Inference Optimization

Inference Optimization for Python AI Workloads

Evaluate Epochly for PyTorch, Transformers, and ONNX Runtime inference workflows with zero-overhead wrapping, opt-in optimization components, and links to benchmark evidence.

Want to inspect the evidence first? Review reproducible benchmark methodology and workload examples before evaluating Epochly on your own inference path.

Review benchmark methodology

Benchmarks |Safety Model |Compare Tools

Zero-overhead wrap() passthroughPyTorch · 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.wrap() returns a transparent passthrough proxy measured at zero per-call overhead — optimization components stay opt-in and measured.

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 and wraps your model as a verified passthrough — outputs identical, overhead measured at zero. No configuration files, no model rewriting, and EPOCHLY_INFERENCE_ENABLED=0 switches it off instantly.

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 path with clear fit guidance, honest notes on what ships today, and a direct route to pricing or a free trial.

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

PyTorch inference optimization

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

Wrap PyTorch models with a passthrough measured at zero per-call overhead, then opt in to batching and caching components deliberately — on 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, measured starting point for transformer-serving economics — for teams that don't need a full stack migration.

Epochly gives Python teams a zero-overhead wrap for transformer models plus opt-in batching and caching components — with clear guidance on where they help 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

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

Wrap ONNX Runtime sessions with a passthrough measured at zero per-call overhead — adoption stays simple, production behavior stays visible, and every optimization step is opt-in.

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 (preview)

Preview the guarded torch.compile workflow — safety components are shipped and testable today, the automatic rollout path is on the roadmap.

Epochly is building a guarded path from torch.compile experiment to production decision. The safety components — compile safety monitor, canary validation, circuit breakers — ship today; the integrated wrap() compile workflow is in preview.

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

You care about conditional rollout, visibility, and reversibility.

See Safe torch.compile (preview) 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	One line	Service class	New APIs	Handler class	Model config
Zero-config detection
Time to first result	Minutes	Hours	Hours	Hours	Days
Epochly integrates with a one-line epochly.wrap() call — no service classes, no handler rewrites, no model changes.

Safety & Optimization

	Epochly	BentoML	Ray Serve	TorchServe	Triton
Circuit breakers	Opt-in
Canary validation	Opt-in
Drift monitoring	Opt-in
Dynamic micro-batching	Opt-in
Result caching (memory + encrypted disk)	Opt-in
torch.compile integration	Preview
Epochly ships measured memoization on wrapped callables plus opt-in batching, caching, and safety components you enable deliberately — nothing changes behavior silently.

Observability & Cost

	Epochly	BentoML	Ray Serve	TorchServe	Triton
Built-in metrics
Cost attribution	Opt-in
A/B testing
Freemium model		OSS	OSS	OSS	OSS
Epochly ships cost-estimation and Prometheus/OpenTelemetry exporters you wire into your stack — with self-reported timings verified against external measurement.

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

What ships for inference today

epochly.wrap() is a transparent passthrough with zero measured per-call overhead. Optimization is opt-in and measured — you choose what to enable.

Wrap

One line: epochly.wrap(model). A transparent passthrough — outputs identical, per-call overhead measured at zero.

Detect

PyTorch, Transformers, and ONNX Runtime are auto-detected on import. No configuration files, no model rewriting.

Memoize

Services exposing encode, generate, predict, transform, or embed get truth-gated memoization — only measured cache-hit speedups are recorded.

Batch & cache (opt-in)

DynamicMicroBatcher measured at 2.28-28.87x vs sequential on an RTX 4070, plus in-memory and AES-GCM encrypted caches — components you enable deliberately.

Guardrails

Circuit breakers, canary validators, and drift monitoring ship as a tested safety library for the optimizations you turn on.

Kill switch

EPOCHLY_INFERENCE_ENABLED=0 turns wrapping off instantly. Nothing changes your model's behavior silently.

Find the right inference path

Epochly auto-detects PyTorch, Transformers, and ONNX Runtime for passthrough-safe wrapping, and a guarded torch.compile workflow is in preview. 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

Preview the guarded rollout design for torch.compile adoption.

Team evaluation

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

Start optimizing your inference today

Install free, review benchmark evidence, and evaluate against a workload your team already understands. Pro includes 30 days free — cancel anytime.

Start Free Get Pro — 30 Days Free Talk to us about your team