Privacy-first synthetic data for OpEx avoidance

Local data engineering is now fast and secure.

Meet the prototab DE browser workbench.

PySpark compliant notebook without the bloated Spark runtime. No sensitive data downloads. Our Dynathesis™ physics-based engine automatically generates join-preserving synthetic data directly to your workstation, letting you iterate and debug locally. When the logic is ready, run your code against real data in Databricks with one click, while Canvient™ keeps real results out of the browser. The cloud meter only runs when the work requires it.

Get started Open the OpEx calculator

Up to 70%

Avoidable data workflow cost

$50K

Flat. Per year.

90 days

Full refund window

prototab DE workbench showing parquet files, joins, filters, and output data.

prototab DE notebook with SQL and PySpark connected to Snowflake, showing query output.

prototab admin console: IT organization management and AgentDE configuration.

What it is

A PySpark and SQL notebook that runs on your own computers.

prototab DE makes synthetic data on demand, so your team builds and tests pipelines locally, at no cloud cost, and only reaches for Databricks or Snowflake when there's real work to run. Same code, same dialects. The cloud bill stops paying for the practice runs.

Runs locally

In the browser, on the engineer's own machine. No cluster to spin up, works offline.

Synthetic on demand

Privacy-safe stand-in data that keeps your joins, so iteration does not need real records.

Same code, same dialects

Spark, Snowflake, Databricks, DuckDB, Hive. What you build locally is what you run in the cloud.

What it does

Generate. Develop locally. Deploy when ready.

Integrated with Databricks, Unity Catalog, Snowflake, and cloud storage. Built by data engineers, for data engineers.

Generate

One click produces synthetic data that keeps the structure, joins, and statistical shape of your real data. Fully automated. No tuning, no PhD required.

Develop locally

Engineers prototype, debug, and test on their own workstations using synthetic data. No cloud compute. PySpark and SQL. Works offline.

Deploy when ready

When the code is validated, run it remotely in Databricks, Snowflake, or a prototab container against real data. Results return through Canvient™, so real data never lands in the browser.

Develop locally is where the savings happen

Early exploration and testing run on the engineer's own machine with synthetic data, so cloud compute sits idle until you actually need it. Savings start on day one. Synthetic data is local, so your team keeps working offline. No internet, no VPN. The airplane is a productive place again.

We don't compete with Databricks and Snowflake. They're more powerful and more expensive. Save them for when you really need them. What we add is choice and control over when the meter runs.

The mechanism

Privacy does not cut the cloud bill. Workload displacement does.

Synthetic data makes real data replacement safe. The integrated workbench makes workload displacement practical. The avoided cloud workload is the OpEx reduction.

Privacy assurance

Each synthesis job produces evidence: PII remediation, privacy-risk indicators, utility metrics, join preservation, and audit-ready certificate details.

Real data replacement

Safe synthetic data stands in for production data during iterative work that does not require real records or production-adjacent access.

Workload displacement

Exploration, development, testing, and debugging move into a local workbench instead of running every loop through a metered cloud platform.

OpEx avoidance

Cloud platforms stay in place for production truth. prototab DE reduces avoidable usage where the cloud meter should not have to run.

Keep the cloud for production truth. Use synthetic data for iteration.

Three buyers, one coherent case

The CFO sees avoided spend. The CISO sees reduced exposure. The CIO sees a customer-owned deployment.

This is not a rip-and-replace story. prototab DE augments Databricks, Snowflake, cloud data lakes, and regulated data platforms by moving the work that does not need real data into a safer, lower-cost place.

"You watched the bill climb every quarter and couldn't say exactly why."

For the CFO

OpEx avoidance with a short payback window.

Fixed price, unlimited use, and a calculator-friendly savings mechanism.

Real data replacement reduces the cloud workload tied to iterative data work.
Workload displacement turns metered cloud loops into lower-cost local work.
Guaranteed payback posture 90-day full refund, no questions asked.
Preserved ROI priced low enough that the vendor does not consume the savings.

"Your data can't leave the building, so half the modern tooling is off the table."

For the CISO

Privacy is the trust layer, not an afterthought.

The cost case only works because the synthetic data is serious enough to replace real data for non-production work.

Privacy-evidenced generation post-job certificates include controls, metrics, and mappings.
Vendor-blind by design no telemetry, callbacks, monitoring, user tracking, or job logs.
Regulated workforce enablement offshore, partner, and contractor teams work without production-data exposure.
Our breach should not become yours we do not host your source data, synthetic outputs, notebooks, or history.

"Every platform takes a quarter to stand up. You want one that's done on day one."

For the CIO

Turnkey means the product is finished.

A customer-deployed workbench with admin controls, synthetic generation, notebooks, no-code workflows, and internal AI.

No platform replacement keep existing cloud platforms and production workflows.
Full admin UI operate under your identity, access, data, and security policies.
Relationship-first synthesis automated join discovery and referential integrity from raw files and tables.
Self-contained by design email or call when needed; otherwise your deployment is yours.

Why synthetic data has missed this use case

A generator alone is another line item. A workbench changes where the work happens.

The market established synthetic data for privacy. We use that trust foundation to open a second value tap: safe, lower-cost iteration.

Traditional synthetic data Often sells generation as the product and leaves the customer to find the workflow value.

Enterprise Synthetic Co. Bundles privacy-evidenced generation with the app teams use to create value immediately.

Model-first generation Can require tuning, privacy review, utility validation, and metadata prep before adoption.

Relationship-first generation Discovers joins, preserves referential integrity, and emits evidence with every job.

Consumption economics Per-seat, per-row, per-dataset, or high platform pricing can erase the savings case.

Fixed-price economics $50K/year, unlimited users and use, priced so the ROI survives procurement.

No real data in the browser

The browser should never become a data endpoint.

Conventional cloud data apps send real query results back to the user's browser. prototab DE changes the flow: the browser works with synthetic data for iteration, and when real execution is required, Canvient™ shields real results so real data stays on the right side of the trust boundary.

Dataflow comparison: the conventional cloud-data path versus the prototab path where the browser only receives synthetic data or an encrypted image.

Security feature, not UI decoration.

Browser-based data tools normalize real data landing on the endpoint. That creates exposure through browser extensions, developer tools, screen capture, cached memory, and local workstation policy gaps.

Dynathesis™ Privacy-evidenced, join-preserving synthetic data on demand for local iteration.

DAGJoin™ Automated join relationship discovery, so raw files become usable multi-table synthetic datasets.

Canvient™ Zero-persistence secure viewing for real-data execution. The browser receives synthetic data or an encrypted image, never raw real data.

The problem

Cloud costs are eroding your margins. You already know this.

+21.5%

annual public cloud spend escalation (Gartner forecast)

27%

of the cloud data-platform budget goes to dev and test (Flexera)

±15%

typical monthly forecast variance on usage-based cloud (Flexera)

Cloud cost tools monitor spending after it happens. They ration and audit, they don't reduce costs. We don't help you forecast better. We eliminate the variance.

Today

Variable. Unpredictable. Escalating.

Per-seat licensing
Usage-based billing
Cluster hours and egress fees
Monthly forecast variance
Annual price increases on renewal

You've been trained to think this is what data engineering costs. It isn't. It's what it costs when your vendor succeeds by you using more.

With prototab DE

Fixed. Predictable. Yours.

One price, $50,000 a year, flat
Unlimited users, unlimited data, unlimited use
Net 30, no invoice until about day 32
Customer-deployed, no metering, no telemetry
Full refund within 90 days, no questions asked

Convert your second-largest expense from an unpredictable variable into a known fixed cost.

Priced to preserve the ROI

How much? No obfuscation. No "contact sales."

We don't need lock-in to prove a short payback. So the price is simple.

$50,000

USD per year

Unlimited users · Unlimited use · Customer-deployed

No invoice until day 32 PO and net-30 invoicing, no card required. Provisioned after a brief review; first invoice about 30 days later.

90-day full refund No questions asked. If the value is not there, we do not keep the budget.

Why so affordable? You deploy and manage it. We do not host, meter, monitor, or track your work.

Support without the services trap Private forum, email, and direct founder access. We support the product, we don't sell support as the product.

Request access

One price. Unlimited users. Unlimited use. Regain the freedom of choice. It's time for an alternative to lock-in.

Capital preservation

prototab DE pays for itself in the first 30 to 60 days.

Most enterprises spend 27% of their cloud data-platform budget on dev and test. We replace that line item with a flat $50K. Prove the payback in billing cycles, not transformation programs.

Annual capital preserved

$300K-$2M+

Typical range for a 25-engineer team. Run your numbers in the OpEx calculator.

Monthly variance

A fixed price, versus ±15% on the usage-based cloud bill

Productivity gain

From eliminating cluster spin-up, VPN drops, and network latency

Offshore enablement

50-75%

Labor savings when synthetic data removes the sovereignty barrier

Three value streams from one fixed investment

1. Cloud dev/test elimination

Replace usage-based cloud spend on dev and test with $50K flat. Savings start day one.

2. Productivity recovery

5% recovered from local execution. No cluster cold-starts, no VPN drops, no cloud timeouts.

3. Offshore enablement

Synthetic data has no PII, so offshore teams gain access without the sovereignty constraint.

Sources: Gartner public cloud forecast (21.5% YoY); Flexera State of the Cloud (27% dev/test allocation, ±15% forecast variance); Atlassian Developer Experience Report (8+ hrs/week lost to inefficiencies); Statista IT Outsourcing Survey and HireWithNear 2026 benchmarks (offshore data engineer 50-75% savings).

Open the OpEx calculator

How it works

It runs in the browser. Here's the stack that makes that real.

No installation, no cluster, no server round trip. Credibility for the part that sounds too good to be true.

WebAssembly and Pyodide

Python and SQL engines run in the browser at close to native speed, inside the browser sandbox.

DuckDB in the browser

A fast analytical SQL engine runs locally. Columnar storage and vectorized execution handle large local files.

Multi-dialect SQL

Write Spark, Snowflake, Databricks, DuckDB, or Hive SQL. The translation layer, built on SQLGlot, converts between them, including CTEs and window functions.

Origin Private File System

Files up to about 20GB live in the browser's OPFS. Persistent, local, and available offline.

Service worker execution

Jobs run in a background service worker, so the interface stays responsive while work happens.

Built-in AI assistant

A local code assistant routed through your own enterprise LLM provider. No external data leakage.

Vendor-blind by design

Enterprise software without the hosted-SaaS tax.

We kept the modern UI, simple licensing, and fast deployment. We removed the hosted data plane, telemetry, usage metering, vendor-side workflow logs, and support dependency.

No customer data stored

Source data, synthetic data, prompts, notebooks, job logs, outputs, and usage history stay inside your environment.

No telemetry or callbacks

We do not monitor your instance, track users, collect activity analytics, or maintain remote access.

Customer-deployed and customer-managed

Your deployment is secured by your policies, identity provider, infrastructure controls, and change-management rules.

Nothing customer-specific to steal

Our hosted footprint is customer-agnostic app distribution and billing. Our breach should not become yours.

This is not marketing language. This is architecture.

×No cookies

×No fingerprinting

×No telemetry

×No tracking pixels

×No usage analytics

×No call-home

×No user data collection

×No idea how many users you have

Stripe stores your purchasing information. We store nothing about your usage. Your data is yours. We're not watching.

Self-contained by default

If software is truly turnkey, support should not be a second product.

prototab DE is built to be installed, operated, and evaluated by your team. Have a question? Email or call us. Otherwise, we are not in your environment.

Full admin UIManage tenants, users, cloud storage, LLM providers, security, jobs, audit settings, and certificates.

Private customer forumShare deployment notes and workflow patterns without public noise.

No paid support contract requiredWe support the product. We do not sell support as the product.

OSS-style deploymentDeploy like infrastructure. License like enterprise software. Operate under your controls.

Product family

prototab is a platform, not a single tool.

prototab DE is the data engineering surface. The platform extends to agentic authoring and local AI runtimes.

Available now

prototabDE

Data Engineering

The PySpark and SQL notebook with Synthetic Data on Demand. Local-first development, remote execution against real data, Databricks and Snowflake integration.

$50,000 / year flat

Coming next

AgentDE

Agentic Data Engineering

The agentic authoring layer. Natural-language task intake produces executable plans you review and approve. Author once with AI, run repeatedly without AI. Successful traces compile into deterministic workflows.

Flow + Notebook, co-equal surfaces

Companion runtime

prototab Engine

Local-first AI workspace

A browser-native local AI runtime. Run approved local models on CPU, Vulkan, or CUDA, with no remote API by default. Managed runtimes, integrated tools, enterprise-grade controls.

Local intelligence, no setup tax

DE · EE · TE · SE · ME, each sub-brand addresses a different stage of the data lifecycle. All run on the same execution and privacy posture.

Still unsure?

Request a demo.

See prototab DE and AgentDE Flow on a live walkthrough. Tell us how to reach you and we'll get it scheduled.

Do not wait for AI to lower your cloud bill.

Move the iterative work that does not belong on the cloud meter. Replace real data where real data is not required. Prove the payback in billing cycles, not transformation programs.

Why doesn't everyone price it this way? Because the companies selling you cloud compute make more when you use more. We don't sell compute. We sell you the tool, once, at a price you can read.

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