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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Lithops — Python code at massive scale</title>
<meta name="description" content="Lithops is a Python multi-cloud serverless computing framework. Run unmodified Python at massive scale across AWS, GCP, Azure, IBM Cloud, Kubernetes and HPC — from thousands of parallel functions to petabytes of data." />
<link rel="icon" href="assets/favicon.png" />
<link rel="preconnect" href="https://fonts.googleapis.com" />
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
<link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500;700;800&family=Space+Grotesk:wght@400;500;600;700&display=swap" rel="stylesheet" />
<link rel="stylesheet" href="styles.css?v=22" />
</head>
<body>
<!-- ===== Background grid / glow ===== -->
<div class="bg-grid" aria-hidden="true"></div>
<div class="bg-glow" aria-hidden="true"></div>
<!-- ===== Nav ===== -->
<header class="nav" id="nav">
<div class="nav-inner">
<a class="nav-logo" href="#top" id="navLogo">
<img src="assets/lithops_logo_white.png" alt="Lithops logo" class="nav-logo-img" />
</a>
<nav class="nav-links">
<a href="#how">How it works</a>
<a href="#features">Features</a>
<a href="#code">Code</a>
<a href="#backends">Backends</a>
<a href="#usecases">Use cases</a>
</nav>
<div class="nav-cta">
<a class="ghost-btn" href="https://github.com/lithops-cloud/lithops" target="_blank" rel="noopener">
<svg viewBox="0 0 16 16" width="16" height="16" fill="currentColor" aria-hidden="true"><path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.01 8.01 0 0 0 16 8c0-4.42-3.58-8-8-8z"/></svg>
<span>GitHub</span>
</a>
<a class="solid-btn" href="https://lithops-cloud.github.io/docs/" target="_blank" rel="noopener">Docs</a>
</div>
<button class="nav-burger" id="burger" aria-label="Toggle menu"><span></span><span></span><span></span></button>
</div>
</header>
<!-- ===== Hero ===== -->
<section class="hero" id="top">
<div class="hero-copy">
<div class="hero-brand" id="heroBrand">
<a class="hero-logo" href="#top" aria-label="Lithops home">
<img src="assets/lithops_logo_white.png" alt="Lithops" />
</a>
<p class="hero-tagline">Multi-cloud serverless for Python</p>
</div>
<h1 class="hero-title">
Run <span class="accent">Python code</span><br />
at <span class="grad">massive scale</span>.
</h1>
<p class="hero-sub">
Lithops turns your laptop into a supercomputer. Fan a single Python
function out to <strong>thousands of parallel cloud workers</strong> in seconds —
no servers, no YAML, no infrastructure to manage.
</p>
<div class="hero-install">
<code><span class="prompt">$</span> pip install lithops</code>
<button class="copy-btn" data-copy="pip install lithops" aria-label="Copy install command">
<svg viewBox="0 0 24 24" width="16" height="16" fill="none" stroke="currentColor" stroke-width="2"><rect x="9" y="9" width="13" height="13" rx="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg>
</button>
</div>
<div class="hero-actions">
<a class="solid-btn big" href="https://lithops-cloud.github.io/docs/">Get started →</a>
<a class="ghost-btn big" href="#how">See how it works</a>
</div>
<div class="hero-stats">
<div class="stat"><span class="stat-num" data-count="10000" data-suffix="+">0</span><span class="stat-label">functions in parallel</span></div>
<div class="stat"><span class="stat-num" data-count="80" data-suffix=" GB/s">0</span><span class="stat-label">aggregate bandwidth</span></div>
<div class="stat"><span class="stat-num" data-count="15" data-suffix="+">0</span><span class="stat-label">cloud backends</span></div>
</div>
</div>
<!-- Canvas visualization: laptop invoking thousands of functions in the cloud -->
<div class="hero-viz">
<canvas id="scaleCanvas"></canvas>
<div class="viz-hud">
<div class="hud-row"><span class="hud-key">invoker</span><span class="hud-val" id="hudState">dispatching…</span></div>
<div class="hud-row"><span class="hud-key">active workers</span><span class="hud-val" id="hudWorkerPool">0</span></div>
<div class="hud-row"><span class="hud-key">running</span><span class="hud-val orange" id="hudActive">0</span></div>
<div class="hud-row"><span class="hud-key">completed</span><span class="hud-val cyan" id="hudDone">0</span></div>
</div>
<div class="viz-progress" aria-hidden="true">
<div class="viz-progress-head">
<span class="viz-progress-label">map()</span>
<span class="viz-progress-pct" id="hudPct">0%</span>
</div>
<div class="viz-progress-track">
<div class="viz-progress-fill" id="hudProgress"></div>
</div>
<div class="viz-progress-meta">
<span id="hudThroughput">0 fn/s</span>
<span id="hudWorkers">10,000 invoked</span>
</div>
</div>
</div>
</section>
<!-- ===== Trust bar ===== -->
<section class="trustbar">
<span class="kicker trust-label">// runs anywhere</span>
<div class="trust-logos" id="trustLogos">
<img src="assets/ibm_cloud.png" alt="IBM Cloud" />
<img src="assets/aws.png" alt="AWS" />
<img src="assets/google_cloud.png" alt="Google Cloud" />
<img src="assets/azure.png" alt="Azure" />
<img src="assets/oracle.png" alt="Oracle Cloud" />
<img src="assets/aliyun.png" alt="Alibaba Cloud" />
<img src="assets/k8s.png" alt="Kubernetes" />
<img src="assets/openshift.png" alt="OpenShift" />
</div>
</section>
<!-- ===== How it works ===== -->
<section class="section" id="how">
<div class="section-head">
<span class="kicker">// architecture</span>
<h2>Your laptop → thousands of workers → back again</h2>
<p class="section-lead">
Write plain Python. Lithops serializes your function and data, provisions
the workers, invokes them in parallel across your chosen backend, and streams
the results straight back to your session.
</p>
</div>
<div class="flow">
<div class="flow-node" data-step="1">
<div class="flow-icon">
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.6"><rect x="3" y="4" width="18" height="12" rx="1.5"/><path d="M2 20h20M9 8l3 2-3 2M13 12h3"/></svg>
</div>
<h3>1 · Write</h3>
<p>A regular Python function and an iterable. No decorators, no rewrites.</p>
</div>
<div class="flow-arrow" aria-hidden="true"></div>
<div class="flow-node" data-step="2">
<div class="flow-icon">
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</div>
<h3>2 · Dispatch</h3>
<p>The executor packages code + data and invokes workers in parallel.</p>
</div>
<div class="flow-arrow" aria-hidden="true"></div>
<div class="flow-node highlight" data-step="3">
<div class="flow-icon">
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</div>
<h3>3 · Scale out</h3>
<p>Thousands of serverless functions run at once, right next to your data.</p>
</div>
<div class="flow-arrow" aria-hidden="true"></div>
<div class="flow-node highlight-green" data-step="4">
<div class="flow-icon">
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.6"><path d="M4 6h16M4 12h16M4 18h10"/><path d="M20 15l2 2-2 2"/></svg>
</div>
<h3>4 · Collect</h3>
<p>Results and logs stream back as if it all ran locally.</p>
</div>
</div>
<div class="arch-card">
<svg class="arch-svg" viewBox="0 62 1000 362" role="img" aria-label="Lithops architecture: client dispatches via the runtime; workers use the storage backend; JobMonitor polls storage and streams results back to the client">
<defs>
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<text class="lbl cyt" x="259" y="208" text-anchor="middle">map()</text>
<text class="lbl cyt" x="590" y="134" text-anchor="middle">invoke</text>
<text class="lbl ort" x="590" y="334" text-anchor="middle">objects</text>
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<text class="code kk" x="50" y="224">import <tspan class="nn">lithops</tspan></text>
<text class="code" x="50" y="244"><tspan class="fnn">fexec.map</tspan>(fn, data)</text>
<text class="code" x="50" y="264"><tspan class="fnn">fexec.get_result</tspan>()</text>
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<text class="foot rgt" x="956" y="114" text-anchor="end">1 … N functions</text>
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</g>
<!-- STORAGE BACKEND -->
<g>
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<text class="chip-t ort" x="644" y="324">STORAGE BACKEND</text>
<g class="doc"><rect x="648" y="338" width="96" height="46" rx="8"/><text x="696" y="366" text-anchor="middle">code</text></g>
<g class="doc"><rect x="752" y="338" width="96" height="46" rx="8"/><text x="800" y="366" text-anchor="middle">data</text></g>
<g class="doc"><rect x="856" y="338" width="104" height="46" rx="8"/><text x="908" y="366" text-anchor="middle">results</text></g>
</g>
<!-- monitor ↔ storage ↔ client (above cards) -->
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<text class="lbl ort" x="588" y="386" text-anchor="middle">poll status</text>
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<text class="lbl grt" x="255" y="388" text-anchor="middle">results</text>
</svg>
</div>
</section>
<!-- ===== Features ===== -->
<section class="section" id="features">
<div class="section-head">
<span class="kicker">// why lithops</span>
<h2>Built for developers, tuned for scale</h2>
</div>
<div class="feature-grid">
<article class="feature">
<div class="f-ico">⚡</div>
<h3>Zero-config scale-out</h3>
<p>Go from one core to thousands with a single <code>map()</code>. No cluster to spin up, no capacity planning.</p>
</article>
<article class="feature">
<div class="f-ico">🌍</div>
<h3>True multi-cloud</h3>
<p>The same code runs on AWS, GCP, Azure, IBM, Oracle, Aliyun, Kubernetes and HPC. Provider details stay transparent.</p>
</article>
<article class="feature">
<div class="f-ico">🐍</div>
<h3>Just Python</h3>
<p>NumPy, pandas, scikit-learn — your existing code, unmodified. No cloud-specific boilerplate.</p>
</article>
<article class="feature">
<div class="f-ico">📦</div>
<h3>Object storage, simplified</h3>
<p>Automatic discovery and transparent partitioning of CSV, Parquet and JSON straight from S3-style buckets.</p>
</article>
<article class="feature">
<div class="f-ico">🎯</div>
<h3>Optimize your way</h3>
<p>Chase latency with Lambda's millisecond starts, or cut cost with Batch on Spot — same program, your call.</p>
</article>
<article class="feature">
<div class="f-ico">🔎</div>
<h3>Error-friendly</h3>
<p>Traceback a remote failure just like a local one. Built-in logging, retries and event sourcing.</p>
</article>
</div>
</section>
<!-- ===== Code ===== -->
<section class="section" id="code">
<div class="section-head">
<span class="kicker">// two lines to the cloud</span>
<h2>Change how it runs, not what you write</h2>
<p class="section-lead">Pick an example. This is the whole program — Lithops handles the rest.</p>
</div>
<div class="code-wrap">
<div class="code-tabs" role="tablist">
<button class="code-tab active" data-tab="hello" role="tab">hello.py</button>
<button class="code-tab" data-tab="map" role="tab">estimate_pi.py</button>
<button class="code-tab" data-tab="reduce" role="tab">map_reduce.py</button>
<button class="code-tab" data-tab="storage" role="tab">storage.py</button>
</div>
<div class="editor">
<div class="editor-bar">
<span class="tl tl-r"></span><span class="tl tl-y"></span><span class="tl tl-g"></span>
<span class="editor-file" id="editorFile">hello.py</span>
</div>
<pre class="code-pane active" id="pane-hello"><code><span class="c"># A single serverless invocation</span>
<span class="k">import</span> lithops
<span class="k">def</span> <span class="fn">hello</span>(name):
<span class="k">return</span> <span class="s">f'Hello {name}!'</span>
fexec = lithops.<span class="fn">FunctionExecutor</span>()
fexec.<span class="fn">call_async</span>(hello, <span class="s">'World'</span>)
<span class="fn">print</span>(fexec.<span class="fn">get_result</span>())
</code></pre>
<pre class="code-pane" id="pane-map"><code><span class="c"># Estimate Pi with 10 parallel workers</span>
<span class="k">import</span> lithops, random
<span class="k">def</span> <span class="fn">is_inside</span>(n):
count = <span class="n">0</span>
<span class="k">for</span> _ <span class="k">in</span> <span class="fn">range</span>(n):
x, y = random.<span class="fn">random</span>(), random.<span class="fn">random</span>()
<span class="k">if</span> x*x + y*y < <span class="n">1</span>:
count += <span class="n">1</span>
<span class="k">return</span> count
np, n = <span class="n">10</span>, <span class="n">15_000_000</span>
parts = [n // np] * np
fexec = lithops.<span class="fn">FunctionExecutor</span>()
fexec.<span class="fn">map</span>(is_inside, parts) <span class="c"># fan out</span>
pi = <span class="fn">sum</span>(fexec.<span class="fn">get_result</span>()) / n * <span class="n">4</span> <span class="c"># fan in</span>
<span class="fn">print</span>(<span class="s">f'Estimated Pi: {pi}'</span>)
</code></pre>
<pre class="code-pane" id="pane-reduce"><code><span class="c"># Map then reduce, in the cloud</span>
<span class="k">import</span> lithops
iterdata = [<span class="n">1</span>, <span class="n">2</span>, <span class="n">3</span>, <span class="n">4</span>]
<span class="k">def</span> <span class="fn">my_map</span>(x):
<span class="k">return</span> x + <span class="n">7</span>
<span class="k">def</span> <span class="fn">my_reduce</span>(results):
<span class="k">return</span> <span class="fn">sum</span>(results)
fexec = lithops.<span class="fn">FunctionExecutor</span>()
fexec.<span class="fn">map_reduce</span>(my_map, iterdata, my_reduce)
<span class="fn">print</span>(fexec.<span class="fn">get_result</span>())
</code></pre>
<pre class="code-pane" id="pane-storage"><code><span class="c"># Unified storage across any cloud</span>
<span class="k">from</span> lithops <span class="k">import</span> Storage
st = <span class="fn">Storage</span>()
st.<span class="fn">put_object</span>(bucket=<span class="s">'mybucket'</span>,
key=<span class="s">'test.txt'</span>,
body=<span class="s">'Hello World'</span>)
data = st.<span class="fn">get_object</span>(bucket=<span class="s">'mybucket'</span>,
key=<span class="s">'test.txt'</span>)
<span class="fn">print</span>(data)
</code></pre>
</div>
<div class="code-side">
<div class="run-panel">
<div class="run-head"><span class="tl tl-r"></span><span class="tl tl-y"></span><span class="tl tl-g"></span><span class="run-title">terminal</span></div>
<div class="run-body" id="runBody">
<div class="run-line"><span class="prompt">$</span> python <span id="runFile">hello.py</span></div>
</div>
</div>
<p class="code-note">Swap <code>FunctionExecutor()</code>'s backend in <code>~/.lithops/config</code> — your code never changes.</p>
</div>
</div>
</section>
<!-- ===== Backends ===== -->
<section class="section" id="backends">
<div class="section-head">
<span class="kicker">// compute anywhere</span>
<h2>One API. Every major backend.</h2>
<p class="section-lead">Mix serverless functions, containers, VMs, Kubernetes and HPC — Lithops keeps provider management out of your code.</p>
</div>
<div class="backend-grid">
<div class="backend-col">
<h4>Serverless functions</h4>
<ul>
<li>AWS Lambda</li><li>Google Cloud Run functions</li><li>Azure Functions</li>
<li>Oracle Functions</li><li>Aliyun Function Compute</li>
<li>OpenWhisk</li>
</ul>
</div>
<div class="backend-col">
<h4>Containers & batch</h4>
<ul>
<li>AWS Batch</li><li>Google Cloud Run</li><li>Azure Container Apps</li>
<li>IBM Code Engine</li><li>Kubernetes Jobs</li><li>Knative</li><li>Singularity</li>
</ul>
</div>
<div class="backend-col">
<h4>Virtual machines</h4>
<ul>
<li>AWS EC2</li><li>Google Compute Engine</li><li>Azure Virtual Machines</li>
<li>IBM Virtual Private Cloud</li><li>Generic VM / on-prem</li>
</ul>
</div>
<div class="backend-col">
<h4>Storage</h4>
<ul>
<li>AWS S3</li><li>Google Cloud Storage</li><li>Azure Blob</li>
<li>IBM COS</li><li>MinIO · Ceph · Redis</li><li>Swift · Infinispan</li>
</ul>
</div>
</div>
</section>
<!-- ===== Use cases ===== -->
<section class="section" id="usecases">
<div class="section-head">
<span class="kicker">// what you can build</span>
<h2>Embarrassingly parallel? Perfect fit.</h2>
<p class="section-lead">If your problem breaks into many small, independent tasks, Lithops solves it at scale — fast.</p>
</div>
<div class="uc-grid">
<div class="uc"><span class="uc-tag">🔁</span><h3>Monte Carlo simulations</h3><p>Run millions of stochastic trials across thousands of cores and aggregate in seconds.</p></div>
<div class="uc"><span class="uc-tag">🧬</span><h3>Genomics & metabolomics</h3><p>Powering platforms like MetaSpace on Lambda + EC2 directly over S3.</p></div>
<div class="uc"><span class="uc-tag">🗺️</span><h3>Geospatial analytics</h3><p>Tile, process and reduce huge raster/vector datasets in parallel.</p></div>
<div class="uc"><span class="uc-tag">🧠</span><h3>ML & hyperparameter tuning</h3><p>Sweep parameter grids and preprocess training data at cloud scale.</p></div>
<div class="uc"><span class="uc-tag">📊</span><h3>Big data ETL</h3><p>Partition CSV/Parquet/JSON automatically and transform terabytes in one pass.</p></div>
<div class="uc"><span class="uc-tag">🔬</span><h3>Scientific pipelines</h3><p>From HPC clusters to pre-exascale supercomputers via the Lithops HPC backend.</p></div>
</div>
</section>
<!-- ===== CTA ===== -->
<section class="cta">
<div class="cta-card">
<h2>Give your Python 1000 cores.</h2>
<p>Two lines of setup. Zero infrastructure. Start on localhost, deploy to any cloud.</p>
<div class="cta-cmd">
<code><span class="prompt">$</span> pip install lithops && lithops hello</code>
<button class="copy-btn" data-copy="pip install lithops && lithops hello" aria-label="Copy command">
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<div class="cta-actions">
<a class="solid-btn big" href="https://lithops-cloud.github.io/docs/">Read the docs</a>
<a class="ghost-btn big" href="https://github.com/lithops-cloud/lithops" target="_blank" rel="noopener">Star on GitHub</a>
</div>
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</section>
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<p>A Python multi-cloud serverless computing framework.</p>
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<div class="footer-col">
<h5>Project</h5>
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<h5>Learn</h5>
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