitHub Copilot users experienced on June 2, 2026, when the platform's new usage-based pricing model went live.

GitHub's old system charged a flat rate regardless of whether you asked a quick question or ran a multi-hour autonomous coding session. The new model charges per token, per model, and per request type. Some users shared estimates showing their normal monthly habits would cost thousands of dollars under the new plan. That is a hard conversation to have with a finance team.

So the real question is: do you adjust your habits, upgrade your plan, or look elsewhere? Let's look at what else is out there for developers who want AI assistance without the billing anxiety.

Why Usage-Based Pricing Hits Developers Hard

Most developers do not think in tokens. They think in tasks. "I need to refactor this function" or "explain what this regex does" feels like one action. Behind the scenes, it might consume hundreds or thousands of tokens depending on the model you picked and the length of your codebase context.

Under GitHub's current structure, the $10/month Pro plan gives you 1,500 credits (equal to $15 of usage). The $39 Pro+ plan gives 7,000 credits ($70 worth), and the $100/month Copilot Max plan includes 20,000 credits ($200 worth). The gap between what you pay and what you actually get to spend grows as you go up the tier ladder, but heavy users are still finding it tight.

The deeper issue is predictability. When you run a local model or use a flat-rate competitor, you know exactly what you are paying each month. Token-based billing introduces variance that makes budgeting genuinely difficult, especially for freelancers or small teams without enterprise contracts. If you want to understand how compounding costs add up over time, a APY calculator tool can actually help you model out annual spend across different pricing tiers before committing.

Developers who use agentic features (where the AI takes multi-step actions autonomously) are the hardest hit. A single agent run can consume far more tokens than a simple autocomplete suggestion. That is not a criticism of the technology. It is just a mismatch between how the billing works and how developers naturally want to use modern AI tools.

5 Alternatives Worth Trying Right Now

1. Cursor

Cursor is a full IDE built on top of VS Code with AI baked in at every layer. It uses a subscription model with a generous free tier and paid plans that bundle a set number of fast requests per month. The key difference from Copilot is that Cursor lets you bring your own API key, so you can connect directly to OpenAI, Anthropic, or other providers and pay those providers directly instead of going through a middleman markup.

Developer comparing AI coding assistant pricing plans on a laptop screen

Cursor's "Composer" feature lets you describe multi-file changes in plain English and apply them across your project. It also supports context from your entire codebase, not just the open file. For teams that want predictable billing, the bring-your-own-key option is genuinely valuable. You pay the model provider's published rate, nothing more.

AI Coding Assistant Pricing Comparison

GitHub Copilot Pro $10/month for 1,500 credits

GitHub Copilot Pro+ $39/month for 7,000 credits

GitHub Copilot Max $100/month for 20,000 credits

Usage Cost per Token Varies per request type

One practical example: a developer working on a React app can open Cursor, describe a new feature in the Composer panel, and watch it draft changes across components, tests, and config files simultaneously. The token cost goes directly to your API provider at their standard rate.

2. Codeium (now Windsurf)

Codeium rebranded its IDE product as Windsurf in late 2024 and has continued building it out through 2025 and 2026. The free tier remains one of the most capable in the market, offering autocomplete, chat, and basic agentic features without a credit limit on standard completions.

Windsurf's "Cascade" agent is worth noting. It can browse your codebase, run terminal commands, and make coordinated edits across files. Paid plans exist for heavier usage, but many solo developers and students find the free tier genuinely sufficient for day-to-day work. That is a hard offer for GitHub Copilot to compete with right now.

3. Amazon Q Developer

Amazon Q Developer (formerly CodeWhisperer) offers a free tier with unlimited code suggestions and a professional tier at $19/month per user. If you already live in the AWS ecosystem, the integration is tight. It supports security scanning, infrastructure-as-code suggestions, and CLI assistance out of the box.

The free tier does not cap on tokens or credits for standard inline completions. You pay more for agent features, but the baseline is genuinely free. For AWS-heavy teams, this is probably the most underrated option on this list.

4. Running a Local Model with Continue.dev

This is the option that costs the least money but requires the most setup. Continue.dev is an open-source VS Code and JetBrains extension that connects to any local or remote model. Pair it with Ollama to run models like Llama 3 or CodeGemma directly on your machine, and your monthly AI bill drops to exactly zero.

The trade-off is hardware. Running a capable coding model locally needs a decent GPU. If you are considering building or upgrading a workstation for this kind of workload, something like the Ultra Game PC Lumix (Ryzen 7600X + RTX 4070 Ti) has the VRAM to run 13B parameter models comfortably. A one-time hardware cost beats a recurring monthly bill that keeps growing.

Here is a minimal setup to get Continue.dev running with Ollama:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a coding model
ollama pull codestral

# In VS Code, install the Continue extension
# Then add this to your Continue config.json:
{
  "models": [
    {
      "title": "Codestral (local)",
      "provider": "ollama",
      "model": "codestral"
    }
  ]
}

Once that is running, you get autocomplete and chat with zero per-token cost. The quality is not identical to GPT-5 class models, but for routine coding tasks it is more than adequate. Many developers use local models for everyday work and reserve cloud-based tools for genuinely complex tasks.

5. JetBrains AI Assistant

JetBrains AI Assistant is built into the JetBrains IDE family (IntelliJ, PyCharm, WebStorm, etc.) and uses a flat subscription model at roughly $10/month. It does not use a credit or token system for standard features. You pay the same amount whether you use it heavily or lightly.

For JetBrains users, this is the path of least resistance. The assistant understands your project structure natively because it lives inside the IDE. It supports code generation, test writing, documentation, and commit message suggestions. The flat pricing is the main appeal right now, given what is happening with Copilot's billing.

How to Actually Compare These Tools

Pricing is only one variable. Before switching tools, it helps to map out how you actually use AI assistance during a typical workday. Are you mostly using autocomplete? Chat? Agentic multi-step tasks? Each tool has different strengths depending on the answer.

A useful way to think about this: track your usage patterns for one week before making a decision. Note how many times you trigger autocomplete versus how many times you start a longer chat conversation. That split will tell you which pricing model works in your favor. You can use a percentile calculator to see where your usage falls relative to the ranges published by these platforms, which can help you spot whether you are a light, average, or heavy user.

Also consider where you want your code to go. Local models keep your code entirely on your machine. Cloud-based tools send your code to external servers for processing. For open-source projects that is usually fine. For proprietary code, check the data handling policies of each provider carefully before connecting your entire codebase.

If you are building something commercial and want to understand how these cost differences affect your competitive positioning, running a quick AI market analysis tool report on your niche can surface how other teams in your space are handling AI tooling costs. It is a step that most solo developers skip but that actually matters at scale.

The Bring-Your-Own-Key Strategy

One pattern that is gaining traction among professional developers is the bring-your-own-key (BYOK) approach. Instead of paying a platform for AI credits, you get API keys directly from model providers like Anthropic, OpenAI, or Google, then plug them into tools like Cursor or Continue.dev.

This gives you full visibility into your spending. You see exactly which models you used and how much each call cost. There are no markups, no credit conversion rates, and no surprises. You can set hard spending limits directly on your API account. For teams managing multiple developers, this is a much cleaner billing story.

The downside is that it adds a small administrative overhead. You need to manage API keys, monitor usage, and potentially rotate keys across team members. But for anyone who has just watched their Copilot credits evaporate in a single afternoon, that overhead probably sounds pretty appealing right now.

Tools like Observable are useful for building quick dashboards to visualize your API spending over time, especially if you are juggling keys from multiple providers. A simple chart tracking daily token spend by model can catch runaway costs before they become a real problem.

What This Pricing Shift Actually Signals

GitHub's move to usage-based billing is not a surprise. It is a correction. The old flat-rate model was effectively a subsidy for power users, and GitHub was absorbing the difference. That was never going to last as AI inference costs became a significant line item.

Side-by-side comparison table of AI coding assistant pricing models in 2026

What is interesting is the timing. Several competitors have specifically held flat-rate pricing as a differentiator in 2025 and 2026. Whether that holds as their own costs grow is an open question. The whole industry is figuring out the same problem: how do you charge fairly for something whose cost depends heavily on how the user chooses to use it?

Predictability of AI Coding Costs

$10/month

Pro Plan Cost

$39/month

Pro+ Plan Cost

$100/month

Max Plan Cost

1,500 credits

Tokens in Pro Plan

For developers, the practical takeaway is that AI coding assistance is no longer a commodity where every tool costs the same. It is worth spending an hour comparing options based on your actual usage patterns. The right tool for a developer who uses autocomplete all day is different from the right tool for someone running agentic refactoring sessions. Both of those are different from a developer who just wants an occasional chat assistant.

If you want to explore how data visualization can help you track and communicate these cost comparisons to a team or client, D3.js is still the standard for building custom interactive charts in the browser. Pair it with Angular if you want a full dashboard framework. And if you are creating documentation or presentations around your findings, running assets through a free image compressor before uploading screenshots or diagrams keeps your pages loading fast.

Game developers navigating similar AI tooling decisions can find useful context at Game Developer, which has been covering AI integration in game pipelines extensively. The token cost problem looks different when you are generating assets versus writing game logic, but the budgeting challenge is the same.

For teams creating internal documentation or sharing tool comparisons, Creative Commons licensed materials can supplement your own content without copyright concerns. And if video walkthroughs are part of your workflow, a free video compressor helps keep file sizes manageable when sharing screen recordings of your AI tooling setup.

Making the Switch: A Practical Checklist

If you are seriously considering moving away from GitHub Copilot, here is a sensible order of operations:

Export your Copilot usage data from your GitHub account settings to understand your actual monthly token consumption.
Map your usage to the three main categories: autocomplete, chat/Q&A, and agentic tasks.
Trial Cursor or Windsurf for two weeks on a non-critical project. Both have free tiers that are genuinely usable.
If you have a capable GPU (RTX 3080 or better), set up Ollama with Continue.dev as a baseline comparison point.
Check your company's code privacy policy before connecting to any cloud-based tool.
After two weeks, compare the actual cost and the quality of suggestions you received.

The goal is not to find the cheapest option. It is to find the option where the cost matches the value you actually get. For some developers, Copilot Max at $100/month is genuinely worth it. For others, a free local model covers 90% of their needs. Knowing which camp you are in is worth the two-week experiment.

The Bottom Line

GitHub Copilot's new credit system has forced a useful conversation. AI coding tools are not free infrastructure. They have real costs, and those costs scale with how you use them. The developers who will come out ahead are the ones who treat AI tooling like any other line in their tech stack: evaluate it, measure it, and choose based on actual fit rather than brand familiarity.

The good news is that the alternatives have never been better. Cursor, Windsurf, Amazon Q Developer, Continue.dev, and JetBrains AI Assistant are all genuinely capable tools in 2026. Several of them have pricing models that are more predictable than Copilot's new system. Try at least two of them before deciding whether to stick with Copilot, upgrade your plan, or move on entirely.

The right AI coding assistant is the one you can actually afford to use every day without watching a credit meter. Go find yours.