Production-ready AI workflow orchestration

The control plane
for AI workflows.

Hard spend caps, enforced. The budget blocks the model call before it happens — not an alert after the money is gone. Every routing decision, cost and policy verdict is recorded and exportable.

Start free — 10 runs, no card See pricing

Free tier, then $29/month · Live demo · GitHub

Prompt Input

"Launch our announcement in Spanish: press copy, a video script, a social post and a voiceover script, plus the narrated voiceover and a launch visual."

↓ decompose & classify tasks

announcement

scripts × 3

voiceover

visual

↓ route to models

claude-opus-5

spanish announcement

✓ done

claude-sonnet-5

video · social · voiceover scripts

✓ done

gpt-4o-mini-tts

spanish narration

✓ done

flux/schnell

launch visual

✓ done

Unified Output · 41s · $0.07

announcement.md scripts.md voiceover.mp3 launch-visual.png

The Gap

You find out what it cost after it ran.

The tooling exists for individual model calls. It doesn't exist yet for the space between them — where prompts branch, models diverge, and outputs need to reconverge.

🧩

Budgets That Warn Instead of Stop

Most tooling tells you about overspend after the model call. A retry loop on an expensive model, or a run that fans out into five image generations, is already billed by the time an alert fires. Worse, two runs starting at the same moment each check the remaining budget, each see room, and both proceed. Enforcement has to happen before dispatch and it has to be atomic — otherwise it is reporting, not a cap.

🔭

Failure Without a Stack Trace

When a multi-step workflow produces garbage output — or silently returns nothing — you have no execution context to debug against. Which step failed? Which model was called? What was the exact input? Without a per-step trace, you're diffing outputs and hoping. There's no run.tasks[i].input to inspect.

🔀

Routing Logic Leaks Into Application Code

Picking different models for different subtasks — claude for long-form, gpt-4o-mini for terse summaries, perplexity for retrieval — means writing if/else in your app. Routing is now coupled to business logic, untestable in isolation, impossible to change without a redeploy, and opaque to anyone on-call at 2am.

Why a Control Plane

"Prompt chaining is not a system. Disconnected tools are not a platform. Real AI workflows need a layer that understands what each task requires, assigns the right model, and makes every decision observable — before, during, and after execution."

01 — Composition

A prompt implies a workflow. That structure should be explicit.

One instruction often implies several distinct tasks with different requirements. A control plane makes that structure visible and executable — your application code just submits the prompt.

02 — Routing

Not every subtask belongs on the same model.

Long-form writing, terse social copy, and structured extraction each have an optimal model. Hardcoding one model for everything is the fastest way to build and the slowest way to improve.

03 — Observability

A workflow you can't inspect isn't production-ready.

When output quality drifts or a task fails silently, you need a ledger: which task ran, which model was used, what the exact input and output were. Without that, you're guessing at causality.

Prompt

↓

Prompt Tornado Control Plane

decomposition

routing

execution

observability

↓

AI models

↓

Unified output

Example Workflows

One prompt. Multiple tasks. Unified output.

Each example shows what you put in, what Prompt Tornado does behind the scenes, and what comes out.

workflow_01

Multilingual Product Launch

Input: "Launch our announcement in Spanish: press copy, a video script, a social post and a voiceover script, plus the narrated voiceover and a launch visual."

One announcement becomes four distinct Spanish deliverables. The writing and the rewriting go to two different Claude models, the narration to a text-to-speech model and the visual to an image model.

prompt→ localize→ scripts→ voice + visual

announcement.md scripts.md voiceover.mp3 launch-visual.png

Steps executed

1.Spanish announcement→ claude-opus-5

2.Video, social & voiceover scripts→ claude-sonnet-5

3.Narration→ gpt-4o-mini-tts

4.Launch visual→ fal-ai/flux/schnell

Steps 4

Run time 41s

Cost $0.07

workflow_02

Vendor Decision Briefing

Input: "Compare three help-desk vendors for a 25-person company against a $500-a-month budget, and recommend one to trial."

Web research with sources, handed down a chain: a structured comparison, a risk review, a decision memo and a five-slide leadership briefing. Anything the research could not verify is marked as such.

prompt→ research→ compare + review→ memo + briefing

comparison.json risk-review.md decision-memo.md briefing.md

Steps executed

1.Research, with sources→ perplexity → gpt-5.5

2.Structured comparison→ gpt-5.5

3.Risk review→ gpt-5.6-sol

4.Decision memo→ claude-opus-5

5.Five-slide briefing→ claude-opus-5

6.Illustration→ fal-ai/flux/schnell

Steps 6

Run time 226s

Cost $0.58

workflow_03

Support Inbox Action Plan

Input: "Triage eight support tickets against our support policy, draft a reply to each for review, and brief the manager."

Tickets are triaged against an approved policy (one of them tries to override it), turned into structured records, answered with reply drafts and summed up in a manager's brief. Nothing is sent: every reply is a draft.

prompt→ triage→ records + replies→ manager brief

triage.md tickets.json reply-drafts.md manager-brief.md

Steps executed

1.Triage→ claude-opus-5

2.Ticket records (JSON)→ gpt-5.5

3.Reply drafts→ claude-haiku-4-5

4.Manager's brief→ claude-opus-5

Steps 4

Run time 47s

Cost $0.12

workflow_04

Billing Bug Fix & Independent Review

Input: "Find the revenue bug in our MRR code, fix it, write regression tests, and have someone independent check the fix."

Diagnosis, patch and tests come from one model; a model from a different vendor then reviews the fix against the spec. The tests are written, not run.

prompt→ diagnose→ patch + tests→ independent review

diagnosis.md billing_mrr.py test_billing_mrr.py review.md

Steps executed

1.Diagnosis→ gpt-5.6-sol

2.Patch→ gpt-5.6-sol

3.Regression tests→ gpt-5.6-sol

4.Independent review→ claude-opus-5

Steps 4

Run time 148s

Cost $0.29

Real production runs from September 2026, with the model that ran each step. The prompts, plans and unedited outputs are on GitHub, and the live demo runs the same four.

How it Works

Prompt in. Orchestrated result out.

Prompt Tornado turns a single instruction into a fully executed multi-step workflow — classification, routing, execution, and observation handled for you.

Submit a Prompt

Write a single natural-language instruction. It can be a compound task — no need to pre-decompose it yourself.

# POST /api/v1/runs
# Authorization: Bearer pt_live_…
{ "prompt": "Launch our
announcement in Spanish…" }
# → {"run_id": "00c67421…",
#    "status": "planning"}

Decompose & Route

The prompt is decomposed into discrete tasks. Each is routed to the appropriate model based on your workflow configuration.

# Tasks classified:
"announcement" → claude-opus-5
"scripts × 3"  → claude-sonnet-5
"narration"    → gpt-4o-mini-tts
"visual"       → flux/schnell

Execute & Unify

Steps run in dependency order, each receiving the outputs it depends on. Results come back as one output — not raw completions to reassemble yourself.

# GET /api/v1/runs/00c67421…
{ "status": "completed",
  "steps_completed": 4,
  "output": {
    "text": "A) Spanish…",
    "audio_url": "…" } }

Observe the Run

Every run produces a full trace in the app: which steps ran, which model handled each, its latency and cost, and the output at each step.

# same response
"usage": {
  "total_cost_usd": 0.0697 }
# per-step model, provider,
# latency and cost: the run
# page in the app

Workflow Audit Trail

Every run is a fully inspectable record.

Prompt Tornado logs every execution step — the model called, the provider used, duration per step, tokens consumed, cost, and the exact output. When something goes wrong, you have a ledger, not a guess.

Prompt Tornado — Run Details with Execution Audit Log

ed643882 · Vendor Decision Briefing

6 steps · 6/6 completed 226s $0.58 ✓ completed

Models routed in this run

perplexity/preset/medium openai/gpt-5.5 openai/gpt-5.6-sol anthropic/claude-opus-5 fal_ai/flux/schnell

Step execution trace — every decision logged

StepTaskModelDurationCostStatus

Step 1Research (two-stage)perplexity → openai/gpt-5.5126s$0.241

✓ ok

Step 2Structured comparisonopenai/gpt-5.534s$0.129

✓ ok

Step 3Risk reviewopenai/gpt-5.6-sol21s$0.053

✓ ok

Step 4Decision memoanthropic/claude-opus-526s$0.088

✓ ok

Step 5Five-slide briefinganthropic/claude-opus-517s$0.068

✓ ok

Step 6Illustrationfal_ai/flux/schnell4.9s$0.003

✓ ok

Unified Output

A sourced vendor comparison, a risk review, a 350-word decision memo, a five-slide leadership outline and an illustration, returned as one result. Every part is traceable to the step and model that produced it. Production run, 16 September 2026.

comparison.jsonrisk-review.mddecision-memo.mdbriefing.mdillustration.png

Output Quality

Quality, measured — not assumed.

Beyond planning accuracy, every text-producing task type is scored 1–10 by an independent LLM judge against task-specific rubrics — and deploys are blocked on regressions. Across 181 registry task types spanning 17 categories:

181 task types · 17 categories · LLM-judged · August 2026

↓ Download the full eval report (PDF)

181

Task types evaluated

across 17 categories · scored every release

8.64

Avg quality score

independent LLM judge · 1–10 scale

91.7%

Quality pass rate

judged text-producing task types

181/181

Routing checks passed

one test case per task type · correct routing

Before Anything Runs

The plan is visible, priced and checked before any model runs.

Quality scoring measures what comes out. This is the step before it: a compound prompt becomes typed steps with dependencies, each with a ranked list of models. You can preview the plan, see its estimated cost and edit it. Then every step passes your spend cap and governance policy before it is dispatched.

Routing and provider figures from production, September 2026

Providers routed automatically

OpenAI · Anthropic · Perplexity · fal.ai · Stability AI · Runway · plus Google, ElevenLabs, Deepgram and AssemblyAI available

181

Task types recognised

each with a routing check · grouped into a small set of routing types

Pre-run

Spend cap check

the estimated cost is reserved before the run starts

Rules first

Repeatable planning

clear requests matched by rules · ambiguous clauses classified at temperature 0

Capabilities

What the control plane manages.

Orchestration is the easy half. These are the parts that decide whether you can put it in production — and they are on the free tier too.

🔒

Enforced Spend Caps

Every model call — text, image, audio, video — passes through one dispatch path that reserves budget first. A reservation the cap refuses stops the call before it is made, and reservations are atomic, so two runs starting at the same instant cannot both read "budget remaining" and both proceed. The one exception is an outage of the budget system itself, which on a paid plan lets the run through rather than blocking your work — stated in full on the pricing page.

🛑

Capability Kill-Switch

Quarantine a model or a provider and new runs stop selecting it immediately. A run already in flight fails closed on its next call to that target rather than dispatching to it — and the quarantine survives a database blip instead of evaporating.

📡

Execution Ledger

A full audit trail for every run: task-level tracing, model routing decisions, exact inputs and outputs, latency, token counts and real cost — all queryable after the fact, and exportable.

🔀

Per-Task Model Routing

Each task goes to the model best suited for it, across six providers in automatic routing. Routing lives in the control plane rather than in your application code — change a model or a ranking without a redeploy.

🛡️

Fallback Routing

Every task type has a ranked list, not a single model. If the primary fails or is unavailable, the run continues down the list — your application code doesn't need to handle the exception.

🧩

Prompt Decomposition

A single compound prompt becomes a set of typed, discrete tasks. The decomposition is explicit and inspectable before anything runs — not an opaque chain of inferences.

The control plane
for AI workflows.

You find out what it cost after it ran.

One prompt. Multiple tasks. Unified output.

Start building reliable AI workflows.

Prompt in. Orchestrated result out.

Watch a live workflow run.

Every run is a fully inspectable record.

Quality, measured — not assumed.

The plan is visible, priced and checked before any model runs.

Works with leading AI models

What the control plane manages.

Clear, citable pages for what Prompt Tornado does.

AI workflows deserve
a real control plane.

The control planefor AI workflows.

You find out what it cost after it ran.

One prompt. Multiple tasks. Unified output.

Start building reliable AI workflows.

Prompt in. Orchestrated result out.

Watch a live workflow run.

Every run is a fully inspectable record.

Quality, measured — not assumed.

The plan is visible, priced and checked before any model runs.

Works with leading AI models

What the control plane manages.

Clear, citable pages for what Prompt Tornado does.

AI workflows deservea real control plane.

The control plane
for AI workflows.

AI workflows deserve
a real control plane.