Modal vs Together AI Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 1, 2026
Overview
Compare key metrics and features at a glance
Modal
https://modal.com
Modal is a cloud infrastructure platform that allows developers and data scientists to run code in the cloud without managing servers or infrastructure. It provides a Python-native interface for running serverless functions, training machine learning models, and deploying AI applications with on-demand GPU and CPU compute. Modal handles scaling, containerization, and dependency management automatically, enabling teams to go from local code to production cloud workloads with minimal configuration.
Together AI
https://www.together.ai
Together AI is a cloud platform that enables developers and enterprises to run, fine-tune, and deploy open-source large language models (LLMs) at scale with high performance and cost efficiency. The platform provides access to a wide range of open-source models including LLaMA, Mistral, and others through a unified API, along with tools for custom model fine-tuning and inference optimization. Together AI also conducts AI research and has developed its own inference infrastructure designed to deliver fast and affordable generative AI capabilities.
Quick Comparison
| Detail | Modal | Together AI |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | Free | Free |
| Plans Available | 3 | 6 |
| Features Tracked | 20 | 15 |
| Founded | 2021 | 2022 |
| Headquarters | New York, USA | San Francisco, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| OpenAI-Compatible APIs | ||
| core | ||
| Automatic Dependency Management | ||
| Autoscaling GPU Clusters | ||
| Batch Job Processing | ||
| Cron Jobs | ||
| Custom Container Runtime | ||
| Dedicated Model Inference | ||
| Fine-Tuning Workflows | ||
| Full-Stack Observability | ||
| GPU-Backed Notebooks | ||
| High-Performance Inference | ||
| High-Throughput Storage System | ||
| Instant GPU Clusters | ||
| Kubernetes & Slurm | ||
| Model Training and Fine-tuning | ||
| Multi-Cloud GPU Pool | ||
| NVIDIA GPU Support | ||
| Pay-As-You-Go Pricing | ||
| Python-Native Code Definition | ||
| Scale to Zero Pricing | ||
| Self-Healing Clusters | ||
| Serverless GPU Inference | ||
| Serverless Inference | ||
| Web Endpoints | ||
| Zero Egress Fees | ||
| integration | ||
| Cloud Bucket Integration | ||
| External Database Connectivity | ||
| Key-Value Dictionaries | ||
| Networking Tools | ||
| Open-Source Model Hub | ||
| Persistent Volumes | ||
| SDK Support | ||
| Task Queues | ||
| security | ||
| Sandboxes for Untrusted Code | ||
| support | ||
| Integrated Logging and Monitoring | ||
Pricing
Compare pricing plans and value for money
Modal
From $0/mo
Price Components
- base_fee: $0/month (30 included)
- seats: $0/user (3 included)
- CPU: $0.0000131/core-second
- Memory: $0.00000222/GiB-second
- Nvidia B200: $0.001736/second
Best For
Python-focused ML teams and startups needing rapid GPU-accelerated model training and inference without managing Kubernetes, containers, or infrastructure scaling.
Together AI
From $0/mo
Price Components
- GLM-5.1 Input Tokens: $1.4/1M tokens
- GLM-5.1 Output Tokens: $4.4/1M tokens
- Llama 3.3 70B: $0.88/1M tokens
- 1x H100 80GB: $3.99/hour
- 1x H200 141GB: $5.49/hour
Best For
Developers and enterprises needing fast, cost-efficient deployment and fine-tuning of open-source LLMs with flexible GPU clusters and serverless APIs.
Integrations
See which third-party services are supported
Supported Integrations
Coming Soon
Integration comparison data for Modal, Together AI is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
Modal
Python-focused ML teams and startups needing rapid GPU-accelerated model training and inference without managing Kubernetes, containers, or infrastructure scaling.
- Python-native serverless platform eliminates manual containerization and dependency management, reducing deployment friction for ML engineers and data scientists
- On-demand access to high-performance GPUs (A100, H100) with per-second billing removes upfront infrastructure costs and commitment lock-in common with traditional cloud providers
- Automatic horizontal scaling to thousands of parallel containers with zero-to-scale capability enables cost-efficient handling of bursty AI workloads without manual orchestration
- Limited to Python ecosystem, excluding teams using Go, Node.js, or other languages that dominate in serverless and edge computing markets
- Series B funding and 11-50 employee count signal smaller scale and fewer enterprise resources compared to hyperscalers (AWS, Google Cloud, Azure) controlling 65% of AIaaS market revenue
Together AI
Developers and enterprises needing fast, cost-efficient deployment and fine-tuning of open-source LLMs with flexible GPU clusters and serverless APIs.
- Serverless inference with OpenAI-compatible APIs and up to 4x faster performance via custom optimizations differentiates from generic cloud providers.
- Instant self-service GPU clusters up to 64 NVIDIA H100/H200 GPUs deploy in minutes with zero egress fees and autoscaling.
- Fine-tuning for 200+ open-source models like LLaMA and Mistral using proprietary data, with dedicated $2,872/month inference options.
- Full-stack observability via Grafana dashboards and pay-as-you-go token-based pricing for cost-efficient scaling.
- Young company founded in 2022 with 51-200 employees may lack the enterprise maturity and global scale of hyperscalers like AWS.
- Focus on open-source models limits access to proprietary LLMs from providers like OpenAI or Anthropic.
- High entry for dedicated options at $2,872/month suits enterprises but may deter small teams preferring fully serverless.
Company Info
Company details and background
Modal
Together AI
Comparison FAQ
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