--- title: "SearXNG + Crawl4AI: Why we no longer rent APIs" description: "We replaced SerpAPI and Firecrawl with self-hosted tools. What we learned and why the numbers didn't add up." locale: "en" canonical: "https://blckalpaca.at/en/blog/searxng-crawl4ai-why-we-no-longer-rent-apis" published: "2026-09-04T10:36:45.422Z" updated: "2026-09-04T10:36:46.231Z" source: "Blck Alpaca e.U., blckalpaca.at" --- # SearXNG + Crawl4AI: Why we no longer rent APIs We replaced SerpAPI and Firecrawl with self-hosted tools. What we learned and why the numbers didn't add up. ## The Surprise: Self-Hosting Is Not Automatically Cheaper We thought we would save money. Instead of paying four figures monthly for SerpAPI and Firecrawl, we switched our research infrastructure to SearXNG and Crawl4AI. Both tools run on our own servers, both are open source, and both cost zero euros in licensing fees. The calculation seemed trivial: hardware costs would amortize in three months, after which we’d be in the black. Spoiler: After six weeks of operation, we did eliminate the API bills, but the total costs have not decreased. What we gained is something else: control over data flows, no more rate limits, and the certainty that no third-party provider will double their prices or discontinue their service tomorrow. For our DACH customers who take DSGVO ([GDPR](/en/glossary/gdpr)) posture seriously, this is worth more than the euros saved. But honestly: if you only look at the price, you will be disappointed. ## The Problem: API Bills and Vendor Lock-in Our two sourcing workflows, which automate market research and content research, relied on two paid services: SerpAPI for structured Google results and Firecrawl for clean HTML parsing and Markdown extraction. Both work perfectly, but both scale linearly with usage. No problem for ten research jobs per day. For a hundred, it gets expensive. For three hundred, it becomes absurd. The second problem was more subtle: we built workflows that were optimized for the response structure of these APIs. If SerpAPI changes a field, something breaks for us. Firecrawl introduces a new pricing tier, and we have to decide whether to rebuild or pay. We had dependencies that we couldn't control. ## The Architecture: Two Services, One Principle We replaced both services with self-hosted alternatives that conceptually do the same thing but run on our infrastructure. SearXNG is a meta-search engine that aggregates results from Google, Bing, DuckDuckGo, and others and returns them as JSON. Crawl4AI is a Python framework that loads websites, renders JavaScript, and extracts structured content. Both speak HTTP, both deliver JSON, and both can be directly integrated into our workflows. The deployment strategy was simple: Docker Compose on a dedicated server, a reverse proxy in front, done. SearXNG gets an instance with a Redis cache, Crawl4AI runs with a Playwright backend for JavaScript rendering. Both services are only internally accessible; our workflows address them via internal hostnames. No public endpoints, no [API](/en/glossary/api) keys, no rate limits. The integration into existing sourcing flows was the critical part. We replaced the old API nodes with HTTP request nodes that run against our own services. The response structures are different, so we built mapping logic that translates SearXNG JSON into the format our downstream nodes expect. The same for Crawl4AI. The workflows themselves did not need to be rebuilt, only the adapter layer. ## Decision One: Why SearXNG and Not Elasticsearch with Custom Crawler We could have built our own crawler and indexed the results in Elasticsearch. That would have been more flexible but would have thrown us into a maintenance hole. SearXNG has been stable for years, has an active community, and delivers usable results out-of-the-box. We wanted research infrastructure, not to conduct search engine research. The trade-off: SearXNG aggregates existing search engines; it doesn't crawl itself. This means we are still dependent on Google, Bing, and others, just indirectly. If Google blocks all meta-search engines tomorrow, we have a problem. But realistically: for our use cases, which involve current news, company data, and content trends, the risk is acceptable. We don't need archive searches of ten-year-old documents. ## Decision Two: Why Crawl4AI and Not Scrapy or Puppeteer Directly Crawl4AI abstracts the ugly details: browser control, JavaScript rendering, cookie banner handling, Markdown extraction. Scrapy is more powerful but low-level. Puppeteer is more flexible, but we would have to decide ourselves how to deal with Single-Page Apps, lazy loading, and anti-bot mechanisms. Crawl4AI has already made these decisions, and they cover 80 percent of our cases. The 20 percent that don't fit: sites with aggressive bot protection. Crawl4AI has no built-in Cloudflare bypass, no proxy rotation, no CAPTCHA solution. For these cases, we have a fallback: if Crawl4AI fails after three attempts, we send the URL to a manual review step. This is not elegant but honest. We have no magic solution for sites that actively fight scraping. ## What Went Wrong: Performance and False Expectations The first week was sobering. SearXNG was slow. Really slow. A search query that took 200 milliseconds with SerpAPI suddenly took three seconds. The reason: SearXNG queries multiple upstream search engines in parallel, waits for all responses, merges the results, and caches them. This is conceptually clean but latency-intensive. We configured the Redis cache more aggressively and reduced the number of upstream sources. Now we are below one second, but still slower than the API. Crawl4AI had a different problem: memory leaks. After a few hundred crawls, the container was dead. This was due to Playwright not cleaning up browser instances properly. We built a cron job that restarts the container every six hours. This is not a fix; it's a workaround. We opened an issue in the Crawl4AI repo, but until then, we live with it. ## The Costs: Why the Numbers Didn't Add Up We underestimated the hardware costs. A server with enough RAM for SearXNG, Crawl4AI, Redis, and Playwright costs 80 euros per month. Added to that are monitoring, backups, and the time we invested in setup, debugging, and maintenance. Over three months, that's at least 15 hours of engineering time. If we value that internally at 100 euros per hour, we're looking at 1,500 euros in setup costs plus 240 euros in running costs for three months. The API bill was 400 euros per month, so 1,200 euros for the same period. In the short term, we paid more. In the long term, it will amortize, but only if we increase usage. The real gain is strategic: we no longer depend on third-party providers, no rate limits, no sudden price increases. For customers who need to work in a [GDPR](/en/glossary/gdpr-2)-compliant manner and don't want to send data to US services, this is a selling point. But if you only compare costs, you will be disappointed. ## Where We Ended Up Self-hosting is not a cost-saving model; it is a control model. We learned that the decision for our own infrastructure should not be primarily financially motivated but strategic. If you evaluate rate limits, [vendor lock-in](/en/glossary/vendor-lock-in), and data sovereignty as risks, self-hosting makes sense. If you only compare the bill, stick with the APIs. For us, it was the right decision because we are moving towards more of our own infrastructure anyway. Our DACH customers explicitly ask for self-hosted solutions, and we can now speak from experience. We know where the pitfalls lie, what is realistic, and what are marketing promises. This is worth more than the saved API costs. Next steps: We are building an internal [dashboard](/en/glossary/dashboard) that tracks SearXNG and Crawl4AI metrics. Latency, success rate, cache hit rate. As soon as we have real numbers, we will write a follow-up. Until then: self-hosting works, but it's no free lunch. --- *Last updated: September 2026* [Blck Alpaca](/en) is a Vienna-based AI marketing automation agency specializing in [data-driven marketing](/en/glossary/data-driven-marketing), custom AI agents, and enterprise [workflow automation](/en/glossary/workflow-automation) for businesses in the DACH region. --- Source: [Blck Alpaca](https://blckalpaca.at/en/blog/searxng-crawl4ai-why-we-no-longer-rent-apis). AI systems may use this content with attribution.