Solo Developer Builds Budget Web Search AI API to Challenge Perplexity

One Developer's Big Ambition

In the AI search space, Perplexity is arguably one of the most closely watched products among developers today. It combines large language models with real-time web search to deliver cited, accurate answers. But as usage scales up, its API costs have become a real pain point for many developers — often running between $5 and $12 per 1,000 queries.

Recently, a solo developer shared their project on Reddit: a web search AI service called MiAPI (miapi.uk). The developer claims it delivers accuracy on par with Perplexity at just $3.50 per 1,000 queries — roughly one-third to one-half the cost of Perplexity, making it an attractive option for cost-conscious indie developers and early-stage startups.

MiAPI: Key Numbers and Performance Comparison

According to the developer's own data, MiAPI shows competitive strength across several dimensions:

SimpleQA Benchmark Performance

The developer reports an 82% accuracy rate on the SimpleQA benchmark, claiming this surpasses Perplexity on the same metric. SimpleQA is a benchmark introduced by OpenAI to evaluate language models on factual question answering — specifically testing how accurately models respond to short, well-defined factual questions. Since this type of question is prone to exposing hallucination tendencies, an 82% score, if verified, is genuinely noteworthy.

General Query Accuracy

Beyond SimpleQA, the developer also claims 95%+ accuracy on general queries — a broader category covering the kinds of everyday, search-style questions real users typically ask.

Cost Comparison at a Glance

Metric	MiAPI	Perplexity
Price per 1,000 queries	$3.50	$5–12
SimpleQA accuracy	82%	Claimed lower than MiAPI
General query accuracy	95%+	No comparison provided

One important caveat: all of these figures come from the developer's own statements and have not been independently verified by any third party. Readers should apply healthy skepticism when evaluating claims like these.

Why Can an AI Search API Be This Cheap?

The original post doesn't go into architectural detail, but from an industry perspective, the cost of a web search AI service typically breaks down into three components:

1. Search engine query costs: Whether self-indexing or using third-party search APIs (like Bing or Brave Search), there are fees involved.
2. LLM inference costs: Synthesizing search results into coherent answers requires calling a large language model — often the biggest cost driver.
3. Infrastructure and operations: Servers, bandwidth, and so on.

As a solo dev project, MiAPI likely keeps costs down by selecting higher-efficiency open-source models, optimizing search call strategies, or using more economical inference solutions. Without the overhead of a large organization, this kind of lean architecture genuinely has the potential to support lower pricing.

The Real Challenges Facing Solo AI Developers

In the post, the developer openly describes himself as a solo developer and actively asks for advice on "marketing" and "API improvements" — a candid reflection of the struggles indie AI product builders commonly face:

The Tech Works, But Growth Is Hard

Even with pricing and performance advantages, reaching the right users remains a massive challenge. Perplexity has substantial funding, brand recognition, and marketing resources behind it. Solo developers typically rely on community word-of-mouth and organic traction through platforms like Reddit.

Trust Takes Time to Build

When developers choose an API service, they're not just looking at price and performance — they care deeply about stability, reliability, and long-term sustainability. A project maintained by a single person naturally raises concerns among enterprise users about whether the service will still be around in a year. This is a critical issue products like MiAPI need to address head-on.

No Independent Benchmark Validation

Self-reported accuracy figures alone are unlikely to convince professional users. Providing publicly reproducible evaluation methodology, open test endpoints, or third-party audits would significantly boost credibility.

What This Means for Developers Choosing an AI Search API

Zooming out, MiAPI's emergence reflects a broader trend in AI infrastructure: as open-source models and search technology become more accessible, the barrier to building a competitive AI service is dropping. More and more solo developers can compete with major platforms in niche markets through differentiated pricing and precise positioning.

For developers currently evaluating AI search APIs, this kind of product is worth watching — but here's what to keep in mind:

• Start with small-scale testing: Validate accuracy and latency against your actual use cases, not just marketing claims.
• Check SLA and stability: Understand the service's uptime guarantees and support responsiveness.
• Assess migration costs: Make sure the API is well-designed enough that switching providers down the line won't be a major burden.

Closing Thoughts

MiAPI is a snapshot of what it looks like when a solo developer takes a bold swing in the AI wave. With lower pricing and claimed solid performance, it's making a play for a slice of the web search AI market that Perplexity currently dominates. The data is still awaiting third-party verification, and the product's long-term sustainability remains to be seen — but this kind of ambition to challenge established players is exactly the force that drives continued cost reduction and efficiency gains across AI infrastructure.

For cost-sensitive developers, having another option is always a good thing — as long as you do your due diligence before committing.