Design your system.
Simulate it.
See where it breaks.
Like a flight simulator for system design. Build visually, run real traffic through it, and find the bottlenecks before they find you.
Free to use. Works in any browser. Mobile PWA available.
As featured on
Top 20 launch out of 569, alongside launches from OpenAI, Google, and Anthropic.
Add a component. Rewire the flow. See what changes.
Design decisions have consequences. Watch them play out in real time.
Design anything
7 universal blocks that model any domain. Backends, kitchens, hospitals, factories. No infrastructure jargon required.
- ✓Drag, drop, and wire components
- ✓12 behavior modes (queues, retry, circuit breakers...)
- ✓Start from 16 templates or a blank canvas
See it break
Deterministic simulation runs traffic through your design. Watch queues fill, requests drop, and bottlenecks form.
- ✓Animated request flow with live stats
- ✓Scrub the timeline to inspect any moment
- ✓Stress test with traffic spikes and outages
Your AI design partner
Chinilla AI teaches you while you build. It reads your simulation, explains what went wrong, and walks you through the fix. It proposes, you decide.
- ✓Paste code, papers, or specs and get a visual diagram
- ✓AI-generated PRD spec sheets from your design
- ✓Explains the why, not just the what
Connect a GitHub repo.
Point Chinilla at any public GitHub repository and it scans the codebase, infers the architecture, and produces a polished diagram. Publish it as a live link or embed the iframe straight into your README or docs site.
Works best on small-to-medium repos. Large monorepos may need manual touch-up after import.
Paste your code. Get a living diagram.
Drop in a Python script, a YAML config, or any source file. Chinilla AI reads your code, extracts the architecture, and builds an interactive diagram you can simulate right away.
Best with single files or focused modules. For large codebases, paste the core file.
Have a research paper or spec instead? Upload it to Chinilla AI and describe what you want to design.
Export anything. Use it anywhere.
Your designs aren't trapped in a tab. Export a PNG for your GitHub README, an SVG for crisp docs, an animated GIF of your simulation running (Pro), or grab the Python code to keep building outside Chinilla.
Free tier covers the core experience. See what Pro adds →
Built for system design interviews.
Load a real interview problem, run the simulation, and see if your design holds. Each template is fully wired with realistic metrics, behaviors, and costs.
Design together. Live.
Start a session, share the link, and design the same canvas with a teammate in real time. See each other's cursors, edits, and selections. Then flip to Interview mode to run a mock system-design round with a built-in rubric.
Pro feature. 1-on-1 sessions (you + a guest). Your project survives when the session ends.
Design it. Simulate it. Understand it.
Free to start. No credit card required.
Try a live simulationWho Chinilla is for. And what it is not.
Picking the right tool starts with knowing where it fits. Here is the honest version.
Built for
- ✓System design interview prep. Load a problem, build a design, see if it holds.
- ✓Learning architecture. Watch packets flow through queues, retries, and circuit breakers in real time.
- ✓Whiteboarding new services. Validate topology and capacity before writing code.
- ✓Teaching and content. Embed live diagrams in docs, READMEs, and lessons.
- ✓Visual thinkers. Diagrams that respond instead of staring back at you.
Not for
- ×Production load testing. Use k6, Gatling, or Locust for hitting real services.
- ×Kubernetes or cloud deployment views. Use Lens, k9s, or Cloudcraft.
- ×Observability or APM. Use Datadog, New Relic, or Grafana.
- ×Wire-level network simulation. No jitter distributions, no GC pauses, no kernel scheduling.
- ×Capacity-planning a real fleet. The numbers you put in are the numbers you reason with.
Chinilla is a design + learning environment for reasoning about topology, throughput, capacity, and failure modes. Read how the engine works →
Chinilla by the numbers
No vibes. Just what is in the box.
Frequently asked questions
The actual answers, not marketing copy.
What is Chinilla? ↓
A browser-based system design simulator. You build architectures from 7 universal component types, attach 12 programmable behaviors (queues, retries, circuit breakers, rate limits, and so on), and run a deterministic discrete-event simulation that shows packets flowing through your design in real time. Used for interview prep, architecture brainstorming, teaching, and stress-testing designs before writing code.
How does the simulation engine actually work? ↓
Discrete-event, not queueing-theory closed-form. The runtime identifies entry points, injects seed packets, processes one topological layer per step, applies each component's behavior (queue, filter, drop, delay, retry, and so on), and routes outputs to downstream connections. Backpressure kicks in at 80% queue capacity. Queue overflow drops the oldest packet (FIFO). Random behaviors use a fixed seed (42) so results are reproducible. Full engine docs →
What is Chinilla NOT? ↓
Not a load tester for production services (use k6, Gatling, or Locust). Not a Kubernetes or cloud deployment visualizer (use Lens, k9s, or Cloudcraft). Not an APM or observability platform (use Datadog, New Relic, or Grafana).
Does not model network jitter distributions, garbage collection pauses, cache coherence protocols, or kernel scheduling. The numbers you reason with are the numbers you put in.
It is a design and learning environment for topology, throughput, capacity, and failure modes. That is the entire scope.
How accurate is the simulation? ↓
Accurate for what it models: throughput, capacity, processing time, queue depth, drop and filter rates, retry counts, rate limits, circuit-breaker trips, and conditional branching. The deterministic seed means the same design plus the same parameters always produces the same result, so debugging and before/after comparisons are meaningful. Treat it as a high-fidelity topology and behavior simulator, not a wire-level network simulator.
How is this different from Excalidraw or diagrams.net? ↓
Excalidraw and diagrams.net are static drawing tools. The boxes do nothing. In Chinilla the components have behavior, capacity, and throughput; you connect them and packets actually flow. If you want a picture for a slide, draw it elsewhere. If you want to find out whether the design holds under load before you build it, use Chinilla. Detailed comparison →
Is Chinilla really free? ↓
Yes. Free tier: 3 cloud projects, full simulation, all 7 components, all 12 behaviors, all 16 templates, PNG/SVG/Python/spec export, and 1 AI-generated design via the wizard. Pro is $15/month and adds 500 AI credits, GitHub repo-to-diagram, animated GIF export, unlimited projects, learning modules, and stability scoring with Monte Carlo runs. Pricing →
Does it work on mobile? ↓
Yes. Installable mobile PWA at /m/workspace with a touch-optimized canvas viewer, pinch-to-zoom, tap-for-component-details, and AI chat (Pro). Editing and simulation run best on desktop, but the mobile experience is a real first-class viewer.
What about my data and privacy? ↓
Designs. Stored in Supabase Postgres on AWS. The underlying disks are AES-256 encrypted at rest. Row-level security restricts each row to its owning user.
AI features. Send your canvas state and prompt to xAI Grok under their privacy policy. We do not log AI request content. We do not sell data.
GitHub repos. Your OAuth token is never stored in our database; it lives only in your Supabase auth session and is used at request time. We do not clone or store your source files. We store only repo metadata (owner, name, branch, last commit SHA, public/private flag) and the resulting diagram. Repo analyses are not cached or shared between users; every re-analyze runs a fresh pass.
"This project was inspired by pottery, the moon, dinosaurs, and DNA-like love."