Modular Retrieval vs Bundled Search
Exa
Bundled search pushes developers deeper into a single model stack, but it also turns retrieval into a black box that is harder to swap, tune, or combine with other tools. Independent APIs like Exa, Tavily, and Parallel are winning where teams want to choose their own model, control how search is routed, and avoid rebuilding product logic every time they change LLM providers or search vendors.
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In practice, flexibility means being able to separate search from generation. Exa describes most of its value as retrieval, not the LLM layer, and customers often bring their own model and prompt on top. That makes search a modular input instead of a bundled answer product.
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Teams that care about portability actively design for it. Ecosia uses Exa for about 500,000 AI overview queries per day, but keeps an abstraction layer so it can switch to Tavily or Parallel without rewriting the whole product. The hardest part to swap is latency tuning, not the core workflow.
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The tradeoff is convenience versus control. Tavily and Parallel both package search into LLM friendly outputs and research endpoints, while OpenAI and Google bundle web search inside their own model platforms. That removes setup work, but it also compresses the stack and makes outside search providers easier to displace.
The market is moving toward a split. Foundation model companies will keep bundling search to make their APIs easier to adopt, while independent providers will move upmarket by offering better routing, domain specific sources, and deeper research workflows that sit across models. The winners will be the products that make switching costs low for customers while making output quality visibly better.