MLOps platform · Python authoring · Rust execution

Write Python.
Run Rust.

Author transforms and model definitions in Python — the engine compiles and executes them in Rust for speed, memory safety and deterministic builds. Train in PyTorch, export to ONNX, and run inference anywhere.

Deploy your first modelSee pricing
Python SDKRust enginePyTorch → ONNX

Your code, compiled — not interpreted

Python for the ideas, Rust for the heavy lifting.

⚡

Speed

Transforms and training graphs compile to native Rust. No cold runtimes, no GC pauses on scoring traffic.

🛡

Memory safety

Borrow and type checks move data bugs to compile time. A bad transform fails the build, not your nightly retrain.

🔁

Deterministic execution

Same code, seed and dataset hash — the same model. Every build, every environment, reproducible to the byte.

📦

ONNX portability

Train in PyTorch, export to ONNX, and run inference on any onnxruntime host — edge, cloud or your laptop.

From definition to artifact

What you write, and exactly what the engine produces from it.

absolutno › transform.pyyou write
# author everything in Python
from absolutno import transform, model

@transform
def clean(df):
    df = df.drop_null("close")
    df["ret_1d"] = df["close"].pct_change()
    return df

@model(target="close_next", kind="regression")
def momentum_net():
    return layers(Linear(12, 64), ReLU(), Linear(64, 1))

compiled to Rust

rustc · 0 warnings

fn clean(input: DataFrame) -> DataFrame { let df = input.drop_null(col("close")); df.with_col("ret_1d", pct_change(col("close"))) }

exported to ONNX

opset 17

momentum_net.onnx · 1.2 MB Gemm → Relu → Gemm runs on any onnxruntime host

✓Same source hash → same binary → same model. Always.

Author → compile → serve

Three steps, no toolchain setup.

1

Author

Transforms, features and model definitions in the Python SDK.

↓
2

Compile & execute

The engine translates to Rust, builds, trains PyTorch and exports ONNX.

↓
3

Serve

Versioned ONNX artifacts behind a one-click inference endpoint.

Where teams use it

Built for models that carry real traffic.

⏱

Real-time scoring

Low-latency ONNX inference for live decisions — risk, ranking, forecasting — on traffic that never waits.

🧪

Feature engineering at scale

Compiled transforms crunch large tables in Rust while you keep iterating in Python.

⚙

Retraining pipelines

Dataset-change triggers rebuild and re-export the model, deterministically, end to end.

Feeds and is fed

Streaming pipelines can write feature tables straight into this platform — datasets land versioned and ready to train.

See streaming pipelines →

Pricing

Start free. Ship when it’s ready.

Free while you experiment. Upgrade when a model carries traffic.

Starter

Experiment on your own data.

$0forever
Start building
  • ✓Up to 2 deployed models
  • ✓Python SDK & CLI
  • ✓PyTorch → ONNX export
  • ✓1 shared build runner
  • ✓Community support
Most popular

Team

For teams shipping models.

$49per month
Start with Team
  • ✓Up to 10 models
  • ✓4 parallel Rust build runners
  • ✓Shared transform registry
  • ✓Dataset-change retraining triggers
  • ✓Versioned artifacts & rollback
  • ✓Email support

Enterprise

Dedicated capacity, your compliance.

Customtalk to us
Request access
  • ✓Unlimited models & runners
  • ✓Dedicated CPU / GPU pools
  • ✓On-prem / self-hosted option
  • ✓SSO, audit log, VPC peering
  • ✓99.95% SLA, named engineer

Compare plans

CompareStarterTeamEnterprise
Deployed models210Unlimited
Build runners1 shared4 parallelDedicated pools
Inference runs / month100k5MCustom
Retraining triggersManualDataset-changeFully scheduled
ONNX export✓✓✓
Shared transform registry—✓✓
SSO & audit log——✓
On-prem / self-hosted——✓
SupportCommunityEmailNamed engineer
SLA——99.95%