# Agent 37 vs Thunder Compute Agent 37 records 180,030 monthly visits with +24.2% month-over-month growth and Free Trial, Contact for Pricing pricing. Thunder Compute records 102,147 monthly visits with -29% growth and Paid (per-minute instances, per-second sandboxes) pricing. | Metric | [Agent 37](https://openaitools.app/tool/agent-37) | [Thunder Compute](https://openaitools.app/tool/thunder-compute) | | :--- | :--- | :--- | | Website URL | https://www.agent37.com | https://thundercompute.com | | Categories | AI Coaching, AI Agent, AI Developer Tools, Large Language Models (LLMs) | AI Developer Tools, AI Models, Large Language Models (LLMs) | | Pricing Model | Free Trial, Contact for Pricing | Paid (per-minute instances, per-second sandboxes) | | Monthly Visits | 180,030 | 102,147 | | MoM Growth Rate | +24.2% | -29% | | Primary Market | Paraguay | United States | | Direct Visits | 72,672 | 40,743 | | Review Score | 4.3 | 4.4 | | Saved Count | 0 | 0 | | Supported Platforms | Website | Website | ## Agent 37 Agent 37 is a cloud infrastructure product aimed at people who need somewhere real for AI agents to run. Instead of a chat window, every customer gets a sandbox: an isolated Linux container with CPU, memory, persistent disk, an optional public HTTPS URL, and outbound network access. The company positions itself primarily on price, claiming its sandboxes cost roughly 94% less than most competing providers and publishing a comparison table against AWS, Daytona, E2B, Modal, Vercel, and others. Billing is prepaid and metered by the minute — vCPU, memory, and disk each have their own unit rate — with no subscription fee. Beyond raw compute, Agent 37 ships a layer of agent-specific conveniences. You can start from a ready-made harness such as Claude Code, Codex, OpenClaw, OpenCode, or Hermes, or bring your own Docker image. Agents keep their files across sleep, stop and restart cycles, and can be given cron schedules so they run unattended and simply report results. Model access can come from the platform gateway (Claude, OpenAI, Gemini, Grok) or from your own API keys, and integrations cover services such as Gmail, Slack, Notion, and GitHub through a Composio-based connector catalog, plus metered access to paid tools like web scraping and people/company data. Security is a major part of the pitch: sandboxes are isolated with gVisor, hosts have no public hostname, ingress goes through Cloudflare with API-key authentication, and outbound traffic is restricted to the public internet. The company states it does not collect prompts, files, or terminal sessions, holds SOC 2 Type I, and is working toward Type II. It also offers startup credits and a free personal agent tier for individuals who just want an agent of their own. **Pricing & Plans:** Prepaid, pay-as-you-go compute metered per minute with no subscription or minimum. New accounts get a $5 starter credit once a card is on file, and the company advertises up to $10,000 in credits for startups. Exact unit rates are published on the pricing page and change over time, so confirm current figures there. - **Per-minute sandbox billing:** Pay for vCPU, memory and disk separately while the sandbox runs; when it sleeps or is deleted the compute meter stops and you are left with disk cost only. - **Ready-made agent harnesses:** Launch Claude Code, Codex, OpenClaw, OpenCode, Hermes, Grok or your own Docker image, each in its own tenant-isolated sandbox. - **Persistent workspace and scheduling:** Files survive sleep, restarts and weeks of inactivity, and agents can be attached to cron-style schedules so work runs unattended. - **Networking and custom domains:** Expose individual ports publicly, attach your own domain, or keep everything behind an API key on an authenticated URL. - **Integrations and metered tools:** Connect 1,000+ apps such as Gmail, Slack, Notion and GitHub, and buy one-off capabilities like scraping or enrichment from the same balance. **Strengths:** Aggressively low per-minute pricing with an explicit public comparison against major sandbox providers; Persistent disk and scheduling make it suitable for real agent workloads rather than one-shot tasks; Strong isolation story (gVisor, firewalled egress, no public hostnames) plus SOC 2 Type I; Flexible model and tool sourcing: platform gateway, bring-your-own keys, or pay-per-use tools **Limitations:** Prepaid balance model means you must top up in advance and monitor spend across many sandboxes; Persona pricing and plan details are only available after contacting the team for the white-label and enterprise tracks; Documentation and benchmark claims are largely self-published, so independent verification is limited [Profile (Markdown)](https://openaitools.app/tool/agent-37.md) ## Thunder Compute Thunder Compute is a cloud GPU provider that markets itself as the GPU sandbox company. Rather than a single generic VM offering, it splits its product into two shapes. Instances are long-lived GPU machines for hands-on development and training, available with RTX A6000, L40, A100 80GB, or H100 SXM GPUs, configurable across GPU count, vCPU, RAM, and disk, and billable per minute only while running. Sandboxes are isolated, short-lived Ubuntu microVMs designed for agent workloads: reinforcement-learning environments, evaluation sweeps, and CI jobs that need a GPU for a few minutes and can then disappear. The company leans on price and developer ergonomics. Its published rates list H100 SXM instances at $3.20 per GPU-hour, A100 80GB at $1.09, L40 at $0.79, and RTX A6000 at $0.35, with per-second sandbox pricing of $2.95 per GPU-hour for H100 and $1.95 for A100. Storage is metered per GB-month with monthly snapshot pricing, and there are no data egress fees. Thunder Compute says it keeps costs down by scheduling workloads efficiently and reducing idle time, and passes those savings on. The workflow is designed to be scriptable rather than click-heavy. A CLI handles instance creation and connection, a VS Code extension supports in-editor iteration, and an MCP server plus REST API let agents and internal platforms provision GPUs programmatically. Snapshots preserve disk state between sessions, and neither instances nor sandboxes can be preempted. The company raised a $13 million Series A in 2026 to expand the platform, and documents the stack through guides, quickstarts, and API references. **Pricing & Plans:** Thunder Compute bills instances per minute while running and sandboxes per second. Published instance rates: RTX A6000 $0.35/GPU-hour, L40 $0.79, A100 80GB $1.09, H100 SXM $3.20, with extra vCPUs at $0.04/vCPU-hour. Sandbox rates are $2.95/GPU-hour for H100 SXM and $1.95/GPU-hour for A100 80GB plus vCPU, memory, and storage components. Instance disk storage is $0.219/GB per month (first 100 GB per GPU included), snapshots are $0.05/GB per month, and data egress is free. Grilled specifically: rates change over time, so verify on the pricing page. - **Persistent GPU instances:** Launch RTX A6000, L40, A100 80GB, or H100 SXM machines, often ready to SSH into within minutes, and pay only while they run. - **Per-second GPU sandboxes:** Spin up isolated Ubuntu VMs with or without a GPU for RL rollouts, evals, and CI, billed per second and scaled to zero between runs. - **Developer-first tooling:** A CLI, VS Code extension, MCP server, and REST API let you drive infrastructure from a terminal, an editor, an AI agent, or your own services. - **Snapshots and persistent storage:** Save an instance disk as a snapshot and restore it later, so you can pause experiments without leaving machines running. - **No preemption, no egress fees:** Instances and sandboxes are never preempted, and outbound data transfer is not billed. **Strengths:** Aggressive pricing versus hyperscalers: published H100 rates around $3.20/GPU-hour against $6+ at major clouds.; Two billing models that match two real workloads: per-minute persistent instances and per-second ephemeral sandboxes.; No egress fees and no preemption, which removes two common sources of surprise cost and interruption.; Real developer surface area: CLI, VS Code extension, MCP server, REST API, and templates. **Limitations:** GPU availability can fluctuate, so a particular type may be sold out when you try to launch it.; Sandbox access is requested through sales rather than being fully self-serve at sign-up.; The platform's GPU catalog is narrower than the largest hyperscalers, which offer a wider range of accelerator families. [Profile (Markdown)](https://openaitools.app/tool/thunder-compute.md) ## Frequently Asked Questions ### How do Agent 37 and Thunder Compute compare in monthly traffic? Agent 37 has 180,030 monthly visits, while Thunder Compute has 102,147. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Agent 37 has a month-over-month growth rate of +24.2%; Thunder Compute has -29%. ### How does pricing compare? Agent 37: Free Trial, Contact for Pricing. Prepaid, pay-as-you-go compute metered per minute with no subscription or minimum. New accounts get a $5 starter credit once a card is on file, and the company advertises up to $10,000 in credits for startups. Exact unit rates are published on the pricing page and change over time, so confirm current figures there. Thunder Compute: Paid (per-minute instances, per-second sandboxes). Thunder Compute bills instances per minute while running and sandboxes per second. Published instance rates: RTX A6000 $0.35/GPU-hour, L40 $0.79, A100 80GB $1.09, H100 SXM $3.20, with extra vCPUs at $0.04/vCPU-hour. Sandbox rates are $2.95/GPU-hour for H100 SXM and $1.95/GPU-hour for A100 80GB plus vCPU, memory, and storage components. Instance disk storage is $0.219/GB per month (first 100 GB per GPU included), snapshots are $0.05/GB per month, and data egress is free. Grilled specifically: rates change over time, so verify on the pricing page. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | The AI developer cloud for GPU pods, serverless inference and clustering | [.md](https://openaitools.app/tool/runpod.md) | | 2 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Rent cloud GPUs by the second from a global marketplace | [.md](https://openaitools.app/tool/vast-ai.md) | | 3 | [Modal](https://openaitools.app/tool/modal-com) | 1,196,021 | +30.2% | Freemium, Contact for Pricing | Serverless cloud infrastructure for AI and data workloads | [.md](https://openaitools.app/tool/modal-com.md) | | 4 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | An AI-native cloud built from silicon to API for training, tuning, and serving models | [.md](https://openaitools.app/tool/nebius.md) | | 5 | [Salad](https://openaitools.app/tool/salad-gpu-cloud) | 713,519 | +10.4% | Free Trial, Paid, Contact for Pricing | Distributed GPU cloud that orchestrates the world's idle consumer GPUs | [.md](https://openaitools.app/tool/salad-gpu-cloud.md) | | 6 | [Browserbase](https://openaitools.app/tool/browserbase) | 283,597 | -8% | Freemium | Cloud browsers and web APIs for AI agents | [.md](https://openaitools.app/tool/browserbase.md) | | 7 | [Lightning AI](https://openaitools.app/tool/lightning-ai) | 272,562 | -12.3% | Freemium, Paid, Contact for Pricing | The AI cloud built by the creators of PyTorch Lightning | [.md](https://openaitools.app/tool/lightning-ai.md) | | 8 | [Novita AI](https://openaitools.app/tool/novita-ai) | 271,371 | -10.4% | Paid | One cloud for 200+ model APIs, agent sandboxes and GPU compute | [.md](https://openaitools.app/tool/novita-ai.md) | | 9 | [FluidStack](https://openaitools.app/tool/fluidstack) | 154,620 | +7.3% | Paid, Contact for Pricing | Gigawatt-scale AI compute, built and operated end to end | [.md](https://openaitools.app/tool/fluidstack.md) | | 10 | [Coder](https://openaitools.app/tool/coder) | 149,417 | -12.8% | Free Trial, Contact for Pricing, Free | Self-hosted infrastructure platform for provisioning secure cloud development workspaces and governing AI coding agents. | [.md](https://openaitools.app/tool/coder.md) | --- Source: https://openaitools.app/compare/agent-37-vs-thunder-compute More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)