# Modal vs Workable

Modal records 1,196,021 monthly visits with +30.2% month-over-month growth and Freemium, Contact for Pricing pricing. Workable records 10,780,627 monthly visits with +5.5% growth and Free Trial, Paid pricing.

| Metric | [Modal](https://openaitools.app/tool/modal-com) | [Workable](https://openaitools.app/tool/workable) |
| :--- | :--- | :--- |
| Website URL | https://modal.com | https://www.workable.com |
| Categories | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models | AI Recruiting, AI Agent, AI CRM |
| Pricing Model | Freemium, Contact for Pricing | Free Trial, Paid |
| Monthly Visits | 1,196,021 | 10,780,627 |
| MoM Growth Rate | +30.2% | +5.5% |
| Primary Market | United States | United States |
| Direct Visits | 845,636 | 5,309,440 |
| Review Score | 4.8 | 4.4 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Website |

## 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)

## Workable

Workable positions itself as agentic software for recruiting and HR. The centrepiece is a native AI recruiting agent that operates inside the applicant tracking system: it continuously sources passive candidates from a 400M+ profile pool, evaluates every inbound applicant against up to 14 job criteria, messages candidates to verify interest and fill gaps, and produces a scored shortlist where strong fits auto-advance and clear rejections are filtered out. Because the agent inherits the platform's permissions and security model, every action is logged, permissioned and reviewable.

Underneath the agent sits a deep, conventional ATS. Teams manage requisitions and approvals, custom pipelines and profiles, interview kits, scorecards and assessments, self-service interview scheduling, and offer management with e-signatures and integrated background checks. One click publishes a job to 200+ boards across 100+ countries, and a drag-and-drop careers page builder plus LinkedIn Preferred and Indeed Platinum partnerships help with visibility. A searchable talent CRM keeps past applicants warm for future roles.

After the hire, Workable keeps going as a full HR platform: automated onboarding portals, employee self-service, employee records and document management, time off and attendance, performance and engagement tools, and payroll reporting. It is also deliberately open — MCP-native with comprehensive coverage for Claude, ChatGPT or custom agents, a documented REST API with webhooks, 270+ integrations, custom reporting dashboards and a mobile-first experience. Pricing combines a free trial with paid plans tiered by company size and modules.

**Pricing & Plans:** Free trial available with paid subscription plans; pricing scales by company size and selected modules, with demo and enterprise options. Check the official pricing page for current rates.

- **AI candidate sourcing:** The agent continuously searches 400M+ profiles, identifies strong fits before they apply and sends each a personalised outreach message for every open role.
- **Automated screening and engagement:** Every applicant is scored against up to 14 job criteria with visible reasoning, then messaged directly to confirm interest and close information gaps before reaching a recruiter.
- **Complete ATS underneath:** Requisitions, pipelines, interview kits and scorecards, interview scheduling, offer letters with e-signatures and integrated background checks in one platform.
- **Job ad syndication and careers pages:** Publish jobs to 200+ boards in 100+ countries and build branded career sites with a drag-and-drop editor, plus LinkedIn and Indeed Apply integration.
- **HR platform after the hire:** Onboarding, employee self-service, time off and attendance, performance reviews and engagement tools sit on the same employee data created during hiring.
- **Open platform and integrations:** MCP-native coverage plus a documented REST API with webhooks, 270+ integrations, custom reporting dashboards and a mobile-first employee and candidate experience.

**Strengths:** Native AI agent does real sourcing, screening and candidate messaging, not just keyword ranking; Runs on top of a complete ATS rather than being a bolt-on tool with limited context; Hiring context carries through to onboarding and HR, so talent data compounds over time; Open and MCP-native: bring Claude, ChatGPT or your own agents while Workable stays the system of record

**Limitations:** Pricing scales with headcount and module selection, so costs grow as you add HR features; Full value depends on adopting both the ATS and HR modules, which is a bigger rollout for small teams; Very feature-rich, so administrators need time to configure workflows, permissions and approvals; Partner job-board and integration advantages vary by region and plan tier

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

## Frequently Asked Questions

### How do Modal and Workable compare in monthly traffic?

Modal has 1,196,021 monthly visits, while Workable has 10,780,627. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Modal has a month-over-month growth rate of +30.2%; Workable has +5.5%.

### How does pricing compare?

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. Workable: Free Trial, Paid. Free trial available with paid subscription plans; pricing scales by company size and selected modules, with demo and enterprise options. Check the official pricing page for current rates.

## 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 | [Recruit CRM](https://openaitools.app/tool/recruit-crm) | 729,799 | -7.8% | Contact for Pricing | Unifies applicant tracking and client management with AI outreach, but is tailored more heavily for agency recruiters. | [.md](https://openaitools.app/tool/recruit-crm.md) |
| 8 | [Kula AI](https://openaitools.app/tool/kula-ai) | 492,326 | +4.2% | Paid, Contact for Pricing | Provides an AI-native ATS with automated sourcing and CRM, but has a steeper learning curve than Workable. | [.md](https://openaitools.app/tool/kula-ai.md) |
| 9 | [Manatal](https://openaitools.app/tool/manatal) | 459,004 | -1% | Free Trial, Paid | Combines an applicant tracking system with powerful AI candidate matching, but offers less built-in employee onboarding. | [.md](https://openaitools.app/tool/manatal.md) |
| 10 | [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) |

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

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)