✓ Survived Rank 1 of 5 proposedIndie AI infra Oct 18, 2026

Benchmark and routing layer for agent search APIs

Continuously benchmarks web search/fetch APIs on quality, price and fetch success, then routes each agent query to the best provider.

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A ready-to-paste brief for Claude Code, Cursor or any coding agent.

I want to build a small, bootstrappable product. Help me plan and start the MVP.

Product: Benchmark and routing layer for agent search APIs
What it is: Continuously benchmarks web search/fetch APIs on quality, price and fetch success, then routes each agent query to the best provider.
Who pays: Platform/AI engineers at agent and RAG startups spending $500-5,000/mo across Tavily/Exa/Brave/DeepSeek/Jina; $99-499/mo from the existing search-API budget line they already have.
Closest alternatives: Artificial Analysis (static benchmarks, no routing), LiteLLM/OpenRouter (route LLMs, not search/fetch APIs), Exa/Tavily/Parallel (single providers, no cross-provider choice)
The gap: Benchmark sites are read-only snapshots and LLM routers ignore the search/fetch layer; nobody offers live quality-plus-price evaluation with automatic per-query routing across search and fetch providers.
Why now: In the last ~12 months the agent search-API market fragmented (DeepSeek at $0.75/1k, Brave $5, Kagi $12, Exa premium, Parallel, Jina free tier), so choosing and re-choosing providers became a recurring engineering chore that did not exist before.
Moat to build toward: Compounding benchmark corpus and per-domain fetch success-rate data (which provider actually returns usable content for which sites) improves routing over time and is costly to replicate.
Biggest risk: Target customers are extremely price-sensitive (thread obsesses over $0.75 vs $5 per 1k), so a paid middleware may be rejected in favor of free DIY scripts and open-source kits like searxng-ai-kit.

First three steps:
1. Build a harness that runs a fixed query/URL set weekly against 6+ search/fetch APIs and scores cost, latency, content quality and anti-bot failures.
2. Publish a public leaderboard page to capture SEO and email signups from agent developers.
3. Ship a paid proxy endpoint that accepts a search/fetch request and routes it to the best-scoring provider per query type, with usage and savings reporting.

Start by asking me up to five questions about constraints (stack, budget, timeline), then propose the smallest version a paying customer would use, and scaffold it.

Overall6.2/10

Pain6

Willingness to pay5

Timing7

Gap7

Moat6

Feasibility7

For

Who pays

Platform/AI engineers at agent and RAG startups spending $500-5,000/mo across Tavily/Exa/Brave/DeepSeek/Jina; $99-499/mo from the existing search-API budget line they already have.

What people said · 4

Commenter ran their own fetch benchmarks and found DeepSeek's quality lacking versus Jina, Parallel, Firecrawl, Tavily and Exa, implying everyone must test providers themselves. Read the comment ↗
Commenter points to a third-party compilation of per-1000-query search API costs, showing buyers actively hunt for price comparisons. Read the comment ↗
Commenter hand-built their own search/fetch MCP tools from scraping providers because integrated offerings cost 15-20x more, an expensive DIY workaround. Read the comment ↗
Commenter pays a premium for Exa specifically for higher signal-to-noise retrieval and a longer query field, showing quality-vs-price tradeoffs drive real spend. Read the comment ↗

Paraphrased from the discussion. Follow the links for the original comments.

Competitors and gap

Artificial Analysis (static benchmarks, no routing)LiteLLM/OpenRouter (route LLMs, not search/fetch APIs)Exa/Tavily/Parallel (single providers, no cross-provider choice)

Benchmark sites are read-only snapshots and LLM routers ignore the search/fetch layer; nobody offers live quality-plus-price evaluation with automatic per-query routing across search and fetch providers.

Why now

In the last ~12 months the agent search-API market fragmented (DeepSeek at $0.75/1k, Brave $5, Kagi $12, Exa premium, Parallel, Jina free tier), so choosing and re-choosing providers became a recurring engineering chore that did not exist before.

Moat · 6/10

Compounding benchmark corpus and per-domain fetch success-rate data (which provider actually returns usable content for which sites) improves routing over time and is costly to replicate.

Against

Red team: Live quality-plus-price benchmarking with automatic routing across fragmented search/fetch APIs is a real, newly-created chore no incumbent covers (Artificial Analysis is read-only, LiteLLM ignores search), and buyers already pay for these APIs.
What would kill it: Target customers are extremely price-sensitive (thread obsesses over $0.75 vs $5 per 1k), so a paid middleware may be rejected in favor of free DIY scripts and open-source kits like searxng-ai-kit.

Start

First three steps: Build a harness that runs a fixed query/URL set weekly against 6+ search/fetch APIs and scores cost, latency, content quality and anti-bot failures.
Publish a public leaderboard page to capture SEO and email signups from agent developers.
Ship a paid proxy endpoint that accepts a search/fetch request and routes it to the best-scoring provider per query type, with usage and savings reporting.
Source discussion: DeepSeek search and fetch – $0.75 per 1000 successful requests ↗ 477 points233 commentsFrom the commentsLinked article