# Inquir Compute vs Modal

Inquir Compute records 769 monthly visits with -59.1% month-over-month growth and Freemium pricing. Modal records 1,196,021 monthly visits with +30.2% growth and Freemium, Contact for Pricing pricing.

| Metric | [Inquir Compute](https://openaitools.app/tool/inquir) | [Modal](https://openaitools.app/tool/modal-com) |
| :--- | :--- | :--- |
| Website URL | https://inquir.org | https://modal.com |
| Categories | AI Search Engine, AI Chatbot, AI API, No-Code&Low-Code | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models |
| Pricing Model | Freemium | Freemium, Contact for Pricing |
| Monthly Visits | 769 | 1,196,021 |
| MoM Growth Rate | -59.1% | +30.2% |
| Primary Market | Global | United States |
| Direct Visits | 305 | 845,636 |
| Review Score | 3.8 | 4.8 |
| Saved Count | 1 | 3 |
| Supported Platforms | Website | Website |

## Inquir Compute

Inquir Compute is a cloud platform for running applications, positioned as an alternative to platforms like Railway, Render, Fly.io, Vercel Functions and AWS Lambda. The workflow follows a pattern developers already know: connect a repository or container image, let the platform build it, then promote the release to production. It also keeps the previous release around as a reserve for quick rollbacks.

What distinguishes it is how much it packs into a single project. Container apps, serverless functions, scheduled pipelines, background workers and databases all live side by side, connected over a private network rather than the public internet. You can provision PostgreSQL with pgvector for embeddings, Redis for caching, and MongoDB or MySQL as needed, and wire them to your services without external exposure. Observability panels cover memory, CPU, request latency and network egress so you can spot a service nearing its limits and add replicas.

Access is flexible: a web console with an integrated editor, an Inquir CLI for deploying functions and containers from the terminal, and MCP-based deployment from AI coding tools like Claude Code, Cursor and Codex. The platform is priced in roubles with a 14-day free trial, Simple and Pro subscription tiers, and an Enterprise/on-premise option for organizations that need it in their own infrastructure.

**Pricing & Plans:** Paid with a free trial. A 14-day free trial is available without a card (once per account). Simple is 590 roubles/month and Pro is 2,490 roubles/month (yearly billing gives two months free), while Enterprise/On-Premise is a custom quote. Prices are in roubles and payment is handled through the platform's payment provider.

- **Dockerfile and container deploys:** Deploy from a Dockerfile or container image; Inquir builds the image, cuts a release and switches traffic when readiness checks pass.
- **Private networking:** Bring apps, functions and databases into one project and let them talk over an internal network instead of the public internet.
- **Serverless functions and pipelines:** Run synchronous and asynchronous functions, scheduled jobs and pipelines with run history, including background processing for web apps.
- **Managed databases:** Provision PostgreSQL (with pgvector), Redis, MongoDB and MySQL alongside your services, complete with persistent volumes.
- **Observability and scaling:** Watch memory, CPU, network and latency, then add replicas when an app approaches its limits.
- **CLI, IDE and API access:** Deploy from the web console, the Inquir CLI or AI coding agents over MCP, with custom domains and API key auth for serverless functions.

**Strengths:** Apps, functions, pipelines and databases managed together in one project with private networking; Deploy from Dockerfile or container image, with rollback to the previous release; Managed PostgreSQL with pgvector, Redis, MongoDB and MySQL included; Observability and replica scaling built in, plus CLI and MCP-based deployment

**Limitations:** Interface and pricing are currently oriented toward Russian-speaking users and local payment methods; Public prices are in roubles and card payments were rolling out gradually at the time of review; Less aimed at casual/free users, since the free tier is a 14-day trial rather than a permanent plan; Managed hosting means less low-level control than running your own servers

[Profile (Markdown)](https://openaitools.app/tool/inquir.md)

## Modal

Modal provides cloud infrastructure aimed squarely at AI and data teams. The core idea is that developers write ordinary Python, decorate functions or define container images, and Modal takes care of the underlying compute: scheduling containers, attaching GPUs, autoscaling from zero and back, and exposing endpoints over HTTPS. Infrastructure concerns like networking, storage, and capacity management are abstracted away, so a workload that runs on a laptop can run the same way across a fleet.

The platform is organised around a few primitives. Functions handle serverless compute and can fan out across thousands of GPUs for parallel or batch work. Sandboxes provide isolated environments for coding agents, reinforcement learning rollouts, and other untrusted code, with sub-second start times and support for very high concurrency. Training covers fine-tuning, reinforcement learning, and evaluation on single or multi-node GPU capacity. On the inference side, Modal offers OpenAI-compatible endpoints for open-weight models and lets teams deploy custom stacks and tune endpoints to match their traffic shape.

Underneath, Modal runs its own container runtime and image system designed for large images and fast GPU scheduling, along with a distributed filesystem for fast access to model weights and datasets. Capacity is drawn from a multi-cloud pool spanning many regions, with automatic routing to available hardware and continuous fleet health checks. Billing is usage-based, charged by the second while code runs, with granular cost controls. The company offers a free starting allowance and scalable paid usage, and markets to enterprises with SOC 2 Type II compliance and HIPAA readiness.

**Pricing & Plans:** Modal uses usage-based pricing billed by the second: a free monthly compute allowance gets you started, then you pay for the CPU, memory, and GPU resources your workloads consume. Enterprise plans and startup credits are available, and custom pricing can be discussed with sales.

- **Serverless Python functions:** Write plain Python, attach GPUs or containers, and deploy HTTPS endpoints that autoscale with traffic and scale to zero when idle.
- **Sandboxes for agents:** Launch isolated, secure environments for coding agents, RL rollouts, and untrusted code, with sub-second starts and support for over a million concurrent sandboxes.
- **Training and fine-tuning on demand:** Run fine-tunes, LoRA, full training, and reinforcement learning on elastic single or multi-node GPU capacity with no quota requests.
- **Model inference and endpoints:** Serve LLMs and image, video, or audio models through OpenAI-compatible endpoints, or bring your own inference stack and let Modal handle the infrastructure.
- **Fast storage and multi-cloud capacity:** A distributed filesystem loads large images and model weights at full bandwidth, while a pool spanning 20+ clouds routes workloads to the right hardware and region.

**Strengths:** Python-native developer experience with almost no infrastructure boilerplate; Scales from zero to thousands of GPUs with per-second, usage-based billing; Sandboxes are well suited to agentic and untrusted-code workloads; Multi-cloud capacity spans many regions with out-of-the-box observability and enterprise compliance

**Limitations:** Billed by real usage, so heavy GPU workloads can get expensive quickly without cost controls; Some advanced inference and enterprise features require working with the team rather than self-serve signup; The serverless model can feel less familiar to teams used to managing their own VMs or Kubernetes clusters

[Profile (Markdown)](https://openaitools.app/tool/modal-com.md)

## Frequently Asked Questions

### How do Inquir Compute and Modal compare in monthly traffic?

Inquir Compute has 769 monthly visits, while Modal has 1,196,021. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Inquir Compute has a month-over-month growth rate of -59.1%; Modal has +30.2%.

### How does pricing compare?

Inquir Compute: Freemium. Paid with a free trial. A 14-day free trial is available without a card (once per account). Simple is 590 roubles/month and Pro is 2,490 roubles/month (yearly billing gives two months free), while Enterprise/On-Premise is a custom quote. Prices are in roubles and payment is handled through the platform's payment provider. Modal: Freemium, Contact for Pricing. Modal uses usage-based pricing billed by the second: a free monthly compute allowance gets you started, then you pay for the CPU, memory, and GPU resources your workloads consume. Enterprise plans and startup credits are available, and custom pricing can be discussed with sales.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Extremely fast serverless execution optimized specifically for generative media models rather than general Python code. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 2 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | Provides flexible GPU infrastructure and serverless endpoints, though less focused on pure Python function decorators. | [.md](https://openaitools.app/tool/runpod.md) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Focuses heavily on managed open-source model inference APIs rather than arbitrary custom container execution. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [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) |
| 5 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | Delivers robust enterprise GPU clusters and managed inference, requiring more infrastructure setup than Modal's serverless approach. | [.md](https://openaitools.app/tool/nebius.md) |
| 6 | [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | -8.4% | Paid | Easier model packaging via Cog, but offers less arbitrary serverless Python container execution than Modal. | [.md](https://openaitools.app/tool/replicate-ai.md) |
| 7 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Low-cost serverless inference APIs for 200+ open models | [.md](https://openaitools.app/tool/deep-infra.md) |
| 8 | [Zeabur](https://openaitools.app/tool/zeabur) | 275,642 | -15.9% | Free Trial, Paid | AI DevOps engineer that deploys and scales your apps | [.md](https://openaitools.app/tool/zeabur.md) |
| 9 | [Lightning AI](https://openaitools.app/tool/lightning-ai) | 272,562 | -12.3% | Freemium, Paid, Contact for Pricing | Provides integrated development studios and training workflows, but with less granular serverless function orchestration. | [.md](https://openaitools.app/tool/lightning-ai.md) |
| 10 | [Novita AI](https://openaitools.app/tool/novita-ai) | 271,371 | -10.4% | Paid | Combines ready-to-use model APIs and serverless GPU jobs, with simpler API consumption than custom Modal apps. | [.md](https://openaitools.app/tool/novita-ai.md) |

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Source: https://openaitools.app/compare/inquir-vs-modal-com

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