Chinilla vs Temporal AI

Chinilla is a modeling tool. Seven universal component types (Person, Step, Storage, Decision, Trigger, Tool, Channel) drop onto a canvas, and eight programmable behaviors — passthrough, filter, split, delay, retry, circuitbreaker, batch, replicate — attach per node. Queueing is universal with settable capacity; backpressure engages at 80% and FIFO overflow drops the oldest packet. Runs are deterministic (seed 42), processed topologically layer by layer with real-time flow visualization, plus a timeline scrubber, Monte Carlo with 95% confidence intervals, parameter sweeps, stability scoring with baseline diffing, and stress testing with outages and latency injection. Output is understanding; nothing deploys.

Temporal is a production runtime. It persists Workflow state at every step so a crashed worker or abandoned session resumes where it stopped, with no checkpointing code. Activities retry automatically with backoff, four timeout classes, and heartbeating. Signals, Queries, and Updates mutate running Workflows mid-flight; Workflow Streams add live interactivity; durable timers sleep for months. AI coverage is concrete: durable agents via the OpenAI Agents SDK and Google ADK, an OpenAI Agents SDK sandbox integration in public preview (2026-04-16), Serverless Workers for AWS Lambda (public preview 2026-08-03), AWS Bedrock AgentCore (pre-release 2026-09-18) and Cloud Run (public preview 2026-10-05). Standalone Activities went GA across six SDKs (2026-09-15) as a durable job queue; the Rust SDK is GA.

The overlap is one concept — retries and circuit breakers. In Chinilla those are simulated behaviors to observe; in Temporal they're runtime guarantees that keep real work alive. Context limits differ too: Chinilla's own docs steer industrial agent-based modeling to AnyLogic or Arena, and wire-level simulation (jitter distributions, GC pauses, kernel scheduling) is out of scope.

Both list freemium, but they monetize different things. Chinilla's value is bounded by simulation runs and designers — a paid tier buys more modeling headroom, not infrastructure. Nothing it does consumes production compute, so its cost ceiling is per-seat learning and design review.

Temporal's freemium is open-source-core: the Temporal Service and SDKs are self-hostable, and the paid path is Temporal Cloud, where you're buying operational guarantees that show up in the news — Cloud UI Strict Session Mode GA (2026-09-18) with a 15-minute inactivity timeout and 12-hour maximum session for SAML users, and Serverless Workers for Cloud Run that scale to zero when the queue empties (public preview 2026-10-05). Scale-to-zero matters for pricing: idle workers stop accruing worker cost, though durable state and action throughput still drive the bill.

What's missing from both fact sets is per-unit numbers — no published seat price for Chinilla and no per-action or per-connection rate for Temporal Cloud. Don't sign either on vibes: price a Chinilla scenario against the size of your design team, and price Temporal against your Workflow action volume plus the enterprise features (SAML SSO, audit logging, SOC 2/HIPAA, SLA-backed support) that a regulated buyer is effectively forced into. A simulator that costs tens per seat and a Cloud contract that costs per thousand actions aren't comparable line items, which is the whole point.