Verda vs Baseten
Verda rents you whole machines: Nvidia GPUs and plain CPU VMs with root, by the hour, in Helsinki. Baseten runs your models for you as containers that scale with traffic, sells hosted open models per token, and has agent sandboxes in private preview.
The companies and products
Verda
Verda, called DataCrunch until 2025, is a European AI cloud that rents Nvidia GPUs, CPU machines and GPU clusters.
You create a GPU or CPU instance, add a disk and an SSH key, and get a full Linux VM with root and its own public IP. Verda also sells GPU clusters with InfiniBand and serverless containers that scale to zero for inference. It is built for teams that train, fine-tune and serve AI models, not as a sandbox for agents.
Baseten
Baseten is an inference cloud: it runs AI models on GPUs for production apps, and sells hosted open models and training.
You package a model with Truss, its open-source format, or bring a Docker image, and Baseten runs it as replicas on Kubernetes that scale up with traffic and down to zero. It also sells Model APIs (open models priced per token) and training jobs on the same GPUs. Sandboxes, isolated environments where an agent runs commands and works with files, are in private preview; this page compares boat with both the CPU instances and the sandbox preview.
Which use cases each one fits
For each use case, Battleships prices every sandbox provider on the same workload and sorts them from the cheapest monthly bill up. “10th cheapest of 377” means 9 of the other 376 providers run that workload, with every feature it needs, for less. “With limits” means the provider runs it but misses something, so it is listed after every full match. Open a use case for its whole ranking and what each provider misses.
| Use case and its workload | Verda: price rank | Baseten: price rank | boat: price rank |
|---|---|---|---|
| Software factory 100 at once · 4 vCPU / 8 GB · 2,200 sessions of 8 h a month Verda gives a coding agent a real VM with root, but a stopped instance keeps billing; Baseten containers run as a non-root user with no persistent disk, and boat ships VMs with agents and free stops. | 88th of 367, with limits $788 a month | 216th of 367, with limits $2,619 a month | 3rd cheapest of 367 $634 a month |
| Always-on AI employees 50 machines 24/7 · 2 vCPU / 4 GB | 133rd of 366, with limits $901 a month | 242nd of 366, with limits $3,258 a month | 5th cheapest of 366 $657 a month |
| Code interpreter bursts 100 at once · 1 vCPU / 2 GB · 200,000 sessions of 1 min a month Both can scale to zero: Verda's serverless containers and Baseten's deployments wake on a request, though Baseten also bills cold starts and, by default, 15 idle minutes before scaling down. | 99th of 345, with limits $165 a month | 319th of 345, with limits $1,856 a month | 186th of 345, with limits $2,000 a month |
| Sandboxes for your users 300 at once · 2 vCPU / 4 GB · 30,000 sessions of 23 min a month Verda does not publish how many instances can run at once, and Baseten's sandbox preview allows 10 per organization by default, so 300 user sandboxes means asking either one for more first. | 71st of 361, with limits $629 a month | 277th of 361, with limits $1,868 a month | Cheapest of 361 $209 a month |
| RL rollouts 1,000 at once · 2 vCPU / 4 GB · 1,000,000 sessions of 3 min a month Verda's CPU VMs at $0.012 per vCPU-hour come out well ahead of Baseten's CPU instances for many parallel rollouts. | 20th of 360, with limits $1,097 a month | 322nd of 360, with limits $31,140 a month | 34th of 360, with limits $2,000 a month |
| Agent evals 250 at once · 4 vCPU / 8 GB · 15,000 sessions of 20 min a month | 91st of 364, with limits $123 a month | 297th of 364, with limits $1,609 a month | 10th cheapest of 364 $200 a month |
| CI runners 40 at once · 8 vCPU / 16 GB · 6,000 sessions of 12 min a month Verda's dedicated EPYC cores with root make good CI runners, while Baseten's non-root model containers do not document Docker; boat gives root and Docker with free stops between jobs. | 10th cheapest of 377 $142 a month | 320th of 377, with limits $1,119 a month | 4th cheapest of 377 $86 a month |
| 24/7 dev boxes 1 machine 24/7 · 4 vCPU / 8 GB | 36th cheapest of 368 $45 a month | 170th of 368, with limits $130 a month | 18th cheapest of 368 $26 a month |
| Computer-use agents 50 at once · 4 vCPU / 8 GB · 20,000 sessions of 15 min a month Neither has a built-in desktop: Verda has a guide to install a VNC server yourself, Baseten has no desktop at all, and boat streams a 60fps desktop with computer use. | 82nd of 369, with limits $123 a month | 294th of 369, with limits $1,799 a month | 6th cheapest of 369 $200 a month |
| Browser-use agents 50 at once · 2 vCPU / 4 GB · 30,000 sessions of 10 min a month | 169th of 367, with limits $110 a month | 318th of 367, with limits $1,194 a month | 7th cheapest of 367 $200 a month |
| Scraping & bots (startup scale) 50 at once · 1 vCPU / 2 GB · 100,000 sessions of 3 min a month | 184th of 346, with limits $103 a month | 220th of 346, with limits $183 a month | 6th cheapest of 346 $90 a month |
| Mass scraping & bots (undetectable) 1,000 at once · 1 vCPU / 2 GB · 3,000,000 sessions of 2 min a month | 84th of 338, with limits $2,057 a month | 126th of 338, with limits $3,654 a month | 71st of 338, with limits $1,800 a month |
| Parallel red teaming 200 at once · 2 vCPU / 4 GB · 5,000 sessions of 30 min a month | 122nd of 360, with limits $66 a month | 254th of 360, with limits $381 a month | 2nd cheapest of 360 $200 a month |
| GPU eval jobs 10 at once · 8 vCPU / 32 GB · 500 sessions of 30 min a month Both fit and boat has no GPUs: Verda rents Nvidia V100 up to GB300 including spot machines, and Baseten runs T4 up to B200 with up to 8 per instance. | 14th cheapest of 98 $1,039 a month | 24th cheapest of 98 $1,625 a month | Can't run it |
| macOS agents 5 at once · 4 vCPU / 8 GB · 1,000 sessions of 20 min a month | Can't run it | Can't run it | Can't run it |
Key features
| Feature | Verda | Baseten | boat |
|---|---|---|---|
| Kind of product | GPU and CPU cloud: raw VMs, GPU clusters and serverless containers | Inference cloud for serving and training models; agent sandboxes in private preview | Sandboxes for AI agents: VMs with agents, desktop and hosting ready |
| Isolation | Every instance is a full VM on KVM/QEMU | Kubernetes containers in a dedicated namespace per customer; sandbox isolation is not documented | Every sandbox is a full VM with its own kernel |
| Root and Docker | Root on your own VM, so you can install Docker; nested virtualization is not documented | Containers run as the non-root app user; Docker inside is not documented | Docker, root, systemd and /dev/kvm on every machine |
| GPUs | Nvidia V100 up to GB300, on-demand or spot, plus clusters with InfiniBand | Nvidia T4 up to B200, 1 to 8 per instance, multi-node over InfiniBand | None |
| Serving models | Serverless containers run your Docker image, scale to zero and wake on the next request | Deploy with Truss or your own image, scale to zero and wake on request; hosted open models priced per token | No wake on request; you start a stopped sandbox through the API |
| Biggest CPU machine | Up to 360 dedicated vCPU / 1,440 GB | Up to 16 vCPU / 64 GiB on shared CPU instance types | 16 vCPU / 32 GB on the $100 plan and up |
| Files after a stop | The disk stays, but a shut-down instance keeps billing; only Delete stops the charge, and kept volumes bill at $0.20/GiB-month | Model replicas start fresh from the image; preview sandboxes are deleted with their files after 7 days by default | Snapshots every minute and on stop; a stopped sandbox costs nothing and keeps its disk |
| Clone and fork | Clone a volume once its instance is shut down or it is detached | No fork or clone; new replicas cold-start from the image | Fork a running sandbox into many |
| Desktop and computer use | No built-in desktop; a guide to install XFCE and TigerVNC yourself | No desktop or computer-use API | 60fps desktop and Chromium, computer use and browser control, embeddable stream |
| Coding agents | An MCP server and CLI agent mode so an outside agent can manage Verda machines | Baseten Switch points Claude Code or Codex on your laptop at its Model APIs; an agent drives a sandbox through the CLI or MCP | Claude Code, Codex, OpenCode and more preinstalled, driven from outside with a prompt, steer and events API |
| Reaching the machine | A free public IPv4 per instance and SSH; you set up TLS and the firewall yourself | HTTPS endpoint per deployment; sandbox ports through a /port/ route; SSH once enabled for your organization | HTTPS URLs for up to 50 ports, token-protected; raw TCP; dedicated IPv6 or IPv4 |
| Keeping keys out of the machine | Secrets only for serverless containers; on instances you place keys yourself | Secrets are passed into the model as arguments or files under /secrets | AI credentials proxy for model providers and any HTTPS API you register |
| Regions | EU only: three data centers in Helsinki, Finland | US and EU for GPU deployments; self-hosted in your own AWS, GCP or OCI account on Enterprise | EU only: Germany, Finland, France |
| Billing | Prepaid balance charged in 10-minute blocks, unused part refunded | Per minute, rounded up, including image builds and cold starts; no plan fee on Basic | Monthly plan from $20, all of it usage credit spent per second |
Every feature Battleships tracks (43)
| Feature | Verda | Baseten | boat |
|---|---|---|---|
| Full VM (own kernel) | Yes | No | Yes |
| Docker inside | Yes | No | Yes |
| Nested virtualization | Not published | No | Yes |
| Root | Yes | Not published | Yes |
| Systemd | Not published | Not published | Yes |
| Desktop GUI | No | No | Yes |
| Browser | No | No | Yes |
| Computer-use API | No | No | Yes |
| SSH | Yes | Yes | Yes |
| Persistent disk | Yes | No | Yes |
| Pause/resume | No | No | Yes |
| Automatic snapshots | No | No | Yes |
| Fork/clone | Yes | No | Yes |
| Live fork (no pause) | No | No | Yes |
| Memory fork | No | No | No |
| Custom image (Docker or snapshot) | Yes | Yes | Yes |
| ≥24 h sessions | Yes | Yes | Yes |
| Idle auto-stop | No | Yes | No |
| Wake on request | Yes | Yes | No |
| HTTPS preview URLs | Yes | Yes | Yes |
| Raw TCP inbound | No | Not published | Yes |
| Public IPv4 | Yes | No | No |
| Egress allowlist | No | Yes | No |
| Secret proxy | No | No | Yes |
| Secret proxy for any API | No | No | Yes |
| Preinstalled agents | No | No | Yes |
| Agent harness API | No | No | Yes |
| MCP server | Yes | Yes | No |
| GPU | Yes | Yes | No |
| Windows | No | No | No |
| Arm64 | No | No | No |
| Self-hosting | No | Yes | No |
| BYOC | No | Yes | No |
| Open source | No | No | No |
| Scoped API keys | Not published | Not published | Yes |
| Spend limits | Yes | No | Yes |
| Usage per sandbox (API) | Yes | Yes | Yes |
| Audit logs | Yes | Not published | No |
| SSO | No | Yes | No |
| SOC 2 | Yes | Yes | No |
| HIPAA | No | Yes | No |
| EU data residency | Yes | Yes | Yes |
| Zero data retention | Not published | Not published | Yes |
Performance
There is no comparable measurement for either: Verda and Baseten were not part of the public StarSling benchmark run. boat did 26.69 Node.js build-and-test runs per second at 4 vCPU / 8 GB in that run, on dedicated AMD Ryzen 9 9950X hosts. Verda says its CPU instances use dedicated AMD EPYC cores, and Baseten publishes no CPU benchmark.
Node.js build-and-test loop at 4 vCPU / 8 GB, higher is better. Measured with the open hpc-sandbox-benchmarks suite. Verda and Baseten have no published run in this suite yet, so there is no like-for-like number to compare.
Pricing in five scenarios
Each bill is priced from the providers' public price lists by the open Battleships engine, with the cheapest plan that runs the scenario.
| Scenario | Verda, a month | Baseten, a month | boat, a month |
|---|---|---|---|
| One coding agent, workdays 4 vCPU / 8 GB, 50 GB disk, 8 hours a day, 22 days a month, files kept between days | $18 · Pay as you go | $34 · Basic · with limits | $20 · $20/mo |
| One agent that never stops 4 vCPU / 8 GB, 50 GB disk, running 24/7 all month | $45 · Pay as you go | $136 · Basic · with limits | $26 · $20/mo |
| 50 agents in parallel 50 machines of 4 vCPU / 8 GB at once, 8-hour sessions, 1,100 sessions a month, Docker inside | $622 · Pay as you go | $1,701 · Basic · with limits | $317 · $20/mo × 3 seats |
| Sandboxes for your users 2 vCPU / 4 GB, 30,000 sessions of 10 minutes a month, up to 300 at once | $254 · Pay as you go · with limits | $1,246 · Basic · with limits | $200 · $100/mo × 2 seats |
| 1,000 short jobs at once 2 vCPU / 4 GB, 1,000 at once, 3 minutes each, 200,000 jobs a month | $507 · Pay as you go · with limits | $6,228 · Basic · with limits | $2,000 · $2000/mo · with limits |
Verda: Verda charges a prepaid balance in 10-minute blocks and refunds the unused part when you delete early. CPU machines cost $0.012 per vCPU-hour with 4 GB of RAM each, the disk is extra at $0.20/GiB-month, and a shut-down instance keeps billing until you delete it.
Baseten: Baseten bills per minute, rounded up, from image build and cold start until the replica stops; a 4 vCPU / 16 GiB CPU instance is $0.00346 a minute. There is no plan fee on Basic, and sandbox pricing for the private preview is not published.
Baseten: You pick a monthly plan from $20, and all of it becomes usage credit spent per second on running sandboxes. A stopped sandbox costs nothing and keeps its disk.
Limits in those scenarios
Baseten's CPU instances are model containers with no persistent disk, so in every scenario files are lost when a replica stops, and the table marks them as not fitting; its preview sandboxes run at most 10 per organization by default. Verda fits the single-agent and 50-agent scenarios, but does not publish how many instances can run at once, so 300 user sandboxes or 1,000 jobs at once need a higher quota first. Verda's smallest CPU machine is 4 vCPU / 16 GB, so small sandboxes cost the same as medium ones there. On boat, the 1,000-job burst is queued and needs more starts per day than the $2,000 plan allows.
| Scenario | Verda | Baseten | boat |
|---|---|---|---|
| One coding agent, workdays |
|
| None that bite |
| One agent that never stops |
|
| None that bite |
| 50 agents in parallel |
|
| None that bite |
| Sandboxes for your users |
|
| None that bite |
| 1,000 short jobs at once |
|
|
|
Which one should you use?
Use Verda when
you want whole GPU or CPU machines with root in the EU, a very large CPU server, GPU clusters, or spot GPUs.
- Whole VMs with root and a disk that stays. Useful if you want a plain Linux machine: every Verda instance is a full KVM VM with root, SSH and its own public IP.
- Very large CPU machines. Useful for one huge job: Verda CPU instances go up to 360 dedicated vCPU and 1,440 GB of RAM.
- The newest GPUs, on spot too. Useful for training on a budget: Verda rents Nvidia GPUs up to GB300, on-demand or as cheaper spot machines.
Use Baseten when
you ship models to users and want them served for you, scaled to zero when idle, priced per token, or run in the US or your own cloud.
- Models served for you. Useful if you ship a model to users: Baseten runs it as replicas that scale with traffic, down to zero when idle.
- Hosted open models per token. Useful if you just want to call a model: Baseten's Model APIs sell open models priced per token, no deployment needed.
- US region and your own cloud. Useful if you must run in the US or in your own AWS, GCP or OCI account, which Baseten offers on Enterprise.
Where boat fits
Verda and Baseten are GPU clouds, and boat has no GPUs, so model training and serving belong on one of them. If what you are really running is agents that write code, browse and work across days, boat gives each one a ready VM instead. You can keep the GPU work on Verda or Baseten and run the agents on boat.
Free stops that keep the disk
Agents sit idle between tasks. A shut-down Verda instance keeps billing at its full rate until you delete it, and Baseten replicas lose their files when they stop. boat snapshots the disk every minute and a stopped sandbox is free.
Agents and a desktop already inside
On Verda you install the agent, a browser and a VNC server on every VM yourself. Baseten has no desktop and only points a laptop agent at its models. boat ships Claude Code, Codex and a 60fps desktop in every sandbox.
Fork a running machine
Agents branch their work. Verda can only clone a volume after shutting its machine down, and Baseten has no fork, so new replicas cold-start from the image. boat forks a running sandbox into many.
Use boat when you run agents: VMs that come with coding agents, a desktop, HTTPS ports and a credentials proxy, that fork while running and cost nothing while stopped. Compare it one to one: boat vs Verda and boat vs Baseten.
Questions people ask
Is Verda cheaper than Baseten?
For the CPU scenarios we priced, yes. One agent running all month is $45 on Verda and $136 on Baseten, and 50 agents in parallel are $622 versus $1,701. boat comes to $26 and $317 for the same two.
Which is faster, Verda or Baseten?
Neither has a comparable CPU measurement: both were left out of the public StarSling benchmark run. Verda uses dedicated AMD EPYC cores, while Baseten's CPU instances are shared. boat did 26.69 runs per second in that benchmark.
Can I run Docker on Verda or Baseten?
On Verda you have root on your own VM, so you can install Docker. Baseten does not document Docker inside its containers, which run as the non-root app user, though it can run your own Docker image as a model server. Every boat sandbox has root and Docker ready.
Which has more GPUs?
Verda rents Nvidia V100 up to GB300, on-demand or spot, and sells clusters with InfiniBand. Baseten runs T4 up to B200, with up to 8 per instance and multi-node. boat has no GPUs.
Does either have sandboxes for AI agents?
Baseten has sandboxes in private preview: you ask for access, an organization runs up to 10 at once by default, and they are deleted after 7 days by default. Verda sells plain VMs that you set up yourself. boat is built as sandboxes for agents.
Where does my data run?
Verda runs only in Helsinki, Finland. Baseten lets GPU deployments pick the US or the EU, and Enterprise customers can self-host in their own cloud. boat runs only in the EU, in Germany, Finland and France.
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