Should I Build It Should I Build It
31 May 2025
SEO

an api for llm search engines to find current information on the ...

...internet

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Freemium

People love using similar products but resist paying. You’ll need to either find who will pay or create additional value that’s worth paying for.

Should You Build It?

Build but think about differentiation and monetization.

Your are here

You're entering a space with a good number of competitors (n_matches=17), all vying to provide APIs for LLM search engines. The average engagement (n_comments=8) suggests moderate interest, reflecting a market that's curious but not yet rabid. The core challenge, as indicated by the 'Freemium' category, is monetization. People are interested in using these tools, but convincing them to pay is another story. You'll need to differentiate your offering and identify the specific value drivers that will compel users to upgrade from free to paid plans. Don't worry too much though, since most ideas are in the same boat (lots of use but no clear willingness to pay).

Recommendations

  1. Given the freemium nature of the market, focus on identifying which users derive the MOST value from the free version of your API. Understand their pain points and usage patterns. This information will be crucial for designing premium features that directly address their needs and justify the cost of upgrading. For example, are they hitting rate limits or lacking specific data sources?
  2. Based on what you learn, create premium features that provide significantly enhanced value. This could include higher rate limits, access to more comprehensive data sources, advanced filtering and analytics capabilities, or dedicated support. Consider how Metaphor Search API was suggested to enhance their product with advanced filtering, analytics, and customization options. Think about what 'advanced' means in your context.
  3. Explore the possibility of charging teams or organizations rather than individual users. Businesses are often willing to pay for solutions that improve team productivity or provide access to valuable data insights. This also aligns with the idea of offering personalized help or consulting, as larger organizations may require more tailored support.
  4. Offer personalized help or consulting services to enterprise clients that need more hand-holding. This could include onboarding assistance, custom integration support, or ongoing optimization of their LLM search strategies. This also helps get more feedback.
  5. Test different pricing approaches with small groups of users before rolling them out to the entire user base. Experiment with various pricing models (e.g., usage-based, subscription-based, tiered pricing) to determine what resonates best with your target audience. Pay close attention to conversion rates and customer feedback to refine your pricing strategy.
  6. Address criticisms found in similar product launches. For instance, LangSearch faced concerns about search quality and scalability. Ensure your API provides high-quality, reliable results and can handle increasing traffic volumes. Proactively address these potential pain points in your marketing materials and documentation.
  7. Consider offering enhanced documentation based on the comments in the Metaphor API launch. Complete documentation helps the user to understand and use the product quickly and properly.
  8. Prioritize speed and accuracy, as these were highlighted as positive aspects in the SearchApi launch. Optimize your API for performance to ensure users can quickly retrieve the information they need. Continuously monitor and improve accuracy to maintain user trust and satisfaction.

Questions

  1. Given the criticism around transparency for Algomo, how will you ensure transparency in your LLM search API, especially regarding data sources, search algorithms, and potential biases?
  2. Considering the concerns around scraping ethics mentioned in the 'Turn any website into a knowledge base' launch, what measures will you implement to ensure ethical data sourcing and compliance with website policies and copyright laws?
  3. Based on the mixed feedback for similar products, how will you differentiate your API to prevent it from being seen as redundant with existing search solutions like Google, and what unique value proposition will you offer to attract and retain users?

Your are here

You're entering a space with a good number of competitors (n_matches=17), all vying to provide APIs for LLM search engines. The average engagement (n_comments=8) suggests moderate interest, reflecting a market that's curious but not yet rabid. The core challenge, as indicated by the 'Freemium' category, is monetization. People are interested in using these tools, but convincing them to pay is another story. You'll need to differentiate your offering and identify the specific value drivers that will compel users to upgrade from free to paid plans. Don't worry too much though, since most ideas are in the same boat (lots of use but no clear willingness to pay).

Recommendations

  1. Given the freemium nature of the market, focus on identifying which users derive the MOST value from the free version of your API. Understand their pain points and usage patterns. This information will be crucial for designing premium features that directly address their needs and justify the cost of upgrading. For example, are they hitting rate limits or lacking specific data sources?
  2. Based on what you learn, create premium features that provide significantly enhanced value. This could include higher rate limits, access to more comprehensive data sources, advanced filtering and analytics capabilities, or dedicated support. Consider how Metaphor Search API was suggested to enhance their product with advanced filtering, analytics, and customization options. Think about what 'advanced' means in your context.
  3. Explore the possibility of charging teams or organizations rather than individual users. Businesses are often willing to pay for solutions that improve team productivity or provide access to valuable data insights. This also aligns with the idea of offering personalized help or consulting, as larger organizations may require more tailored support.
  4. Offer personalized help or consulting services to enterprise clients that need more hand-holding. This could include onboarding assistance, custom integration support, or ongoing optimization of their LLM search strategies. This also helps get more feedback.
  5. Test different pricing approaches with small groups of users before rolling them out to the entire user base. Experiment with various pricing models (e.g., usage-based, subscription-based, tiered pricing) to determine what resonates best with your target audience. Pay close attention to conversion rates and customer feedback to refine your pricing strategy.
  6. Address criticisms found in similar product launches. For instance, LangSearch faced concerns about search quality and scalability. Ensure your API provides high-quality, reliable results and can handle increasing traffic volumes. Proactively address these potential pain points in your marketing materials and documentation.
  7. Consider offering enhanced documentation based on the comments in the Metaphor API launch. Complete documentation helps the user to understand and use the product quickly and properly.
  8. Prioritize speed and accuracy, as these were highlighted as positive aspects in the SearchApi launch. Optimize your API for performance to ensure users can quickly retrieve the information they need. Continuously monitor and improve accuracy to maintain user trust and satisfaction.

Questions

  1. Given the criticism around transparency for Algomo, how will you ensure transparency in your LLM search API, especially regarding data sources, search algorithms, and potential biases?
  2. Considering the concerns around scraping ethics mentioned in the 'Turn any website into a knowledge base' launch, what measures will you implement to ensure ethical data sourcing and compliance with website policies and copyright laws?
  3. Based on the mixed feedback for similar products, how will you differentiate your API to prevent it from being seen as redundant with existing search solutions like Google, and what unique value proposition will you offer to attract and retain users?

  • Confidence: High
    • Number of similar products: 17
  • Engagement: Medium
    • Average number of comments: 8
  • Net use signal: 1.4%
    • Positive use signal: 7.9%
    • Negative use signal: 6.4%
  • Net buy signal: -4.0%
    • Positive buy signal: 0.8%
    • Negative buy signal: 4.8%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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The Product Hunt launch received overwhelmingly positive feedback, with many users congratulating the team and wishing them good luck. Several users explicitly mentioned the product's appealing aesthetics and expressed excitement about its potential. One user highlighted the Metaphor Search API's utility for connecting LLMs to the web. A suggestion was made to enhance the product with advanced filtering, analytics, customization options, and comprehensive documentation. Jeff and the team received specific praise and kudos for the launch, with at least one user calling the product a favorite.

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The Product Hunt launch garnered interest with users inquiring about content and media data sources like Twitter/Reddit, as well as free tier scalability, usage limits, and transition to paid plans. Positive feedback highlighted the tool's cost-effectiveness and the excellent performance of its rerank model. Users found LangSearch useful, with questions focusing on language support and integration options. There were also criticisms regarding search quality compared to alternatives like Exa and Tavily, indicating room for improvement in reranking capabilities.

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Dear HN, we'd love your feedback on a different approach to Retrieval Augmented Generation (RAG) for LLMs that uses function calling to retrieve the most relevant data from APIs. It aims to solve the problem of connecting LLMs with your data so that the LLM can pull in the context it needs to provide high quality answers to user questions. We are calling it "apiRAG" for now (yes, very creative ;-)You can run the examples in the GitHub repository or watch this video to see how it works: https://www.youtube.com/watch?v=Mx-slh6h42cWe developed apiRAG because we found that existing RAG approaches (text search, vector based, FLARE, etc) don't work well with structured and semi-structured data. apiRAG can efficiently augment from structured and semi-structured data by translating user questions to relevant API requests and then presenting the result data to the user. It supports textual presentation (with Markdown for tables and such) and visual presentation by charting data when appropriate. Check out the IoT chatbot that can answer questions about collected sensor data or the credit card example that gives users a customized spending analysis (shown in the video).Unlike RAG approaches that use text or vector search, apiRAG uses the LLM to determine what information should be retrieved and augmented into the context via function calling. That means, it often does a better job at identifying relevant information by pushing down filters, like when you ask "Who appears in the third episode of season 2" in our "Rick and Morty" example.apiRAG also works great for customer sensitive data, like in our Nutshop and banking examples where customers can ask questions about their past purchases/transactions and the LLM can answers those effectively by pulling in the relevant data from the API while being "sandboxed" to only access a particular authenticated customer's data.Most importantly, apiRAG is really simple. Just a lightweight library configured with a single JSON file that specifies the API endpoints (you can reuse existing APIs). All the heavy lifting is done by the LLM.Let us know what you think of apiRAG and if you'd find it useful for building generative AI applications. How could we make it better?

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