Verda vs Cerebrium
Both rent Nvidia GPUs, but in different shapes. Verda gives you a full virtual machine with root and SSH that runs until you delete it, while Cerebrium runs your app as containers behind an endpoint that scale with requests and down to zero.
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.
Cerebrium
Cerebrium is a serverless GPU cloud for real-time AI: voice agents, video models, LLMs and other ML apps.
You describe an app in a cerebrium.toml file, with your Python code or your own Dockerfile, and Cerebrium builds it and serves it as HTTP or WebSocket endpoints. It starts containers when requests arrive, scales them across GPUs and regions, and scales them back to zero when traffic stops. It is built for teams that serve models in production, not for agents that need a machine of their own, so this page compares the places where the two overlap.
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 | Cerebrium: price rank | boat: price rank |
|---|---|---|---|
| Software factory 100 at once · 4 vCPU / 8 GB · 2,200 sessions of 8 h a month A software factory needs Docker, root and a whole disk that lasts; Verda has them but bills while stopped, and Cerebrium keeps only /persistent-storage. | 88th of 367, with limits $788 a month | 182nd of 367, with limits $2,008 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 | 221st of 366, with limits $2,534 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 Short code runs suit Cerebrium's per-second containers that scale to zero, and Verda's serverless containers do the same; boat's start limits make it a compromise here. | 99th of 345, with limits $165 a month | 225th of 345, with limits $131 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 Cerebrium keeps only one folder between runs; boat ranks 1st with a full VM per user. | 71st of 361, with limits $629 a month | 230th of 361, with limits $821 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 ranks 20th for RL rollouts and Cerebrium ranks 72nd; boat ranks 34th, and none of the three is a full match. | 20th of 360, with limits $1,097 a month | 72nd of 360, with limits $4,068 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 | 32nd cheapest of 364 $639 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 ranks 10th for CI runners on dedicated EPYC cores, Cerebrium ranks 81st, and boat ranks 4th with Docker and /dev/kvm on every machine. | 10th cheapest of 377 $142 a month | 81st cheapest of 377 $766 a month | 4th cheapest of 377 $86 a month |
| 24/7 dev boxes 1 machine 24/7 · 4 vCPU / 8 GB A dev box needs SSH and a disk that keeps your installs, which Verda has and Cerebrium does not; Verda ranks 36th, Cerebrium 259th and boat 18th. | 36th cheapest of 368 $45 a month | 259th of 368, with limits $102 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 desktop product: Verda has a guide to install a VNC server yourself and Cerebrium has none, while boat streams a 60fps desktop with computer use. | 82nd of 369, with limits $123 a month | 179th of 369, with limits $575 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 | 221st of 367, with limits $287 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 | 223rd of 346, with limits $195 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 | 135th of 338, with limits $3,991 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 | 197th of 360, with limits $191 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 Verda ranks 14th and Cerebrium ranks 28th for GPU eval jobs; both fit, Verda as rented GPU VMs and Cerebrium as GPU containers, while boat has no GPUs. | 14th cheapest of 98 $1,039 a month | 28th cheapest of 98 $1,975 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 | Cerebrium | boat |
|---|---|---|---|
| Kind of product | GPU and CPU cloud: raw VMs, GPU clusters and serverless containers | Serverless GPU inference: your app runs as autoscaling containers behind endpoints | Sandboxes for AI agents: VMs with agents, desktop and hosting ready |
| What you get | A full VM on KVM/QEMU with root, SSH and its own public IP | Containers built from your code or Dockerfile; no SSH, you reach the app through its endpoints | A full VM with its own kernel, root, SSH and systemd on dedicated AMD Ryzen 9 9950X hosts |
| Docker and nested virtualization | Root on your own VM, so you can install Docker; nested virtualization is not documented | The app is the container; Docker inside and /dev/kvm are 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 | T4 to B200 and RTX PRO 6000, up to 8 per container, plus Inferentia2 and Trainium | None, CPU only |
| Biggest CPU machine | Up to 360 dedicated vCPU / 1,440 GB | Up to 48 vCPU / 192 GB on L4 or T4 containers; 128 vCPU / 512 GB on Trainium (Enterprise) | 16 vCPU / 32 GB on the $100 plan and up |
| Stopping a machine | A shut-down instance keeps billing; only Delete stops the charge, and kept volumes bill at $0.20/GiB-month | Containers scale to zero for free, but only the /persistent-storage folder survives | A stopped sandbox costs nothing and keeps its whole disk |
| Snapshots and fork | Clone a volume once its instance is shut down or it is detached; no snapshots of a running machine | No fork; an early-beta memory checkpoint (no files) speeds up new containers of the same build | Snapshots every minute and on stop; fork a running sandbox |
| Scale to zero | Serverless containers scale to zero and start on the next request | Apps scale to zero, start on the next request and grow to many replicas, across regions | No wake on request; you start a stopped sandbox through the API |
| Run time | No documented maximum; an instance runs until you stop or delete it | No container cap, but a request times out after 15 minutes by default, 12 hours at most for async runs | No session cap on paid plans; you set the time to live |
| Desktop and computer use | No built-in desktop; a guide to install XFCE and TigerVNC yourself | No desktop or browser product | 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 | None to run inside; an Agent Skill and a docs MCP server help your agent deploy to Cerebrium | 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 | One endpoint per app on one port, open unless you turn auth on; custom domains on Standard | 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 exposed to the app as environment variables | AI credentials proxy for model providers and any HTTPS API you register |
| Regions | EU only: three data centers in Helsinki, Finland | US East, US Central, Stockholm and Finland; 8 more, such as Tokyo and Sao Paulo, on request | EU only: Germany, Finland, France |
Every feature Battleships tracks (43)
| Feature | Verda | Cerebrium | 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 | No | Yes |
| Persistent disk | Yes | Yes | 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 | No | 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 | No | No |
| GPU | Yes | Yes | No |
| Windows | No | No | No |
| Arm64 | No | No | No |
| Self-hosting | No | No | No |
| BYOC | No | No | No |
| Open source | No | No | No |
| Scoped API keys | Not published | Not published | Yes |
| Spend limits | Yes | Not published | Yes |
| Usage per sandbox (API) | Yes | Yes | Yes |
| Audit logs | Yes | Yes | No |
| SSO | No | No | 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: neither Verda nor Cerebrium was part of the public StarSling CPU benchmark run, so we cannot say which is faster at CPU work. 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 Cerebrium's speed claims are about GPU inference and cold starts.
Node.js build-and-test loop at 4 vCPU / 8 GB, higher is better. Measured with the open hpc-sandbox-benchmarks suite. Verda and Cerebrium 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 | Cerebrium, 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 | $53 · Hobby · 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 | $210 · Hobby · 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 | $2,557 · Hobby · 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 | $715 · Hobby | $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 | $1,681 · Standard | $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, disks are extra at $0.20/GiB-month, and a shut-down instance keeps billing until you delete it.
Cerebrium: Cerebrium bills per second for each vCPU, GB of memory and GPU while a container runs, and the protected tier costs 2x on all compute. Hobby has no plan fee, Standard adds a $100 fee each month with no usage credit, and storage past the first 100 GB costs $0.05 per GB a month.
Cerebrium: Plans start at $20 a month, and the whole fee is usage credit spent per second on running sandboxes. A stopped sandbox costs nothing and keeps its disk.
Limits in those scenarios
Cerebrium does not fit the coding agent, always-on and 50-agent scenarios, because only /persistent-storage survives a stop and the fleet also needs Docker inside, which is not documented. Verda fits those, but it does not publish how many instances can run at once, so 300 user sandboxes or 1,000 short jobs at once are not confirmed until you get a higher quota. Cerebrium's Standard plan fits the 1,000-job burst, while boat's $2,000 plan queues those starts. Neither Verda nor Cerebrium publishes an egress price.
| Scenario | Verda | Cerebrium | 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 GPU or very large CPU machines you log into and keep, such as for training, fine-tuning or long jobs on a full VM in Finland.
- A GPU machine you can log into. For training and fine-tuning, Verda rents full VMs with root and SSH, on GPUs from V100 to GB300, plus InfiniBand clusters.
- Very large CPU machines. For one huge job, Verda CPU instances go up to 360 dedicated vCPU and 1,440 GB of RAM.
- A disk that stays with the machine. For long work on one box, a Verda VM keeps its whole disk and runs with no documented time limit.
Use Cerebrium when
you serve a model, voice or video app as an endpoint that scales with traffic and down to zero, or need regions outside Europe.
- Endpoints that scale with traffic. For serving models, a Cerebrium app scales from zero to many replicas and back, billed per second while it works.
- More regions. For users outside Europe, Cerebrium runs in the US and the EU, with Asia, South America and more on request.
- Large published container limits. For big bursts, Cerebrium publishes 500 CPU containers at once on Hobby and 1,000 on Standard.
Where boat fits
Verda and Cerebrium are GPU clouds, and if you need GPUs, pick one of them. If what you are really running is agents that work inside a machine for hours, boat gives each one a full VM with agents, a desktop and hosting ready, which costs nothing while stopped. Many teams keep the GPU work on a GPU cloud and run the agents on boat.
Idle agents keep their whole machine
Agents sit idle between tasks. A shut-down Verda instance keeps billing at full rate, and Cerebrium resets everything outside /persistent-storage when it scales down. boat stops for free, saves the disk every minute and can fork a running sandbox.
Agents and a desktop already inside
On Verda you install the agent, a browser and a VNC server on a clean VM yourself; Cerebrium has no shell or desktop at all. boat ships coding agents you drive from outside and a 60fps desktop with computer use.
Docker, ports and keys handled
Verda leaves TLS, the firewall and your keys on the machine to you; Cerebrium gives one port and passes secrets in as environment variables. boat has Docker and /dev/kvm, HTTPS on 50 ports, raw TCP and a credentials proxy.
Use boat when you run agents: full VMs with Docker, /dev/kvm, coding agents, a desktop, HTTPS ports and a credentials proxy, that cost nothing while stopped. Compare it one to one: boat vs Verda and boat vs Cerebrium.
Questions people ask
Is Verda cheaper than Cerebrium?
In the CPU scenarios on this page, yes. One agent that never stops is $45 on Verda and $210 on Cerebrium, and 50 agents in parallel are $622 against $2,557, though Cerebrium does not fully fit those. For 1,000 short jobs at once Cerebrium is $1,681 a month and fits, while Verda's $507 is not confirmed at that scale.
Which is faster, Verda or Cerebrium?
We have no comparable CPU measurement for either one, so we cannot say. boat did 26.69 runs per second on the StarSling CPU test at 4 vCPU / 8 GB.
Can Verda or Cerebrium run Docker?
On Verda you have root on your own VM, so you can install Docker. Cerebrium builds your app from a Dockerfile, but running Docker inside the app is not documented. A boat sandbox runs Docker, systemd and /dev/kvm out of the box.
Does a stopped machine cost money?
On Verda, yes: a shut-down instance keeps billing, and only Delete stops the charge. Cerebrium scales to zero for free, but only /persistent-storage is kept. A stopped boat sandbox costs nothing and keeps its whole disk.
Where do Verda and Cerebrium run?
Verda runs in three data centers in Helsinki, Finland. Cerebrium runs in US East, US Central, Stockholm and Finland, with more regions on request. boat runs in the EU only, in Germany, Finland and France.
Does boat have GPUs?
No. boat runs CPU VMs on dedicated AMD Ryzen 9 9950X hosts. If your agents need GPUs to train or serve models, rent them on Verda or Cerebrium.
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