In the world of large language models (LLM), speed is the only feature that matters once accuracy is resolved. For a human, it’s okay to wait 1 second for a search result. For an AI agent performing 10 sequential searches to solve a complex task, a 1-second delay per search creates a 10-second interval. This latency ruins the user experience.
Axa, the search engine startup formerly known as Metaphor, recently released axa instant. It is a search model designed to provide the world’s web data to AI agents 200 ms. For software engineers and data scientists building retrieval-augmented generation (RAG) pipelines, it removes the biggest hurdle in agentic workflow.
Why is latency the enemy of RAG?
When you create a RAG application, your system follows a loop: the user asks a question, your system searches the web for context, and LLM processes that context. If search steps 700 ms To 1000msThe total ‘time to first token’ becomes sluggish.
Exa Instant returns results with latency in between 100ms And 200 ms. In tests conducted from us-west-1 (Northern California) area, network latency was roughly 50ms. This speed allows agents to perform multiple searches in a single ‘consideration’ process without the user feeling the delay.
No more ‘wrapping’ Google
Most search APIs available today are ‘wrappers’. They send a query to a traditional search engine like Google or Bing, extract the results and send them back to you. This adds layers of overhead.
AXA Instant is different. It is built on a proprietary, end-to-end neural search and retrieval stack. Instead of keyword matching, Exa uses embedding And transformer To understand the meaning of a question. This neural approach ensures that the results are relevant to the AI’s intent, not just the specific words used. By owning the entire stack from the crawler to the inference engine, Axa can optimize for speed in ways that ‘wrapper’ APIs cannot.
benchmarking speed
The Exa team benchmarked Exa Instant against other popular alternatives Tawili Ultra Fast And Brave. To ensure that tests were fair and avoided ‘cached’ results, the team used SealQA query dataset. They also added random words generated GPT-5 Forcing the engine to perform a new search every time for each query.
The results showed that Exa Instant is dependent on 15x Faster than competitors. While Exa offers other models like axa fast And axa auto For high-quality logic, AXA Instant is the clear choice for real-time applications where every millisecond counts.
Pricing and developer integration
The change to Exa Instant is simple. accessible via API dashboard.exa.ai platform.
- Cost: axa instant price is $5 Per 1,000 Demand.
- Capacity: It searches the same huge index of the web as Exa’s more powerful models.
- accuracy: Despite being designed for speed, it maintains high relevance. For specific entity searches, Exa webset The product remains the gold standard, proving 20x More accurate than Google for complex queries.
The API returns clean content ready for LLM, eliminating the need for developers to write custom scraping or HTML cleanup code.
key takeaways
- Sub-200 ms latency for real-time agents: Axa Instant is optimized for ‘agentic’ workflows where speed is a constraint. giving below results 200 ms (and the lower the network latency 50ms), it allows AI agents to perform multi-step reasoning and parallel searches without the lag associated with traditional search engines.
- Proprietary Neural Stacks vs. ‘Wrappers’‘: Unlike many search APIs that simply ‘wrap’ Google or Bing (adding 700ms+ overhead), Axa Instant is built on a proprietary, end-to-end neural search engine. It uses a custom Transformer-based architecture to index and retrieve web data, offering 15x Faster performance than existing alternatives like Tavili or Brave.
- Cost-Efficient Scaling: The model is designed to make the Discovery look ‘primitive’ rather than an expensive luxury. its price is $5 Per 1,000 Request allows developers to integrate real-time web lookups at every step of the agent’s thought process without breaking the budget.
- Semantic intent on keywords: AXA Instant Leverage embedding Giving priority to the ‘meaning’ of a question rather than exact word matching. This is particularly effective for RAG (Retrieval-Augmented Generation) applications, where finding ‘link-worthy’ content that fits the context of the LLM is more valuable than simple keyword hits.
- Optimized for LLM consumption:The API provides more than just URLs; It provides clean, parsed HTML, Markdown and Token-Efficient Highlights. This reduces the need for custom scraping scripts and reduces the number of tokens required to process LLM, thereby speeding up the entire pipeline.
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