How Spider Went to Market: What Worked, What Didn’t, and What We’d Do Differently

Most developer tool GTM advice is written by marketers who have never shipped a product. This is written by the person who built Spider and had to figure out how to get people to use it.

Spider started as a Rust crate. No cloud service, no landing page, no pricing. Just a library on crates.io that crawled websites fast. The path from there to a production API serving companies like Zapier and Swiss Re was not a straight line. This post covers what actually happened.

Starting point: a crate with no users

The spider crate was published to crates.io in 2023. The initial version did one thing: async web crawling in Rust with configurable concurrency. No markdown conversion, no proxy support, no AI features. Just fast HTTP fetching with robots.txt compliance.

The first users found it through crates.io search and Rust community forums. The Rust ecosystem is small enough that a well-written crate in an underserved niche gets noticed organically. We did not do any marketing. The README was sparse. The docs were auto-generated.

What we learned: the Rust ecosystem is a fantastic wedge for developer tools. Rust developers are early adopters by temperament. They care about performance. They read source code. And they tell other people about tools they like. Our first 500 GitHub stars came entirely from organic Rust community discovery.

The decision to build a cloud API

The crate was getting stars but not making money. More importantly, the feedback from users was consistent: “I love the speed, but I don’t want to manage proxies and anti-bot bypass myself.”

This is the classic open source to SaaS transition. The library solves the core technical problem. The managed service solves the operational problem. The gap between “I can crawl 50,000 pages per minute” and “I can reliably crawl 50,000 pages per minute across sites that don’t want to be crawled” is where the business lives.

We launched spider.cloud with a simple API: send a URL, get content back. Pay per page, no subscription. The decision to avoid subscriptions was deliberate and opinionated.

Pricing: what we got right and wrong

What worked: pay-as-you-go with no subscription

Every competing scraping API uses subscription tiers. $29/month, $99/month, $299/month. You pick a tier, you get a credit bucket, unused credits expire at the end of the month.

We went the other way. Buy credits, use them whenever, they never expire. No monthly commitment. The minimum purchase is $5.

This worked for three reasons:

1. Lower barrier to trial. A developer evaluating scraping tools can try Spider for $5 without committing to a monthly subscription. They compare it to competitors in an afternoon instead of signing up for a trial period they forget to cancel.

2. Usage-aligned incentives. With subscriptions, the vendor benefits when you buy a plan and don’t use it. With pay-as-you-go, we only make money when Spider delivers value. This forces us to make the product worth using, not just worth signing up for.

3. Enterprise procurement is easier. A $500 one-time credit purchase goes through a credit card. A $500/month SaaS subscription goes through procurement review. We closed enterprise deals months faster because the initial purchase was a discretionary expense.

What we got wrong: not having clear volume pricing earlier

For the first six months, we had one price: the per-page rate. Large customers asked for volume discounts and we handled them ad hoc over email. This created friction in the sales process and made it hard for customers to forecast costs.

We eventually added a 30% credit bonus for purchases over $4,000. Simple, transparent, no negotiation needed. We should have done this from day one.

The lite_mode discovery

Our most impactful pricing decision came from watching user behavior, not from market research. We noticed that a large segment of customers were crawling documentation sites and blogs — well-structured static HTML that did not need full-fidelity processing.

We added a lite_mode flag that skips some of the heavier content analysis and roughly halves the per-page cost. Customers who were crawling 500K pages per month of static documentation immediately cut their bills in half. Several customers who had been building their own scrapers for cost reasons switched to Spider once lite_mode made the economics work.

The lesson: watch what your users actually do, not what they say they want. Nobody asked for lite_mode. They asked for lower prices. The product answer was better than the pricing answer.

Distribution channels: what actually drove adoption

Channel 1: Framework integrations (highest ROI)

The single highest-impact GTM investment we made was building native integrations into AI frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, FlowiseAI, Composio, Agno, and Julep.

Each integration is a document loader that makes Spider a first-class data source inside the framework. When a developer follows a LangChain tutorial on building a RAG pipeline, Spider is one of the options in the “document loaders” section of the docs. When a CrewAI user needs their agent to browse the web, Spider is in the tools list.

This is pull distribution, not push. We are not advertising to developers. We are showing up in the place where they are already building something, at the exact moment they need a scraping tool.

The integration work itself is minimal — each one is a thin wrapper around our API. The impact is disproportionate because framework docs are where developers make tool decisions.

Channel 2: Open source as top-of-funnel

The crate is MIT licensed. This was a business decision as much as a philosophical one.

Developers discover Spider through the open source crate, try it locally, and then graduate to the managed API when they hit the operational complexity of proxies and anti-bot at scale. We see this pattern repeatedly in our conversion data: crate user → API trial → production customer.

The MIT license removes friction from this funnel. No license review, no legal approval, no concerns about AGPL contamination in their stack. They can start using Spider with zero commitment and upgrade when they are ready.

Channel 3: Data destination integrations

Less obvious but equally important: we built integrations with data destinations. Spider can push crawl results directly to Amazon S3, Google Cloud Storage, Azure Blob Storage, Google Sheets, and Supabase.

This matters because it lets Spider fit into existing data pipelines without custom glue code. A customer who stores everything in S3 can add Spider as a data source with a single configuration change. The pipeline goes from “Spider API → custom script → S3” to “Spider API → S3.” Every removed step is a removed reason to build something custom instead.

What didn’t work: traditional content marketing

Our early blog posts were generic “how to web scrape with Python” tutorials. They ranked reasonably well in search but converted poorly. The readers were beginners learning to scrape, not production developers evaluating tools.

What converted better: specific, technical content aimed at developers who already know how to scrape and are looking for a better solution. Posts about cost comparisons at specific volume tiers, benchmark results they can reproduce, and architectural explanations of how the system works internally. The audience is smaller but the intent is higher.

We also learned that developer blog posts that read like marketing materials get ignored. The Hacker News audience in particular has a finely tuned detector for promotional content disguised as technical content. A post that says “our tool is 10x faster than everything” gets flagged and buried. A post that says “here is exactly how we built our crawler, here is what went wrong, here is what we measured” gets discussed and shared.

What we would do differently

1. Launch with both the crate and the API simultaneously

We launched the crate first, built an audience, then launched the API. In retrospect, we should have launched both on the same day. The crate-to-API conversion funnel works, but having the API available from day one would have captured users who wanted managed infrastructure from the start.

2. Build evaluation tools, not just the product

Our customers’ biggest pain point is not scraping itself — it is evaluating whether the scraping output is good enough for their use case. We should have built a web-based playground where you type a URL, see the markdown output, and compare it to the raw HTML side-by-side. This would have been the single highest-converting page on the site.

We are building this now. We should have built it first.

3. Publish benchmarks with reproducible methodology from day one

We ran internal benchmarks but did not publish them with methodology and source code until much later. Every week a developer asked “how does Spider compare to Firecrawl/Apify/ScrapingBee” and we sent them ad hoc comparisons over email. Publishing a reproducible benchmark with source code on day one would have short-circuited hundreds of these conversations and let the numbers speak for themselves.

4. Invest more in the no-code integrations earlier

We built the API and SDKs first because that is what we knew. But the Zapier and Pipedream integrations drive a surprising amount of adoption from non-developer users (marketing teams, data analysts, operations teams). These users have high-value use cases and long retention because they are not going to build their own scrapers. We should have prioritized no-code integrations alongside the SDK launches.

The GTM playbook for developer infrastructure tools

If you are building a developer infrastructure product, here is what we would recommend based on our experience:

Start with a genuine technical advantage. Ours is Rust-native performance. Yours needs to be something that is hard to replicate. If your product is a thin wrapper around commodity APIs, your GTM will be an uphill battle regardless of strategy.

Open source is a distribution channel, not a charity. MIT-license the core, build the managed service on top, and make the upgrade path frictionless. The open source users who never pay are still valuable: they file bugs, they write about the tool, and they recommend it to colleagues who do pay.

Integrate where developers already are. Framework integrations, package manager listings, IDE extensions, no-code platform connectors. Every place a developer can discover your tool without visiting your website is a distribution channel that compounds over time.

Price for usage, not commitment. Subscriptions create a barrier to trial and misalign incentives. Pay-as-you-go removes both. Your revenue will be lumpier, but your customers will be happier and your churn will be lower.

Write content that would be useful even if your company went out of business tomorrow. This is the Fly.io test. If your blog post only makes sense as a pitch for your product, it will not spread. If it teaches something genuinely useful, people share it, discuss it, and remember that your company knows what it is talking about.

Measure everything, but trust qualitative signals early. We did not have attribution tracking for our first 100 customers. We learned where they came from by asking them in onboarding. “I saw Spider in the LangChain docs” came up more than any paid channel we tried. Trust the pattern even before you have the data to prove it statistically.

Where we are now

Spider serves companies ranging from early-stage startups building their first RAG pipeline to enterprises processing millions of pages per month. The technical moat is the Rust engine. The business moat is the ecosystem — eight AI framework integrations, six data destination connectors, three no-code platform integrations, and an MIT-licensed crate that lets anyone self-host.

We got a lot of things wrong along the way. We launched too late, priced too simply, and wrote too much content that nobody needed. But the core thesis held: developers building AI applications need web data, and the tool that delivers it fastest and cheapest wins.

The scraping market in 2026 is crowded. Firecrawl, Crawl4AI, Apify, and others are all building good products. The competition is making everyone better. We think the best approach is to be open about how we operate, publish real numbers, and let developers decide for themselves.