Chinilla vs Spider Cloud

Chinilla is a runtime, not a drawing tool. Seven universal component types (Person, Step, Storage, Decision, Trigger, Tool, Channel) get wired with latency, capacity and behavior, then a deterministic discrete-event engine (fixed seed 42) walks the graph topologically injecting packets. Eight programmable behaviors — passthrough, filter, split, delay, retry, circuitbreaker, batch, replicate — attach per node, and queueing is universal: capacity set anywhere, backpressure at 80% fill, FIFO overflow dropping the oldest packet. Analysis depth is the selling point: timeline scrubbing, Monte Carlo with 95% confidence intervals, parameter sweeps, stability scoring with baseline diffing, and stress runs with peak traffic, outages and latency injection. Its GitHub integration is the only listed one.

Spider Cloud sits on the other side of the stack entirely. It returns pages: single-page scrape to markdown, JSON, HTML, raw or plain text; site crawls streaming one JSONL line per finished URL; a web search endpoint with SERP results plus the underlying pages. Browser rendering executes scripts and reaches infinite scroll, with a stealth mode through a native browser engine. Prompt-based extraction returns named fields as JSON, and an extraction_schema parameter forces AI output to a schema; a relevance gate skips non-matching pages before billing. Full browser sessions run over WebSocket and the provider router can push requests to outside providers on your own keys. Its integration list is agent-shaped: LangChain, LlamaIndex, CrewAI, MCP, Claude Code, Cursor, Codex.

The overlap is zero: one simulates your own architecture, the other fetches other people's pages.

Neither tool publishes tier prices in the data here beyond both being freemium, so the honest comparison is about what each meter counts. Chinilla's cost driver, per its positioning, is simulation work — Monte Carlo runs, parameter sweeps, stress scenarios — against a free entry point for building and running diagrams. For a buyer, the question is whether simulation volume (repeated runs, sweeps, baseline diffs) stays inside a free tier or pushes you to paid.

Spider Cloud meters bandwidth and concurrency, which makes the unit economics legible in a different way: you pay for gigabytes fetched and parallel requests, not seats or runs. Its own not-for list flags two budget traps worth quoting — buyers assuming proxy mode or AI extraction are included in the $1/GB bandwidth rate, and sparse-budget projects overpaying on a flat Unlimited plan for a few thousand pages a month (Unlimited queues nothing server-side, so 429s with Retry-After are on you). Recent engineering posts frame efficiency gains: markdown output reportedly cut token cost 15x at the median versus raw HTML across ten pages, and a fine-tuning dataset walkthrough measured 396 training records for $0.05 from 60 documentation pages via one search and one crawl call. Those numbers show how cheap metered scraping can be — but they are Spider's own measurements, so validate on your pages.

Bottom line: Chinilla's bill scales with how much you simulate; Spider Cloud's scales with how much web you pull and how many