Beam vs exo (Exo Labs)
Detailed side-by-side comparison to help you choose the right tool
Beam
🔴DeveloperAI Infrastructure
Beam is AI infrastructure for developers: serverless sandboxes, task queues, and GPU model inference with sub-second cold starts and per-second billing. It is a Modal/RunPod competitor focused on AI primitives like vLLM, ComfyUI, and agent code sandboxing.
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Customexo (Exo Labs)
🔴DeveloperAI Infrastructure
Open-source tool that turns your Macs and workstations into a single distributed local LLM inference cluster.
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CustomFeature Comparison
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Beam - Pros & Cons
Pros
- ✓Publicly itemized per-second GPU pricing is unusually transparent for the category
- ✓Sandboxes for agent-generated code are a first-class primitive, not an afterthought
- ✓Single decorator gets a Python function onto a GPU with HTTPS in front of it
Cons
- ✗Usage-based billing can spike fast under unbounded autoscale — set alerts day one
- ✗Less general-purpose than Modal if you also want non-AI batch workflows
- ✗$30 free credit burns quickly on H100s — evaluation budget is smaller than it looks
exo (Exo Labs) - Pros & Cons
Pros
- ✓Full data privacy — every token stays on your network
- ✓One-time hardware cost beats hourly cloud pricing for steady workloads
- ✓Drop-in OpenAI SDK compatibility means zero app rewrites
- ✓Active open-source community and a credible commercial sponsor
- ✓Works with consumer hardware you may already own (Mac Studio, Mac mini)
Cons
- ✗Throughput per node is well below a hosted H100 — not for low-latency consumer products
- ✗GPL licensing complicates commercial embedding for some teams
- ✗Cluster setup still rewards networking knowledge despite auto-discovery
- ✗Apple Silicon is the optimised path; mixed-vendor clusters are rougher
- ✗No SLA or managed support unless you engage Exo Labs commercially
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