# Modal vs WAAS (Whisper as a Service)

Modal records 1,196,021 monthly visits with +30.2% month-over-month growth and Freemium, Contact for Pricing pricing. WAAS (Whisper as a Service) records 0 monthly visits with — growth and Free pricing.

| Metric | [Modal](https://openaitools.app/tool/modal-com) | [WAAS (Whisper as a Service)](https://openaitools.app/tool/waas) |
| :--- | :--- | :--- |
| Website URL | https://modal.com | https://github.com/schibsted/WAAS |
| Categories | AI Developer Tools, AI API, Large Language Models (LLMs), AI Models | AI Speech-to-Text, AI Transcription, AI API, Open Source AI Models |
| Pricing Model | Freemium, Contact for Pricing | Free |
| Monthly Visits | 1,196,021 | 0 |
| MoM Growth Rate | +30.2% | — |
| Primary Market | United States | Global |
| Direct Visits | 845,636 | 0 |
| Review Score | 4.8 | 4.5 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Open Source |

## 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)

## WAAS (Whisper as a Service)

WAAS stands for Whisper as a Service. It wraps OpenAI's Whisper speech-to-text model in a service layer so that transcription becomes an API call instead of a local script. Built and open-sourced by the Schibsted media group (and used to power their VG audio tooling), it ships as Docker containers: a Flask API, a Redis queue, and RQ workers that pull jobs and run Whisper. A separate Jojo front-end lets non-technical users drag in a file and get results without touching the command line.

The workflow is simple. You POST a file to /v1/transcribe along with either an email_callback or a webhook_id and optional parameters such as model, language and task. WAAS enqueues the job, a worker transcribes it asynchronously, and when it is done you get an email with download links or a signed webhook POST. Results can be retrieved as SRT, VTT, timecoded text, plain text or JSON, and the /v1/detect route identifies the language of a file before you commit to a full transcription.

Because WAAS is self-hosted and Apache-2.0 licensed, it is aimed at teams and developers who want Whisper's accuracy without per-minute SaaS fees or sending sensitive audio to a third party. It is not a managed product: you provision the containers, GPU, storage and mail/webhook infrastructure yourself, and the quality of the transcripts is exactly that of the underlying Whisper model you choose.

**Pricing & Plans:** Free and open source. WAAS is released under the Apache-2.0 license and distributed as Docker images/source on GitHub, so there is no licence fee or subscription. Your real costs are the hardware or cloud compute (ideally an NVIDIA GPU with enough VRAM for the Whisper model you run), storage and any email/webhook infrastructure you provide.

- **Jojo web GUI for upload and transcription:** Upload an audio or video file and receive a completed transcription with downloadable SRT, VTT, TXT or JSON output.
- **Asynchronous job queue and REST API:** Submit a POST to /v1/transcribe and WAAS returns a job_id that you poll via /v1/jobs/<job_id> or receive by email or webhook callback.
- **Email and webhook notifications:** Receive an email with download links or let WAAS POST a signed payload (X-WAAS-Signature) to your webhook when the job completes, so you can plug transcription into any pipeline.
- **Browser-based Jojo transcript editor:** Open a .jojo file, play each segment while holding Control, correct mistakes, and save the corrected file locally, with no data leaving your browser.
- **Whisper model and task selection:** Choose from Whisper's tiny through large models per request, and run transcription or translation with automatic language detection via the /v1/detect endpoint.

**Strengths:** Free and fully open source (Apache-2.0), self-hosted with no per-minute vendor fees; Adds queueing, async jobs, email and signed webhooks on top of raw Whisper; Includes the Jojo browser GUI so non-developers can transcribe and correct files; Docker and docker-compose based, with an optional NVIDIA CUDA/GPU setup

**Limitations:** Self-hosting requires Docker, Redis and enough GPU/VRAM (1 GB+ depending on model) to run Whisper efficiently; Not a managed cloud service: you are responsible for uptime, scaling, storage and email/webhook infrastructure; Accuracy and supported languages are inherited from OpenAI Whisper, so very noisy audio or rare language pairs may need manual editing; The queue is simple and job-based without built-in multi-tenant billing or user accounts

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

## Frequently Asked Questions

### How do Modal and WAAS (Whisper as a Service) compare in monthly traffic?

Modal has 1,196,021 monthly visits, while WAAS (Whisper as a Service) has 0. 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%; WAAS (Whisper as a Service) has —.

### 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. WAAS (Whisper as a Service): Free. Free and open source. WAAS is released under the Apache-2.0 license and distributed as Docker images/source on GitHub, so there is no licence fee or subscription. Your real costs are the hardware or cloud compute (ideally an NVIDIA GPU with enough VRAM for the Whisper model you run), storage and any email/webhook infrastructure you provide.

## 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 | Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all b | [.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 | [Deepgram Voice AI](https://openaitools.app/tool/deepgram-voice-ai) | 863,852 | +31.8% | Free Trial | Real-time speech-to-text, text-to-speech and voice agents in one API platform | [.md](https://openaitools.app/tool/deepgram-voice-ai.md) |
| 8 | [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) |
| 9 | [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) |
| 10 | [AssemblyAI](https://openaitools.app/tool/assemblyai) | 453,591 | -6.9% | Freemium, Contact for Pricing | Speech-to-text and voice intelligence APIs for developers | [.md](https://openaitools.app/tool/assemblyai.md) |

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

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