Cloud World Model

Simulate AWS, GCP & DigitalOcean without paying the bill

Software Engineering

Developer Tools

Development

Visit WebsiteSee on Product HuntTwitter ⧉

The per-step warning will help a human reading the run, but the agent only ever optimizes the scalar reward, so a warning buried in a report won't bend the policy. What worked for us was keeping a separate eval env with the 'free' dimensions like egress and cross-AZ switched back on, and scoring the trained policy only there. If its reward collapses on that env, you've caught a policy that overfit to the sim's blind spot before it ever ships. Same idea as a train/test split, just applied to the reward.

This hits a real pain point—our staging AWS bill quietly hit $400/month last quarter because someone left a NAT Gateway running. Which AWS services are fully simulated vs mocked? Specifically curious about Lambda cold starts, DynamoDB Streams, and S3 event notifications. If those three work accurately, this becomes a no-brainer for our CI pipeline.

The unmodeled dimensions are where an RL agent quietly cheats. You said egress and cross-AZ are not in the cost model yet, so an optimizer trained on that sim will not just ignore them, it will learn to exploit them: chatty cross-AZ topologies look free, so the policy you deploy ends up biased along exactly the axes the sim cannot see. 97% is reassuring for one config, but the agent searches for the 3%. Do you flag or penalize decisions that lean on unmodeled resources, or bound how far the agent can wander from validated regions?

simulating the cloud bill away is realy clever 🙌 fidelity is the whole game tho

The agent-training angle is interesting. I’ve wasted time spinning sandbox infra just to test small failure cases, so having cost + autoscaling + failure recovery in one sim feels useful. Curious how you keep provider-specific limits and pricing drift up to date?

When simulating highly stateful infrastructure setups (like managed DB clusters with strict VPC networking rules or IAM permission chains), how deeply does your local mocking layer mirror the cloud providers' internal API state validation? Does it execute structural validation against a custom internal schema engine, or parse translated Terraform/CloudFormation configurations directly?

Really cool - had a multi-cloud setup simulating in a couple minutes. One thing though: connecting resources took a few clicks each time (here's what I mean: Cloud World Model | createademo, ~0:10). Is there a faster way to wire them up - a drag-from-handle or keyboard shortcut? Would speed up building a setup a lot. Congrats on the launch!

Useful angle for teams that want to teach cloud tradeoffs without handing out real cloud accounts. I’d be interested in how close the cost/perf model stays to provider changes over time, since drift is usually where these simulators get hard to trust.

This is the one I keep coming back to, cost is the question that never really leaves the room, and almost always the hardest thing to pin down before you commit.

My real question is fidelity. The headline compute numbers are easy, every calculator gets those right. The bills that actually blow up are the hidden line items: egress and cross-AZ traffic, managed-service markups, spot vs committed pricing. Does the engine reach down to those, or just the sticker compute price? And the case that would really earn its keep: migration, where the egress to leave a provider ambushes everyone and never shows up in a "provider A vs B monthly" comparison until the invoice lands. Can the explorer model the transition cost, not just the steady-state side-by-side?

Genuinely love the concept, the chaos-engineering resilience score is a great touch too. Congrats on the launch! :)

@mathsociety I like this because cloud mistakes usually get expensive after you already deployed them. Being able to play with failure scenarios, cost, and scaling before touching real infra feels especially useful for learners and small teams that don’t have a proper staging setup.

The RL training API is the sharp end of this — and the part I'd push on. An agent is only as honest as its reward. Cost, latency, and resilience are in tension: minimize cost hard enough and the agent learns to ship something cheap and brittle that looks great right up until the zone outage you didn't simulate.

So, two things I'd want before trusting an agent's infra recommendation enough to act on it:

Is the reward multi-objective and user-weightable (I decide cost vs resilience vs latency), and does a run surface the tradeoff the agent chose — "cut 30% cost but dropped your resilience score from 8 to 5" — instead of just handing back one "optimal" config? The tradeoff being visible and mine to set is the whole ballgame.

The chaos-injection + resilience score framing is a great call too. Congrats on shipping, Kevin

Congrats on the launch! 🚀

Simulating cloud architecture before provisioning real resources is a very useful idea, especially for cost-heavy experiments and failure testing.

I'm curious: how close are the cost and performance predictions to real-world cloud bills after deployment? Do you provide any confidence score or comparison against actual usage data over time?

The RL training API is the part that grabs me - an agent is only as good as the sim it learns in. The capacity-aware engine modeling "real per-provider performance profiles" is where that lives or dies: are those profiles grounded in published benchmarks and vendor specs, or in measured telemetry, and how often do you refresh them? If the sim cost/latency drifts from the actual providers, an agent will happily optimize for the model instead of the cloud, so how do you validate fidelity against a real deployment?

Strong launch. The RL training API is the interesting edge. If an agent learns an infra optimization in simulation, I’d want the handoff receipt before deploy: resources changed, env/secret assumptions, failure case tested, and rollback path.

Do you expect agents to export a plan into Terraform/Pulumi, or stay inside the simulator?

this is going to be a massive hit if you could support the official sdk for each cloud. I'm currently using the go-sdk for AWS and GCP to interact with the underlying api, but if we can have a drop in replacement (similar to localstack) and then it's gonna be disruptive. hope to see this implement soon.

This is highly relevant for developers trying to architecture and test multi-cloud environments without burning budget early on. How accurate is the simulation when replicating complex networking constraints or IAM policies between AWS and GCP? Great launch!

the cost simulation is the part i need most. i blew $400 on an RDS instance i spun up for "testing" and forgot about for 11 days. nobody warned me.

how granular does the cost projection go? if i model a 3-tier app does it tell me i'm about to pay for an over-provisioned NAT gateway, or just give me a total bill estimate?

the value of cost tools breaks for me at the line item level. that's where i actually make decisions.

The chaos engineering part caught my eye, injecting a DB crash and getting a resilience score back seems really useful for catching weak spots before prod. Curious how close the cost estimates land to a real AWS bill in practice. Congrats on shipping!