Why Smart Teams Are Building Vendor-Agnostic AI Infrastructure
There is no question that AI is revolutionary. Soon many people won’t be able to do their jobs without it. Developers are already describing coding without AI as “caveman coding”. Customer support teams have offloaded functionality to AI agents. Some people won’t make a decision without asking ChatGPT first. At every business, AI is somewhere between “very nice to have” and “MISSION CRITICAL.”
So what happens when your vendor doubles prices? Or crashes with no explanation?
I’ve spent the past few months helping a Bay Area manufacturing startup prepare for exactly these scenarios. The solution isn’t technological—it’s architectural. And it’s simpler than you think.
The Problem: This Is a Business Risk, Not a Tech Problem
Let’s be clear: every individual and business that relies on AI needs to take a step back and think about how they are going to mitigate three major risks:
- Financial Risk - AI price hikes
- Performance Risk - AI gets “dumber”
- Outage Risk - AI goes down completely
Most teams integrate AI like this:
Anthropic-API-Key: {your key here}
When that single provider fails, your entire operation stops. Here’s why that’s dangerous.
Financial Risk: Price Hikes Are Inevitable
There is so much to say about the finances of AI companies. Ed Zitron’s analysis shows that generative AI remains deeply unprofitable. OpenAI is projected to make $14 billion in 2025, yet their compute costs alone will hit $14 billion. Sam Altman has openly admitted they’re losing money on Pro subscriptions. They expect to make $100 billion by 2030, but things can’t keep going the way they currently are.
Price raises are inevitable.
Right now all the AI companies are competing for market share by selling below cost—just like Uber did. Once they dominate the market or emerge on top, they’ll raise prices. The problem is that there aren’t huge gains to scale. The cost burden from AI inference is linear. Microsoft’s AI revenue from OpenAI essentially only covers server operating costs. To become profitable, AI companies won’t just need to hike prices—they’ll need to HIKE A LOT. We’re talking double, triple, or more.
Every business needs to be on the lookout for this. It’s not a matter of if, it’s a matter of when.
Performance Risk: When Your AI Gets Dumber
In August 2025, Claude got noticeably worse. As an enthusiastic Claude user, I noticed this firsthand. Anthropic had already introduced aggressive rate limiting in July 2025, and many users reported significant quality degradation. They would also dynamically bump customers down to cheaper, less capable models.
This is a real concern. The line between AI working and AI failing is thin and blurry, but it needs to clear a basic bar of performance. The issue with Claude was especially pernicious because so many developers had grown accustomed to Claude accomplishing tasks in one shot. Suddenly it seemed like it couldn’t do anything, even with multiple tries. This ties up man-hours auditing all the AI output even more closely than before.
If you don’t want to raise prices, rate limits and model downgrades are always an option—and providers will use them.
Outage Risk: When AI Just Disappears
Almost everyone gets AI via API (Application Programming Interface—the connection between your code and the AI service). What happens when the provider goes down? This has happened repeatedly in 2025.
AI can get more expensive, and you can mitigate by using it less or more wisely. AI can get dumber, and you can mitigate by having humans audit the output more closely. But when AI crashes, it’s gone. Your team is offline. Your customers can’t get support. Your developers can’t ship code.
The Solution: Diversify Your AI Providers
A unifying trend in the above risks is the single AI provider. One provider raises prices; one provider deteriorates performance; one provider has an outage. The solution? Stop putting all your eggs in one basket.
Most AI companies do not want you to do this. They need you locked into their ecosystem so they can hike prices once they have dominant market share. This is their only path to profitability.
But there are ways around this. Here’s what I implemented with my client:
Before: Single Vendor Lock-in
Anthropic-API-Key: {your key}
Model: claude-sonnet-4
When Anthropic had outages, the entire development team was dead in the water. When they introduced rate limits, productivity plummeted. When prices inevitably rise, there would be no negotiating power.
After: Multi-Vendor Resilience
OpenRouter-API-Key: {your key}
Models: anthropic/opus-4.1, qwen-coder, moonshot/kimi-k2
Fallback: local Ollama instance
Budget: 1M tokens/day with alerts
We built our AI infrastructure around OpenRouter, a service where you can access different models via a single API. No juggling API keys. No vendor lock-in. It can even choose different models based on different tasks or automatically fail over when one provider is down.
As heavy Claude Code users, my client also wanted a backup to reduce reliance on that tool. We migrated to OpenCode, an open-source alternative that works seamlessly with OpenRouter. Now we’re not locked into Anthropic’s CLI tool, and we can access any AI model we want—even the most recent Anthropic models when they’re working properly.
The Results
After the migration:
- Better visibility: Real-time cost tracking in the OpenRouter dashboard and CLI
- Zero downtime: When Anthropic had outages, we automatically switched to alternative providers
- Cost control: Token-based billing with daily limits instead of unpredictable subscription costs
- Flexibility: Can test new models and switch providers with a single line of code
The mass exodus from Claude Code that happened in 2025 proved we made the right choice. Teams that stayed locked in scrambled to find alternatives during the outages. We just kept working.
Beyond Provider Diversity: Additional Considerations
Token-based billing vs. subscriptions: Subscriptions are below breakeven and hide escalating costs. Token-based billing gives you visibility and control before costs spiral.
Security concerns: Sam Altman has warned that ChatGPT chats can be subpoenaed. For sensitive work, consider self-hosted open-source models.
Ultimate resilience: Anyone truly concerned about access to compute and AI models should consider buying their own GPUs and running open-source models. While not feasible for most businesses today, it’s the only way to eliminate provider risk entirely.
Take Action Now
I help companies build vendor-resilient AI systems. If you’re currently locked into a single provider and want to build resilience into your stack before the next crisis hits, let’s talk.
Schedule a 30-minute assessment call to identify your biggest AI risks and create a mitigation plan.
The question isn’t whether your AI provider will let you down. It’s whether you’ll be ready when they do.
About the author: I specialize in helping companies architect AI infrastructure that survives vendor failures, price hikes, and outages. My clients range from Bay Area startups to established enterprises looking to reduce their AI risk exposure.