GPU infrastructure intelligence

Stop
overpaying for compute.

The independent weekly newsletter that helps AI startup founders make smarter GPU infrastructure decisions — and keep more money in the bank.

Subscribe free — weekly dispatch See the GPU landscape
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Provider comparisons
Current pricing across every major GPU cloud — delivered to your inbox weekly so you're never working from stale data.
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Decision frameworks
When to use spot vs reserved. Training vs inference. How to pick a provider for your specific workload — not a generic recommendation.
🎙
Founder stories
Real AI startups sharing real infrastructure decisions, mistakes, and the exact steps they took to cut their bills.
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Zero vendor bias
No provider pays to appear in our editorial. No affiliate pressure shapes our recommendations. Just the honest picture.
The GPU cloud landscape

Three tiers. Very different costs.

Most AI startups default to Tier 1 out of habit. Understanding all three tiers — and when to use each — is where the biggest savings are found. The newsletter publishes current pricing across all tiers every week.

Tier 1 · Highest cost
Hyperscalers
The default choice for most startups — and the most expensive. Enterprise-grade SLAs, familiar tooling, and enormous sales teams make them easy to land on. But you're paying a significant premium for brand comfort.
AWSGoogle CloudMicrosoft Azure
Relative cost
Right for: enterprise compliance, existing contracts
Tier 2 · Mid cost
Specialist clouds
Purpose-built for AI and ML workloads. Better GPU selection, more ML-native tooling, and meaningfully lower prices than hyperscalers. Increasingly the default for well-informed AI teams.
CoreWeaveLambda LabsCrusoe
Relative cost
Right for: serious training workloads, reliability matters
Tier 3 · Lowest cost
GPU marketplaces
Aggregated underutilised GPU supply from data centres and enthusiasts worldwide. The largest price gap vs hyperscalers — often dramatic. Spot-style pricing, more variability, ideal for training and batch workloads.
Vast.aiRunPodTensorDock
Relative cost
Right for: training, fine-tuning, batch jobs, cost-sensitive work
The problem

Most startups never question the default.

AWS is familiar. The sales rep already called. Everyone else seems to be using it.

But the cost difference between Tier 1 and Tier 3 for identical GPU hardware is consistently dramatic — and has been for years. For a startup doing regular training runs, the gap compounds into real runway that should be going into product and people instead.

Nobody in the market publishes this comparison honestly. Providers don't compare themselves to competitors. Hyperscalers don't explain why you should switch. NebulaPeer does.

"Nobody told us there was a better option. We just assumed AWS was what everyone used."
— Series A AI founder, after switching providers
Relative cost for identical GPU hardware — same spec, different provider tier
Hyperscaler
Tier 1 · AWS, GCP, Azure
Most expensive
Specialist cloud (reserved)
Tier 2 · committed contract
Moderate saving
Specialist cloud (on-demand)
Tier 2 · no commitment
Good saving
GPU marketplace
Tier 3 · Vast.ai, RunPod
Largest saving
↗ Current exact pricing across all providers — in every weekly issue
How NebulaPeer works

Intelligence, not noise.

01
We harvest the signals
Every week NebulaPeer scans Reddit, Hacker News, Dev.to, and Product Hunt for the real questions and pain points AI founders are dealing with around GPU infrastructure. We know what your peers are struggling with right now.
02
We do the comparison
We pull current pricing across AWS, GCP, Vast.ai, RunPod, Lambda, CoreWeave, Modal, and more every week. Same GPU spec, different providers, honest numbers — published without spin, without affiliate pressure, without vendor influence.
03
We translate it for founders
No CUDA internals. No kernel tuning tutorials. Just clear, actionable guidance on which provider to use for which workload, when to switch, and how to cut your GPU bill without slowing your team down by a single day.
What you get

Every Friday. One email.

⚖
Weekly price comparisons
Current pricing across 8+ GPU providers for the most common instance types — updated every issue so you're never making decisions from stale data. This is the one thing you can't get from a static website.
Every issue
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Provider deep-dives
Reliability, availability, egress fees, onboarding friction, support quality. The information providers don't publish about themselves — sourced from the founder community and our own testing.
Rotating weekly
📐
Decision frameworks
When to use spot vs reserved. How to think about training vs inference infrastructure. What changes about your GPU strategy when you raise a round. Practical tools for real decisions, not theoretical guides.
Frameworks
🎙
Founder stories
Real AI startup founders sharing their infrastructure decisions, expensive mistakes, and the exact steps they took to cut their bills. Anonymous where needed. Specific enough to be genuinely useful.
Real cases
Recent issues

What we cover.

Get the next one →
#007
Six things AI founders believe about GPU costs that simply aren't true
Myth-busting
→
#006
Vast.ai vs RunPod — a real reliability comparison for production workloads
Comparison
→
#005
The hidden costs nobody talks about — egress fees, idle compute, and storage
Deep-dive
→
#004
How one startup cut their GPU bill by 70% without changing a line of model code
Founder story
→
#003
Training vs inference — why treating them as the same GPU problem is costing you
Framework
→
#002
The GPU cloud landscape in 2026 — a founder's map of every tier and when to use each
Overview
→
#001
The default trap — why AI startups choose AWS and what it actually costs them
Insight
→
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GPU infrastructure intelligence for AI startup founders. Current pricing, honest comparisons, real founder stories. Every Friday.

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"I had no idea we were overpaying by that much. One NebulaPeer issue gave us the numbers we needed to have a real conversation about our infrastructure."
Technical co-founder, Series A · switched from AWS to Vast.ai
"The training vs inference breakdown finally made sense to me as a non-technical founder. I had that conversation with our ML engineer the same day and we restructured our whole setup."
CEO, pre-seed AI startup
"It's the only newsletter I forward to my whole team every week without even reading it first. That's how much I trust it."
Founder, computer vision startup · Seed stage