# Anyscale vs Thunder Compute

Anyscale records 49,779 monthly visits with -23.2% month-over-month growth and Paid, Contact for Pricing pricing. Thunder Compute records 102,147 monthly visits with -29% growth and Paid (per-minute instances, per-second sandboxes) pricing.

| Metric | [Anyscale](https://openaitools.app/tool/anyscale-com) | [Thunder Compute](https://openaitools.app/tool/thunder-compute) |
| :--- | :--- | :--- |
| Website URL | https://anyscale.com | https://thundercompute.com |
| Categories | AI Developer Tools, Large Language Models (LLMs), AI App Builder, AI Workflow | AI Developer Tools, AI Models, Large Language Models (LLMs) |
| Pricing Model | Paid, Contact for Pricing | Paid (per-minute instances, per-second sandboxes) |
| Monthly Visits | 49,779 | 102,147 |
| MoM Growth Rate | -23.2% | -29% |
| Primary Market | United States | United States |
| Direct Visits | 28,629 | 40,743 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Website |

## Anyscale

Anyscale packages the open-source Ray project into a managed service so that engineering teams do not have to operate the cluster layer themselves. The pitch is production-scale AI on your own cloud GPUs: you bring the compute, Anyscale handles orchestration, scaling, and the operational work of running Ray workloads reliably. The company was founded by the creators of Ray and remains the primary commercial steward of the project.

The platform is organized around the workloads that dominate modern AI development rather than around a single product. Multimodal data curation runs large pipelines over video, images, text, and audio; distributed model training orchestrates GPU clusters with elastic scaling and last-mile preprocessing; batch embedding generation parallelizes embedding jobs for retrieval and search; and post-training supports RL-style frameworks such as SkyRL and veRL natively built on Ray. Existing libraries like PyTorch, vLLM, SGLang, and XGBoost can be scaled with Python APIs instead of rewrites.

Operationally, Anyscale emphasizes fine-grained hardware allocation, so different parts of a workload can claim CPUs, GPUs, TPUs, or accelerator racks independently, plus efficient distributed communication through Ray's in-memory object store or RDMA. Multi-cloud orchestration, compute governance, and workload observability round out the management story, and a free tier with starter credits lowers the cost of evaluating the platform.

**Pricing & Plans:** Anyscale is a paid, usage-based platform with no permanent free plan; it typically provides starter credits and a free trial for evaluation, and enterprise agreements are quoted through sales based on compute usage and support needs. Because pricing depends on cloud and GPU consumption, exact figures should be confirmed with the vendor.

- **Managed Ray clusters:** Run Ray jobs, services, and libraries on managed infrastructure across thousands of nodes without operating the cluster yourself.
- **Distributed training at scale:** Orchestrate multi-GPU and multi-node training with elastic scaling, last-mile data preprocessing, and GPU observability built in.
- **Multimodal data curation:** Build pipelines that download, filter, and transform video, image, text, and audio data ahead of training.
- **Batch inference and embeddings:** Parallelize embedding generation and batch inference for search, retrieval, and downstream training workloads.
- **Open-source library compatibility:** Scale PyTorch, vLLM, SGLang, and XGBoost workloads with Python APIs rather than rewriting code for the platform.

**Strengths:** Built by the creators of Ray, so the managed layer closely tracks the open-source project; Handles data, training, and inference workloads on one distributed runtime; Works with existing PyTorch, vLLM, SGLang, and XGBoost code; Runs in your own cloud account with multi-cloud orchestration and governance

**Limitations:** No permanent free tier, so evaluation is time-limited before real spend; Pricing is consumption-based and hard to forecast without modeling GPU usage; Most valuable to teams already committed to Ray and scale, less so for small projects

[Profile (Markdown)](https://openaitools.app/tool/anyscale-com.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 Anyscale and Thunder Compute compare in monthly traffic?

Anyscale has 49,779 monthly visits, while Thunder Compute has 102,147. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Anyscale has a month-over-month growth rate of -23.2%; Thunder Compute has -29%.

### How does pricing compare?

Anyscale: Paid, Contact for Pricing. Anyscale is a paid, usage-based platform with no permanent free plan; it typically provides starter credits and a free trial for evaluation, and enterprise agreements are quoted through sales based on compute usage and support needs. Because pricing depends on cloud and GPU consumption, exact figures should be confirmed with the vendor. 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 | [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) |
| 7 | [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) |
| 8 | [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) |
| 9 | [Cirrascale](https://openaitools.app/tool/cirrascale-com) | 27,190 | +17.5% | Paid | Private AI cloud for training and inference workloads | [.md](https://openaitools.app/tool/cirrascale-com.md) |
| 10 | [Cerebrium](https://openaitools.app/tool/cerebrium) | 26,974 | -50.8% | Free Trial, Paid | Serverless GPUs for real-time AI with sub-second cold starts | [.md](https://openaitools.app/tool/cerebrium.md) |

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Source: https://openaitools.app/compare/anyscale-com-vs-thunder-compute

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