Why are people turning to LLM API aggregators instead of using official APIs directly?

I've seen this question pop up quite a bit, so I wanted to share my thoughts on it.

1. Simplicity and Development Efficiency

   • Unified API Format: Most aggregation platforms package APIs from different providers, like OpenAI, Anthropic, Google, and DeepSeek, into a single, unified interface. From a developer's perspective, this is a huge time-saver: I only need to integrate one codebase, which often aligns with the OpenAI API format. If I want to switch models later, I simply change the model name or parameters.
   • One API Key for Multiple Models: There's no need to register on multiple official platforms or manage a bunch of different API keys. With just one account and one API key on an aggregator platform, I can access a variety of popular large models. This significantly reduces the hassle of initial setup and ongoing maintenance for developers.

2. Failover and Stability Assurance

   • Automatic Routing and Backup: If an official API is slow to respond or goes down, the aggregator can use smart routing technology to automatically switch to other models or backup channels, ensuring that services remain online.
   • Load Balancing: In high-concurrency situations, aggregators can distribute requests through a pool of accounts, helping to avoid the strict rate limits imposed by official APIs.

3. Cost Advantages

   • Price Differences: Aggregator platforms often secure lower prices through bulk purchasing or channel discounts, making them cheaper than official retail rates. This can significantly reduce token consumption costs for testing or non-core projects. While there might be trade-offs in terms of quality, the savings can be worth it. For example, I’m currently using the gpt-5.5 model on gptproto, which is 20% cheaper than the official site—essentially a discount store for cutting-edge models. Sometimes it’s cheaper than the API, and other times it’s not, but you can switch flexibly when discounts arise.
   • Detailed Billing: Platforms usually offer unified billing management and multi-dimensional usage statistics, making it easier for teams to share costs and manage budgets.

4. Privacy Protection

   • Decoupling Requests from Personal Identity: When using an aggregator to call models, requests aren't directly tied to my personal or team accounts with any specific provider. All the model providers see is the traffic coming from the aggregator, not my direct requests.
   • Potentially More Flexible Experience: Because there's an aggregator in between, certain restrictions from model providers might feel less direct in some scenarios, leading to a more relaxed calling experience. Of course, this ultimately depends on the rules and compliance strategies of the aggregator itself.

I've had positive experiences with a few platforms:

• gptproto: gptproto.com
• openrouter: openrouter.ai
• fal.ai: fal.ai
• Fireworks.ai: fireworks.ai
• WaveSpeed.ai: wavespeed.ai

If you have any other great platform recommendations, feel free to share them in the comments!

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