AI-native Python performance

Cut inference costs by 40%. Accelerate everything else.

pip install epochly

Optimizes when safe, yields when it can't help.

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Why Epochly

Speed, safety, and visibility — without changing a line of code.

AI Inference Acceleration

Inference Optimization

Zero-config, multi-framework

Auto-detect PyTorch, Transformers, and ONNX Runtime. Profile models, apply dynamic micro-batching, and cache results — all without touching your model code.

Safety Architecture

Optimize without risk

Circuit breakers, canary validation, drift monitoring, and automatic fallback chains ensure optimizations never degrade model accuracy.

Cost Intelligence

See your savings

Per-request GPU cost attribution, savings projections, and full Prometheus/OpenTelemetry integration. Know exactly how much you're saving.

Runtime Performance

JIT Compilation

Up to 193x on numerical loops

Numba-backed just-in-time compilation for hot numerical code. No decorators, no type annotations required.

GPU Acceleration

Up to 70x on large arrays

Automatic GPU offloading for large array operations via CuPy. Works with NumPy, SciPy, and compatible libraries.

Multicore Parallelism

Up to 8x on CPU-bound work

Sub-interpreter parallelization and thread pool management. No GIL limitations on Python 3.13+.

Fleet Observability

Fleet Dashboard

Real-time service health

See every node's optimization status, speedup, and enhancement level at a glance. Real-time health grid with L0-L4 tracking.

Performance Analytics

Latency, throughput, JIT stats

Latency distributions, throughput trends, JIT compilation statistics, and CPU hotspot identification across your fleet.

Service Drill-down

Per-service detail

Deep-dive into individual services. JIT compilation stats, current enhancement level, and key performance metrics.

Now Available

Epochly Lens

See your fleet's performance in one dashboard. Real-time service health, optimization coverage, and performance analytics across every node running Epochly.

lens.epochly.com
Epochly Lens fleet overview showing service health grid with optimization status and speedup metrics
Open DashboardLearn more

Get started in 2 minutes

No decorators. No config files. No new API.

# Install
$ pip install epochly
# Wrap your model — that's it
import epochly
model = epochly.wrap(model)
# Use normally — optimization is automatic
output = model(input_tensor)
# Check savings
$ python -c "import epochly; epochly.stats()"
Full documentation

Use cases

Find the right optimization path for your workload

Start with AI inference if you need to cut cost, increase throughput, or add production-safe optimization. Explore broader Python acceleration when your bottleneck is outside inference.

PyTorch inference optimization

Improve PyTorch inference economics without making your first move a serving-stack rewrite.

Explore this path

Transformers inference optimization

A lower-friction path to better transformer-serving economics for Python teams shipping real features.

Explore this path

ONNX Runtime optimization

Runtime-specific efficiency and operational fit for teams running production inference.

Explore this path

Safe `torch.compile` in production

Guardrails, fallback, and safer rollout for teams evaluating compile-driven gains.

Explore this path

Data science acceleration

Speed up CPU-heavy Python analysis and numerical work when the bottleneck is still in Python execution.

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ML pipelines and preprocessing

Improve preprocessing, scoring, and Python-side pipeline work around model training and inference.

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Financial computing

Reduce Python overhead in numerical finance workloads before taking on a rewrite.

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CPU-heavy web backends

Improve compute-heavy endpoints when the bottleneck is Python execution, not database or network I/O.

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Benchmarks

Validated on real hardware, reproducible methodology

AI Inference
20-40%

GPU cost reduction (Pro)

2-5x

Throughput via micro-batching

<1ms

Per-request overhead

Runtime Performance
193x

Peak JIT compilation (Level 2)

70x

GPU acceleration (Level 4)

<5%

Overhead when not helping

GPU example: 100M-element array operation: 1,427ms → 21ms (68x)

Reproducible Results

These benchmarks use our open methodology. Run them yourself: pip install epochly && python -m epochly.benchmark

How we measuredReproduce the benchmarkKnown limitations

Correctness first. Always.

Performance is worthless if it changes your results. Epochly's safety architecture protects both inference accuracy and runtime correctness.

Progressive Enhancement

Monitors first, optimizes only after stability is confirmed. Your code runs unchanged until Epochly is certain it's safe.

Automatic Fallback

Detects problems and reverts to standard Python automatically. No data corruption, no silent failures.

Instant Kill Switch

Set EPOCHLY_DISABLE=1 to turn everything off immediately. Uninstall leaves no trace.

Learn more about our safety model

What's next

The Inference Accelerator ships today. Here's what we're building next.

In Development

Agent Performance Infrastructure

Become the performance layer for AI agent orchestration. Optimize recursive fan-out, manage concurrency, and collapse cold-start latency for agent workloads.

Now Available

Enterprise Tier

Fleet-wide Lens dashboard, unlimited alerts, 13-month data retention, RBAC with audit logs, and priority support. Everything in Pro plus organizational controls for compliance-driven teams.

Learn more
Interested in early access? Get in touch

Start free, then move to Pro when you're ready

Try Epochly without friction. Use pricing to choose your path, checkout when you want unlimited cores and GPU, or contact us if you're evaluating rollout for a team.

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Explore inference guides, benchmarks, or the FAQ.