Velo Workspaces
Disclosure: I built Velo Workspaces, so read this as you'd read any vendor's comparison. Every claim about Codespaces and Ona is sourced, and I say plainly where the cloud tools are simply the right answer. Prices and positioning change fast here; this reflects what was publicly documented in September 2026.
The short version
- GitHub Codespaces is a cloud-hosted dev container, deeply integrated with GitHub, billed by compute-hour and storage.
- Ona, formerly Gitpod, is also cloud-hosted, but since its October 2025 relaunch it's positioned less as your cloud IDE and more as infrastructure for running AI coding agents at scale, priced in credits.
- Velo Workspaces runs locally on a Mac you already own. The app is free, including unlimited workspaces and official cloud images; AI Bridge and disposable workspaces, the features this comparison is about, are part of Pro at a flat $3.99/month, $24.99/year or $79.99 once, never metered.
If you need an environment reachable from any browser on any device, or team onboarding where nobody installs anything, this won't talk you out of Codespaces. If you're weighing cloud against local as a real trade-off, read on.
Pricing, September 2026
| Velo Workspaces | GitHub Codespaces | Ona (formerly Gitpod) | |
|---|---|---|---|
| Price | Free app. Pro (SSH terminal, AI Bridge, disposable workspaces and more): $3.99/month, $24.99/year, or $79.99 once | Free: 120 core-hours and 15 GB storage a month. Beyond that: $0.18–$2.88 per core-hour (by machine size) plus $0.07 per GiB-month of storage. | Core plan: $20/month for 80–2,200 OCU credits, depending on tier |
| Model | Free download plus a flat-rate Pro subscription or one-time purchase, never metered | Metered: compute-hours plus storage, indefinitely | Credit-based subscription |
Codespaces pricing from github.com/pricing and the GitHub pricing calculator. Ona pricing from Gitpod's FAQ on the transition. Check current prices; both have changed a lot in the past year.
Gitpod is now Ona
If you know Gitpod from an "Open in Gitpod" badge or a pay-as-you-go free tier, that product, Gitpod Classic, closed on October 15, 2025. The company rebranded as Ona and refocused on orchestrating AI coding agents at scale rather than being mainly a cloud IDE, moving from hourly billing to credits (Ona Compute Units). Cloud dev environments are still part of the offer, scaling to 32 vCPUs, 128 GB of RAM and 200 GB of disk on Ona-managed AWS infrastructure, but it isn't the product you'd have compared against a few years ago. One carry-over for open-source maintainers: Ona runs an open-source program giving qualifying projects up to $200 a month in free credits.
What cloud dev environments can't do
This is architectural, not a pricing tier. A cloud environment runs on someone else's hardware, so there's no equivalent to AI Bridge: reaching a model on your own Mac's GPU from inside an isolated sandbox at full speed, with no API bill and no data leaving the machine. It's more specific than "GPUs cost extra". GitHub retired Codespaces' GPU machine type in August 2025 when the underlying Azure GPU VM series was retired, and as of this writing neither Codespaces nor Ona's published plans offer a GPU machine at all. If local-model inference is part of your work, testing an agent or computing embeddings, you can't pay your way into it with either right now.
The other side, stated fairly
None of that makes Codespaces or Ona the wrong choice. Cloud environments solve problems local ones don't. They're reachable from a browser on any device, including one that isn't a Mac. There are no hardware requirements, so a contributor on a Chromebook or an iPad can work. It's easier to hand a running environment to a teammate, or spin up dozens for a class or hackathon without provisioning laptops. And they don't use your machine's CPU, memory or battery. Those are real advantages.
Metered or flat-rate, and why your habits matter
This blog argues that disposability works best as the default: a fresh environment per test run or experiment, thrown away and repeated, rather than one long-lived environment you hope is still clean. That's exactly the pattern that costs most under compute-hour-plus-storage billing: more environments created and destroyed means more billed hours and storage, with no ceiling. Locally, creating and discarding workspaces all day costs nothing extra, and since 1.2 a fresh Linux system from an official cloud image takes about a minute. On Pro it's the same $3.99 a month (less if paid yearly, nothing more after the one-time option) however much you use it, so the price doesn't fight the workflow.
Which to use
- You need an environment reachable from any device, or effortless onboarding for a team. Codespaces: deep GitHub integration, prebuilds, nothing to install.
- You're building enterprise workflows that orchestrate AI coding agents at scale. That's what Ona is built for now; evaluate it on those terms rather than as a Gitpod Classic replacement.
- You want local-model inference on your own GPU, create and discard environments constantly, and don't want to pay by the hour. That's what Velo Workspaces is built for.
Related reading: the architecture behind AI Bridge, the disposable VM workflow for QA and DevOps, comparisons with Parallels and UTM and Docker Desktop and OrbStack, and the local-versus-cloud trade-offs underneath this comparison. Or download Velo Workspaces and try it yourself.