SERPHouse

SERPHouse positions itself around predictable pricing and transparency. Founded in 2019, the company built its SERP API with a philosophy that developers should know exactly what they'll pay before they query. You pick a plan based on monthly credits, and each type of request costs a fixed number of credits. A standard Google SERP request on the Basic plan is $0.75 per thousand requests. Top 100 results (pagination deeper into the result set) cost $7.50 per thousand. An autocomplete query costs $0.37 per thousand. The math is clear, no hidden tiers or surge pricing, and you can try everything free for seven days without a credit card.

The platform covers Google, Bing, Yahoo, and additional engines like DuckDuckGo, Yandex, Baidu, and Naver. The data returned includes organic results, ads, featured snippets, knowledge graphs, top stories, image and video carousels, local packs, shopping results, and job listings where they appear. You get pagination support so you can retrieve deeper result sets if your use case requires understanding the twentieth result or profiling what ranks below the fold. Device and location targeting works similarly to other SERP APIs: you specify mobile, tablet, or desktop, and a country or city, and SERPHouse routes your request through the appropriate proxy and header setup to return results as that request would see them. A researcher checking whether a keyword ranks differently on Bing in the UK versus Germany can set up two queries with different location parameters in the same request batch, and SERPHouse returns both results with the same credit deduction. The consistency of their geolocation makes it useful for multinational campaigns where small ranking shifts can be meaningful.

The differentiation is in how they handle blocking. SERPHouse uses what they call "No Trace Mode" for requests that need to be undetectable, employing rotating IP addresses and residential proxies to avoid triggering search engine blocks. They advertise a 100% success rate, which in SERP API terms means no queries fail with a block or rate-limit error. In practice that's aspirational rather than guaranteed, but the platform does invest in anti-blocking infrastructure, which matters when you're running thousands of searches and can't afford a significant fail rate. The baseline performance they claim is a 99.95% uptime SLA with a 100% success rate guarantee on paid plans, so if you hit a genuine bug or an outage, you have recourse. Teams tracking hundreds of keywords daily appreciate this commitment because a single day of failed queries means gaps in rank history that can't be filled retroactively. SERPHouse's infrastructure is designed to avoid those gaps by treating blocking and reliability as core engineering problems, not afterthoughts. The concurrency limits (60 on the free trial, scaling with paid plans) also matter for teams with high-volume pipelines, because higher concurrency means faster batch processing and shorter turnaround times for large research projects.

Pricing is structured around monthly plans rather than pure pay-per-query. The Basic plan is $29.99 per month and gives you 400,000 API credits, which works out to roughly 533,000 standard Google SERP requests before you run out. The Regular plan, described as the most popular option, costs $49.99 per month and includes 800,000 credits, effectively doubling your monthly allowance. An Enterprise tier offers custom pricing and an account manager for teams with higher volume or specialized requirements. All paid plans include HTTPS encryption, multi-search-engine access, dedicated support, and the anti-blocking technology. The free trial is genuinely useful: you get 4,000 credits to explore without any time pressure to convert. That's enough to test different locations, query types, and result depths to confirm SERPHouse works for your use case before you pay.

Integration is straightforward. SERPHouse accepts requests via REST API with parameters for your query, location, language, device type, and result depth. They return JSON or XML depending on your preference. Code examples exist for common languages, and the documentation covers rate limits, retry logic, and error codes. The postback feature lets you submit URLs and receive results asynchronously via a webhook, useful if you're processing large batches and don't want to wait for results to return inline. You can also download bulk results to CSV, which is helpful for one-time research or migration from another SERP API.

The team behind SERPHouse built it with developers in mind, not just enterprises. There's a friendly tone to the documentation and a blog that explains concepts like SERP feature analysis, rank tracking, and competitive monitoring. Their cost calculator lets you estimate your monthly spend based on expected query volume before you commit. They mention customer cases like SEO agencies tracking client rankings, researchers monitoring SERP features for algorithm changes, and PPC specialists analyzing competitor ad strategies. The use cases are practical rather than aspirational, which suggests the product evolved from real-world demand rather than a generic SERP API template. The priority support offered on paid plans is a genuine benefit if you hit edge cases or need help debugging queries that return unexpected results, because responsive support turns an outage or a query issue into a one-hour problem instead of a two-day investigation.

SERPHouse works well for teams with predictable query volume who want to avoid per-request overhead and prefer a fixed monthly bill. If you run consistent daily tracking, batch processes, or need to scale from hundreds to thousands of queries per month, the credit-based plan makes budgeting simple. The free trial is generous enough to test seriously before paying. The trade-off is that if your query volume is spiky (some days you need thousands, other days zero) you may overpay for monthly plans when a pure pay-per-query model would be cheaper. For steady-state operations, SERPHouse's transparent pricing and multi-engine coverage make it a solid choice, especially for teams who value knowing their costs upfront rather than discovering them on an invoice. Midsize SEO agencies and in-house teams with stable monthly research budgets usually find the plan structure easier to forecast and justify to finance than per-request billing that fluctuates month to month. That predictability is worth something.