The era of artificially cheap AI tools is ending, and Cursor’s recent pricing changes are just the beginning.
For over two years, venture capital has subsidized AI tools, keeping prices below true costs while companies built market share. The Cursor team’s recent pricing model overhaul signals this honeymoon period is ending.
What changed at Cursor:
- Previous: Unlimited usage with throttling after 500 requests
- New: $20 API credits plus pay-per-use for premium models
- Result: Many users faced unexpected bills when the rarely-used ‘auto’ mode became default
GitHub Copilot followed suit immediately:
- Premium request billing started June 18, 2025
- Previous: “Unlimited” Copilot Pro subscription
- New: 300 premium request monthly cap + $0.04 per overage request
- Premium requests include Claude Sonnet, Agent mode, and advanced features
- Result: Users discovering that some interactions count as 50+ requests each
While both companies deserve credit for handling customer concerns, this represents a coordinated industry shift toward sustainable pricing. The question isn’t whether other AI tools will follow suit—it’s when.
Understanding the Layers
Before diving into contingency plans, let’s clarify two key concepts:
- Application Layer: The UI and tools you interact with directly (ChatGPT.com, GitHub Copilot, Claude, Cursor)
- API Layer: The underlying AI models themselves (GPT-4, Claude Sonnet, Gemini Pro)
Most current subsidies exist at the application layer, where companies absorb API costs to build user bases.
What does this mean for us?
Developer Mario Zechner captured this moment perfectly:
We are in a VC funded free lunch phase. Have contingency plans.
So what should those contingency plans look like?
Plan 1: Maximize Current Value
Extract more value from existing AI investments before prices rise. If you can boost productivity from $100/month per person to $300/month per person, you can absorb price increases from $20 to $200 monthly while maintaining ROI.
Concrete steps:
- Train your team on advanced prompting techniques
- Integrate AI tools into more workflows beyond coding (documentation, testing, architecture)
- Automate repetitive tasks that previously required manual work
- Measure and document productivity gains to justify higher costs to leadership
Plan 2: Transition to Local LLMs and Open Source Tools
Replace cloud-based AI with self-hosted alternatives to control costs long-term.
Application Layer Replacements:
- Replace ChatGPT with Jan.ai for general AI assistance
- Replace GitHub Copilot/Cursor with Cline for coding assistance
API Layer Options:
- Host models locally using Ollama for easier deployment
- Use cloud services like Devstral by Mistral for managed local-style pricing
Cost Reality Check: You’ll pay for hardware or cloud GPU time, but this may be cheaper long-term than premium SaaS pricing. Start experimenting now to understand your true costs and performance trade-offs.
Plan 3: Build Vendor-Agnostic Infrastructure
Create internal systems that can switch between AI providers (OpenAI, Anthropic, Google) without disrupting your team’s workflow.
Why this matters: Current subsidies exist primarily at the application layer. Moving to API-level integration means paying full rates today, but gaining crucial flexibility for tomorrow.
The strategic payoff: When Google achieves dramatically cheaper inference, or when new providers emerge with better pricing, you can pivot instantly. Your team keeps using familiar tools while you swap the underlying AI provider seamlessly.
Implementation approach: Build or adopt tools that abstract the AI provider, allowing seamless switching between OpenAI, Anthropic, and others. Tools like Jan.ai or Cline can be configured to work with multiple API endpoints.
Change management advantage: Instead of asking your team to switch from ChatGPT to Claude (major workflow disruption), you change the backend while preserving their familiar interface.
The Bottom Line
The VC-subsidized AI era won’t last forever. Whether through maximizing current value, transitioning to local solutions, or building vendor-agnostic infrastructure, the key is preparing now while costs remain manageable.
Choose your approach based on your organization’s size, technical capabilities, and risk tolerance. But choose soon—the free lunch is ending, and the prepared will have a significant advantage over those caught off-guard.
Start experimenting with these approaches today, so you’re ready when the next pricing surprise hits.