AI Inference Accelerator

Inference optimization that pays for itself

Cut inference costs 20-40% with zero code changes. Safety-gated optimization for PyTorch, Transformers, and ONNX Runtime.
Free to start.

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Safety-gated optimizationPyTorch · Transformers · ONNX RuntimeFree tier available

Get Started See Pricing

Free for individual developers. Pro starts with a 30-day free trial.

Two lines. That's all.

Your existing model serving code stays exactly the same. Epochly wraps your model and handles optimization automatically.

BeforeStandard model serving

1import torch
2from fastapi import FastAPI
3
4app = FastAPI()
5model = MyModel().to("cuda").eval()
6
7@app.post("/predict")
8async def predict(data: dict):
9    with torch.no_grad():
10        result = model(data["input"])
11    return {"prediction": result}

AfterWith Epochly

1import epochly                          # ← added
2import torch
3from fastapi import FastAPI
4
5app = FastAPI()
6model = MyModel().to("cuda").eval()
7model = epochly.wrap(model)             # ← added
8
9@app.post("/predict")
10async def predict(data: dict):
11    with torch.no_grad():
12        result = model(data["input"])
13    return {"prediction": result}

Epochly auto-detects your framework, profiles your model, and applies safety-gated optimizations. No configuration files, no model rewriting.

Works with your stack

Epochly detects your framework automatically. No configuration required.

PyTorch

Auto-detected

HuggingFace Transformers

Auto-detected

ONNX Runtime

Auto-detected

FastAPI

Middleware

vLLM

Pro

TGI

Pro

Framework paths

The first live inference cluster

Each framework has its own optimization path with specific performance data, clear fit guidance, and a direct route to pricing or a free trial.

The live set is PyTorch, Transformers, ONNX Runtime, and safe `torch.compile`.

PyTorch inference optimization

Improve the economics of PyTorch inference without starting with a serving-stack rewrite.

Reduce PyTorch inference cost and improve throughput with safety-gated optimization that fits the Python stack you already run.

Your team runs a Python-first PyTorch stack and wants a lower-friction first optimization step.

You care about safer rollout and fallback at least as much as raw speed claims.

See PyTorch inference optimization path

Transformers inference optimization

A simpler path to better transformer-serving economics — for teams that don't need a full stack migration.

Epochly helps Python teams improve transformer inference efficiency — with clear guidance on where it helps and where a specialized stack is the better fit.

You serve transformer workloads from ordinary Python application stacks.

You want a simpler performance step before major architecture changes.

See Transformers inference optimization path

ONNX Runtime optimization

Improve ONNX Runtime deployment economics with honest guidance on where Epochly adds value.

Use Epochly to improve inference economics around ONNX Runtime deployments while keeping adoption simple and production behavior visible.

You already chose a performance-minded runtime and still care about total serving economics.

Python orchestration or control overhead still matters around the runtime.

See ONNX Runtime optimization path

Safe torch.compile in production

Use torch.compile with a safer production posture than a raw compile-and-pray workflow.

Epochly turns torch.compile from a risky performance experiment into a more controlled production decision with safety gates, fallback, and visibility.

You believe torch.compile has upside but need a safer production posture.

You care about conditional rollout, visibility, and reversibility.

See Safe torch.compile in production path

Use Epochly first when

You want a lower-friction performance step on the stack you already run.
You need pricing and rollout clarity before architecture expansion.
You care about production guardrails and fallback, not just a speed claim.

Look deeper before buying when

Your stack is already heavily kernel-optimized.
Your bottleneck is mostly network, storage, or other non-inference overhead.
You need guaranteed identical outcomes for every framework path.

Use proof before promises

View benchmarks Read the safety model Read the AI inference quickstart Browse all use cases

How Epochly compares to serving frameworks

Other tools provide serving infrastructure. Epochly provides optimization AND safety — and works alongside any serving framework.

Getting Started

	Epochly	BentoML	Ray Serve	TorchServe	Triton
Install method	pip	pip	pip	pip + Java	Docker
Code changes needed	None	Service class	New APIs	Handler class	Model config
Zero-config detection
Time to first result	Minutes	Hours	Hours	Hours	Days
Epochly is the only tool that requires zero changes to your existing model code.

Safety & Optimization

	Epochly	BentoML	Ray Serve	TorchServe	Triton
Safety-gated optimization
Circuit breakers
Canary validation
Drift monitoring
Dynamic micro-batching
Multi-tier caching
torch.compile integration
Epochly is the only solution that combines optimization AND safety in a single layer.

Observability & Cost

	BentoML	Ray Serve	TorchServe	Triton
Built-in metrics
Cost attribution
A/B testing
Freemium model	OSS	OSS	OSS	OSS
Only Epochly provides per-request cost attribution and ROI visibility out of the box.

Epochly works alongside your existing serving infrastructure. Use BentoML, Ray Serve, or any framework for deployment — add Epochly for optimization and safety.

How the Inference Accelerator works

Every inference call passes through six stages. Safety gates ensure optimizations never degrade accuracy.

Request

Inference call arrives

Profile

Measure latency, GPU, batch size

Optimize

Apply micro-batching, caching, compilation

Safety Gate

Canary validation + circuit breakers

Serve

Return optimized result

Save

Track cost savings

Request

Inference call arrives

Profile

Measure latency, GPU, batch size

Optimize

Apply micro-batching, caching, compilation

Safety Gate

Canary validation + circuit breakers

Serve

Return optimized result

Save

Track cost savings

Find the right inference path

Epochly supports PyTorch, Transformers, ONNX Runtime, and safe torch.compile. Choose the guide that matches your stack, then start a team evaluation when you're ready to test against your own workload.

PyTorch inference

Optimization guidance and rollout notes for PyTorch-serving teams.

Transformers inference

Framework-specific path for Hugging Face and transformers-heavy inference stacks.

ONNX Runtime

See the ONNX Runtime page for deployment and performance context.

Safe torch.compile

Review the guarded production path for torch.compile adoption.

Team evaluation

Start a technical evaluation once you're ready to test your own workload.

Start optimizing your inference today

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