Over 90% of AI workloads today run on GPUs, and demand for cloud-based GPU servers has surged by 4x in the last two years.

As machine learning models grow more complex, developers need platforms that simplify deployment without compromising speed or scalability.

That’s where Modal comes in—a serverless cloud built for AI teams, allowing Python code to scale effortlessly with GPU support, fast cold starts, and zero server management.

But while Modal is powerful, it isn’t always cost-effective or flexible enough for every use case.

Whether it’s the need for persistent sessions, on-prem deployment, or lower GPU costs, many teams eventually seek alternatives. In this article, we’ll explore the top 10 Modal alternatives worth considering in 2025.

Modal Alternatives: Factors to Consider

When exploring alternatives to Modal, savvy AI/ML teams should weigh several technical factors to ensure the new platform aligns with their needs.

Key considerations include:

1. GPU Types & Availability: Ensure the platform offers a range of GPUs—from consumer-grade to enterprise (A100, H100, etc.)—with reliable multi‑GPU cluster options.
2. Supported Workloads & Frameworks: Confirm the alternative supports your preferred ML frameworks (TensorFlow, PyTorch, JAX, etc.) and custom container deployments.
3. Developer Experience & Tooling: Seek an intuitive UI, robust CLI/SDK, live log streaming, interactive notebooks, and seamless CI/CD integrations.
4. Persistence & Orchestration Flexibility: Verify support for long‑running, stateful workloads, scheduling, and orchestration beyond ephemeral functions.
5. GPU Utilization & Scheduling Efficiency: Assess how well the provider optimizes GPU usage through efficient scheduling or decentralized pooling to cut costs.
6. Support for Privacy & Compliance: Ensure features like VPC deployment, on‑premises options, and strong compliance certifications meet your regulatory needs.
7. Community & Ecosystem: Consider the maturity of the provider’s community, available documentation, and third‑party integrations for effective support.
8. Overall Fit: Use these criteria as a checklist to choose a Modal alternative that aligns with your cost, performance, and integration requirements.

Now, let’s examine ten noteworthy Modal alternatives making waves in 2025, and see how they stack up.

Top 10 Moal Alternatives for 2025

1. Runpod.io

RunPod is one of the best go-to Modal alternative for teams seeking on-demand GPU compute at lower cost.

It’s a cloud platform that lets you spin up GPU containers (for training or inference) with pay-per-second billing.

RunPod is best for cases where you need flexible access to various GPU types (from low-end to high-end) and want to only pay for actual usage.

Unlike Modal’s pure serverless approach, RunPod gives you more control over container environments and even persistent volumes if needed, while still automating a lot of the DevOps.

It shines for ML practitioners who need to run Jupyter notebooks, fine-tune models, or deploy inference endpoints economically.

In practice, RunPod is great for: ad-hoc model training jobs, hosting Stable Diffusion or LLM APIs on GPUs, and experimentation with different GPU hardware (A4000, A6000, A100, etc.) at a click of a button.

Its fast startup times and template library make it appealing to developers who want quick results without managing cloud instances.

Runpod.io Key Features:

1. Offers an extensive GPU selection from consumer-grade (RTX 3090/4090) to data‑center class (A100 80GB, H100) across multiple regions.
2. Enables serverless container deployment with pre‑configured GPU containers or custom Docker images, plus Quick Deploy templates and persistent storage.
3. Utilizes per‑second billing, so you pay only for actual usage without idle charges.
4. Provides autoscaling and full API/CLI access to launch jobs and scale endpoints dynamically.
5. Delivers fast startup times, with many containers launching in seconds (48% under 200ms) and community‑shared templates for common ML tasks.

Runpod.io Limitations:

1. Lacks ancillary services like managed databases, IoT hubs, or big data tools found on larger clouds.
2. Advanced features (e.g. volume mounting, custom networking) can present a learning curve.
3. Operates solely as a hosted service with no on‑prem option.

Runpod.io Pricing:

1. GPU rates are competitive, with examples like an NVIDIA H100 80GB instance at around $2.60/hour and per‑second billing.
2. Offers two pricing tiers—Secure Cloud for dedicated uptime and Community Cloud for cost‑efficient, preemptible usage, plus trial credits for new users.

2. Cirrascale

‍Via Cirrascale Cloud Services

Cirrascale Cloud Services is best known for providing high-end, multi-GPU infrastructure for AI.

You can think of Cirrascale when you are in need of entire servers or clusters with specialized hardware.

It’s an ideal Modal alternative for teams doing heavy ML training (e.g. large CNNs or Transformers) who want dedicated access to powerful accelerators.

Cirrascale is unique in that it offers every type of AI chip in one cloud: top NVIDIA GPUs (A100, H100, etc.), AMD Instinct GPUs, and even exotic hardware like the Cerebras Wafer-Scale Engine​.

Cirrascale Key Features:

1. Rent dedicated multi‑GPU servers with up to 8 GPUs (e.g., 8× NVIDIA A100 or 8× AMD MI100) for distributed training and high‑throughput inference.
2. Offers a wide variety of AI accelerators—including NVIDIA, AMD Instinct, Qualcomm Cloud AI chips, and Cerebras systems—in one unified environment.
3. Provides transparent, no‑surprise billing with fixed monthly or term‑based pricing quoted as $/GPU‑hour, along with optimized high‑speed networking and NVMe storage.
4. Features high‑performance interconnect options like InfiniBand for efficient multi‑node training.
5. Delivers enterprise‑grade support with white‑glove services for cluster setup, model deployment, and an Inference Cloud for optimal routing.

Cirrascale Limitations:

1. Short experiments can be costly due to a pricing model favoring reserved capacity over flexible, short‑term use.
2. Lacks serverless flexibility—resources are provisioned as VMs or bare‑metal, requiring manual shutdown to stop billing.
3. Has a steeper learning curve with more DevOps overhead compared to simpler, plug‑and‑play platforms.

Cirrascale Pricing:

1. Standard configurations (e.g., 8× NVIDIA RTX A4000 server) are about $1,999/month on a monthly term (~$0.34/GPU‑hour), dropping to ~$1,599/month on an annual term.
2. High‑end GPU servers (e.g., 8× A100 at ~$2.17/GPU‑hour equivalent and 8× H100 at around $19,999/month) are available, with custom quotes for large deployments.

3. LastMile AI

‍Via LastMile AI

Another emerging platform that is ideal for developers building generative AI applications who want an all-in-one toolkit from prototyping to deployment is LastMile AI.

Think of LastMile as a Swiss Army knife for AI teams: it combines a notebook-like experimentation environment, synthetic data generation, model fine-tuning, and even an evaluation suite, all in one cloud service.

It’s best for scenarios where you need to rapidly iterate on prompt engineering, fine-tune models (like LLMs or diffusion models), and set up evaluations – essentially covering the “last mile” of AI app development (from a prototype to a production-ready solution).

LastMile AI Key Features:

1. Offers interactive AI Workbooks for multi‑modal experimentation, allowing text, image, and audio model chaining.
2. Provides parametrized templates to create reusable, multi‑modality workflow pipelines.
3. Includes synthetic data generation (AutoEval) for creating diverse, labeled datasets to augment training.
4. Supports a fine‑tuning suite enabling in‑platform customization of popular models (e.g., GPT‑3.5, Stable Diffusion).
5. Delivers one‑click deployment of fine‑tuned models as real‑time endpoints with integrated monitoring and guardrails.

LastMile AI Limitations:

1. Focused primarily on generative AI, making it less suitable for standard ML workflows like tabular data tasks.
2. Offers limited GPU control since compute is fully managed, reducing flexibility over environment and scaling.
3. As a newer platform (launched ~2023), its integrations and community extensions are still evolving.

LastMile AI Pricing:

1. Freemium tier offers basic usage for free, including limited fine‑tuning and evaluation runs.
2. Growth Plan is around ~$50 per user/month (billed monthly), with custom Enterprise plans available on request.

4. MassedCompute.com

‍Via MassedCompute.com

For developers or organizations that want affordable, bare-metal GPU compute with a simple interface and strong customer support is MassedCompute.com.

In many ways, Massed Compute plays in the same space as RunPod or Vast.ai – offering rentable GPUs by the hour – but it differentiates itself with an emphasis on direct ownership of hardware (they own and operate their servers) and white-glove support.

Massed Compute is best for users who need to run both interactive and batch workloads on reliable GPU VMs without dealing with cloud vendor complexity.

It’s also well-suited for those who want an API to programmatically provision instances, which is great for setting up CI pipelines or dynamic scaling in your applications.

Massed Compute Key Features:

1. On‑demand GPU/CPU instances with pre‑installed AI frameworks and multi‑GPU setups in Tier III data centers.
2. Comprehensive NVIDIA GPU catalog—from older cards to cutting‑edge options like A100 80GB and RTX A5000—for budget or high‑performance needs.
3. Robust Inventory API that lets you programmatically provision, reboot, or delete instances.
4. Transparent hourly pricing with no hidden fees—bandwidth and storage are included at no extra cost.
5. Dedicated support from experienced IT professionals and a virtual desktop interface for GUI access.

Massed Compute Limitations:

1. Limited global reach as a smaller provider, with most infrastructure based in the U.S. leading to potential latency issues overseas.
2. Purely infrastructure‑focused; it lacks managed services like databases, serverless functions, or full MLOps pipelines.
3. A smaller, growing community with less extensive documentation and third‑party support compared to major clouds.

Massed Compute Pricing:

1. GPU hourly rates start as low as ~$0.52/hour for an RTX A5000 (24GB) and ~$0.31/hour for a 48GB RTX A6000.
2. High‑end GPUs (e.g., H100) are competitively priced at approximately ~$2–$3/hour, with no long‑term commitment required and bulk discounts available.

5. Clear.ml

‍Via ClearML

ClearML is another Modal alternative for teams seeking an open-source, end-to-end MLOps platform.

ClearML excels in environments where reproducibility and collaboration are key: every run is logged, and you can easily clone past experiments, compare results in a dashboard, and even launch notebooks or scripts on remote compute with one command.

Unlike Modal (which is closed-source and cloud-only), ClearML can be self-hosted or run in your VPC, making it a top choice for enterprises that need privacy or deeper system integration.

Essentially, ClearML is best for ML engineers and data scientists who want an all-in-one solution to “glue” together their data, code, and compute, turning ad-hoc processes into a managed pipeline.

ClearML Key Features:

1. Automatically logs and compares hyperparameters, metrics, and artifacts via an interactive dashboard, enabling comprehensive experiment tracking and remote debugging.
2. Includes a built-in job scheduler and pipeline engine to orchestrate and schedule jobs across on‑prem or cloud compute resources.
3. Offers ClearML Data for dataset versioning and lineage tracking, ensuring reproducible and dataset‑centric workflows.
4. Provides ClearML Serving to deploy models as scalable REST APIs with auto‑scaling capabilities.
5. Is fully open‑source (Apache 2.0 licensed) with a lightweight self‑hosted server and extensive integrations via a thriving GitHub community.

ClearML Limitations:

1. The initial setup and learning curve can be steep due to its comprehensive feature set and self‑hosting requirements.
2. It does not include compute; you must supply your own servers or cloud instances, making it solely an orchestration tool.
3. The web UI and some advanced features may feel less polished compared to commercial, plug‑and‑play platforms.

ClearML Pricing:

1. The Community edition is free for up to 3 users, with costs limited to your own infrastructure expenses.
2. The managed Pro plan is approximately $15 per user/month (up to 10 users), with custom pricing available for larger teams or enterprise needs.

6. MimicPC

‍Via MimicPC

MimicPC is a unique, cloud‑based AI workstation designed for creative and experimental use.

It comes pre‑loaded with over 20 AI tools and APIs. It includes:

• Stable Diffusion
• Whisper
• ComfyU

It allows artists, content creators, and developers to run generative AI apps without installation hassles.

MimicPC supports custom model uploads and LoRA training, providing an all‑in‑one "AI sandbox" environment that lowers the barrier to entry for innovative projects.

MimicPC Key Features:

1. Pre‑installed AI apps: Over 20 popular tools (e.g., Stable Diffusion variants, FLUX, face swapping, video generators, text‑to‑speech) are ready-to-use from a unified dashboard.
2. No‑code web interface: Operate a full browser‑based GUI to launch tools and workflows—no Terminal required.
3. Workflow automation: Visual builders (like ComfyUI/Flowwise) let you chain model components (e.g., OCR to summarization) effortlessly.
4. Custom model training: Fine‑tune models and LoRA adapters directly on the platform using integrated tools (e.g., Kohya_ss).
5. Cloud storage & API integration: Comes with private storage (e.g., 50GB) and supports API integration for seamless external service connections.

MimicPC Limitations:

1. Niche focus: Designed mainly for generative AI, it’s less suited for standard ML workflows or large-scale multi‑GPU training.
2. Resource limits: Typically limited to one powerful GPU at a time; heavy concurrent tasks might slow the interface.
3. Service maturity: As an emerging platform, occasional interface quirks or lag can occur, though improvements are underway.

MimicPC Pricing:

1. Pay‑as‑you‑go: Start with a free $0.50 trial credit; GPU hourly rates range from $0.49 to $1.99 depending on hardware.
2. Subscription plans: Essential at $13.95/month (discounted to $6.98 initially) and Advanced at $26.95/month (discounted to $13.48), with annual plans offering extra storage and credits.

7. Heimdall ML

‍Via Heimdall

Heimdall ML is ideal for teams and developers seeking a rapid path from raw data to a deployed machine learning model with minimal coding.

The platform leverages automated machine learning to handle data preprocessing, model selection, training, tuning, and bias evaluation.

It then auto-generates an API endpoint with required parameters and live test capabilities.

Perfect for small businesses or non‑experts, Heimdall ML enables users to upload CSV files, build forecast or churn prediction models, and seamlessly integrate them into applications—essentially functioning as an automated ML engineer that democratizes access to machine learning.

Heimdall ML Key Features:

1. Automatically ingests raw data, cleans, preprocesses, and trains multiple models while selecting the best based on target metrics.
2. Generates detailed insight reports with performance metrics and bias/feature importance analysis.
3. Instantly deploys models as REST API endpoints with documented input schemas and live testing.
4. Supports NLP and vision tasks with pre-built models for sentiment analysis and image classification.
5. Provides a no‑code, browser‑based interface with natural language guidance and open‑source transparency.

Heimdall ML Limitations:

1. AutoML limits flexibility for experts who want to customize model selection and feature engineering.
2. Designed for individual models, making complex ensembles or very large datasets challenging.
3. Being a new platform (launched ~2023), its documentation and external integrations are still evolving.

Heimdall ML Pricing:

1. Offers a free Hobby tier for moderate datasets (up to ~10k rows, 80 columns) with no cost.
2. Pro plans start at approximately $5/month per user, with custom pricing available for enterprise needs.

8. Lumino AI

‍Via Lumino AI

Lumino AI is a decentralized cloud platform that transforms AI training by aggregating underutilized GPUs from a diverse global network.

It enables organizations to access high‑performance GPUs at costs up to 80% lower than traditional clouds.

With serverless training jobs and auto‑scaling capabilities, Lumino AI ensures you only pay for active compute time, maximizing efficiency.

The platform offers pre‑configured templates for common AI workflows, robust security with cryptographic proofs of model integrity, and seamless integration with existing pipelines.

Ideal for enterprises and startups scaling deep learning models, it fosters transparency and trust in the training process.

Lumino AI Key Features:

1. Aggregates decentralized GPU providers, cutting GPU-hour costs by up to 80%.
2. Enables serverless training jobs that auto‑scale, charging only for active compute.
3. Offers pre‑configured templates that deploy common workflows within seconds.
4. Provides cryptographic proofs for model integrity and tamper‑resistance.
5. Ensures privacy with secure data handling while offering access to in‑demand GPUs.

Lumino AI Limitations:

1. As an emerging, decentralized solution, network performance and maturity may vary.
2. Focuses primarily on training with limited inference/serving capabilities.
3. Currently in devnet, so UI polish and documentation are still evolving.

Lumino AI Pricing:

1. Claims GPU costs up to 80% lower than major clouds (e.g., an 80GB A100 at ~$0.50–$0.75/hr).
2. Uses usage‑based billing with free devnet credits and custom enterprise plans available.

9. CloudSoul

‍Via CloudSoul

CloudSoul shines for rapid cloud deployment and troubleshooting tasks like spinning up instances, adjusting autoscaling, analyzing cost breakdowns, and checking security configurations are made conversational.

This is particularly useful for small teams or startups that don’t have a dedicated cloud engineer – CloudSoul can help set up compliant AWS environments, configure resources, and optimize costs with simple commands.

CloudSoul is essentially an “AI cloud consultant,” making it ideal for scenarios where you might use Modal to run code, but still need to manage underlying cloud resources (networks, storage, etc.) or integrate with non-ML infrastructure.

CloudSoul Key Features:

1. Enables natural language cloud commands to automatically provision AWS resources via API calls or Terraform (e.g., “Launch an EC2 for a web server with load balancer”).
2. Provides real‑time configuration guidance that suggests optimal settings (like enabling S3 versioning and encryption).
3. Automatically detects and fixes misconfigurations, warning against potential security issues.
4. Offers compliance templates that set up fully compliant cloud environments (e.g., HIPAA, SOC2).
5. Delivers cost insights and optimization suggestions to help reduce unnecessary expenses.

CloudSoul Limitations:

1. Currently focused solely on AWS, limiting utility for Azure or GCP users.
2. As a general cloud ops tool, it lacks ML-specific functionalities like model training or experiment management.
3. Being relatively new, some engineers may need time to fully trust its AI-driven infrastructure changes in complex scenarios.

CloudSoul Pricing:

1. Starter Pack is approximately €59/month (~$65) for unlimited natural language deployments on AWS for one user, with a free trial available.
2. Higher team/pro plans and enterprise tiers offer multi‑user support, advanced integrations, and custom compliance features, with pricing in the low hundreds per month.

Why Is Runpod.io Still a Leading Choice?

Among all these alternatives, RunPod.io remains a top choice for AI/ML workloads by offering on‑demand GPU/CPU instances with pre‑configured AI frameworks in Tier III data centers.

With a vast GPU selection—from consumer-grade RTX 3090s/4090s to high‑end A100s and H100s—users can select configurations to optimize performance or budget.

Its serverless containers and “Quick Deploy” templates streamline launching complex AI workflows, while its robust API and CLI enable programmatic management.

Billing is per second with no idle charges, and free trial credits further enhance accessibility.

Although it focuses primarily on computing without ancillary services, its competitive pricing tiers make it an affordable, reliable option for startups, academics, and enterprises.

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