Modal vs Together AI
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Modal | Together AI |
|---|---|---|
| Best for | ML engineers and developers who want serverless GPU compute for training, fine-tuning, and batch inference without managing infrastructure. | Developers and teams deploying open-source LLMs for production inference, fine-tuning, and batch processing with optimized speed. |
| Pricing | Free tier with $30/month credits; pay-as-you-go for all GPUs with no commitment. | Free tier with $5 credits; pay-as-you-go for inference, fine-tuning, and dedicated instances. |
| Setup complexity | Low – define environment in Python, no YAML or Kubernetes; sub-second cold starts and auto-scaling out of the box. | Low to moderate – OpenAI-compatible API, but some advanced features (dedicated instances, GPU clusters) require configuration. |
| Strongest differentiator | Serverless Python-native environment with sub-second cold starts and elastic scaling across clouds. | 100+ open-source models with optimized inference (FlashAttention-4, ATLAS) and fine-tuning platform. |
| Model support | Bring your own model or any Python package; no curated model hub. | 100+ pre-deployed open-source models (Llama, Mistral, DeepSeek, Qwen, etc.) plus fine-tuning. |
| Integrations | Hugging Face, Weights & Biases, GitHub, AWS S3, GCP Cloud Storage, any Python package. | LangChain, LlamaIndex, Hugging Face, OpenAI-compatible API (wide ecosystem via API). |
Together AI vs Modal serve different primary use cases, so the winner depends on your workload. Together AI wins for teams deploying open-source LLMs in production who need fast, optimized inference on 100+ models without managing containers. Its FlashAttention-4 and ATLAS accelerators deliver up to 4x faster inference, and the OpenAI-compatible API reduces integration friction. Modal, on the other hand, is the better choice for ML engineers who need a general-purpose serverless GPU cloud for training, fine-tuning, and custom AI pipelines with Python-native environment definition and sub-second cold starts. If your priority is rapid deployment of curated open-source models, choose Together AI. If you need flexible, infrastructure-free compute for a wide range of AI workloads (including custom models and non-LLM tasks), Modal is superior.
Feature-by-feature
Core Capabilities: Modal vs Together AI
Modal is a serverless GPU cloud that lets you run any AI workload by defining your environment in Python. It abstracts containers, GPUs, scaling, and caching, enabling sub-second cold starts and instant autoscaling to thousands of containers. Together AI, by contrast, is a full-stack AI platform focused on inference and fine-tuning of open-source models. It offers 100+ pre-deployed models (Llama, Mistral, DeepSeek, Qwen) with optimized inference via FlashAttention-4 and ATLAS runtime-learning accelerators. Modal wins for custom workloads and general-purpose compute, while Together AI wins for out-of-the-box model deployment.
AI/Model Approach: Together AI vs Modal
Together AI provides a curated library of 100+ open-source models with a unified API for inference, fine-tuning, and evaluations. It supports JSON mode, function calling, and voice agents. Modal does not host models; you bring your own model or any Python library (e.g., Hugging Face transformers). Modal excels in flexibility — you can run any model, framework, or custom code. Together AI's approach is better for teams who want to quickly experiment with and deploy popular open-source models without managing infrastructure. Modal is better for those who need to run custom models or non-model Python workloads on GPU.
Integrations & Ecosystem
Modal integrates with Hugging Face, Weights & Biases, GitHub, AWS S3, and GCP Cloud Storage via bucket mounting, plus any Python package. Together AI integrates with LangChain, LlamaIndex, Hugging Face, and is OpenAI-compatible, allowing drop-in replacement for OpenAI API calls. Modal's Python-native integration gives it an edge for teams already embedded in the Python ecosystem. Together AI's OpenAI-compatible API eases migration from proprietary APIs. Both have strong integration stories, but Together AI's API compatibility can reduce switching costs for teams using OpenAI.
Performance & Scale
Modal offers sub-second cold starts and instant autoscaling to thousands of containers across clouds, with elastic GPU capacity. Together AI claims up to 4x faster inference via FlashAttention-4 and ATLAS accelerators. Modal is designed for unpredictable, bursty workloads with minimal latency, while Together AI focuses on throughput optimization for inference. For batch inference and high-volume production, Together AI's optimized runtime may offer better cost-efficiency. For training and dynamic workloads, Modal's scaling and caching reduce idle time. The choice hinges on workload pattern: latency-sensitive inference favors Together AI; variable compute favors Modal.
Developer Experience & Workflow
Modal's workflow is entirely Python-based: write code, deploy functions, and run containers without YAML or Kubernetes. It includes real-time collaborative notebooks, scheduled cron jobs, web endpoints, and integrated logging. Together AI provides an OpenAI-compatible API, fine-tuning platform, evaluations, and developer environments (sandbox). Modal offers a more integrated development environment for Python developers, while Together AI's API-first design suits teams using standard REST clients. Modal's deployment rollbacks and secret management add operational maturity. Together AI's fine-tuning platform and evaluations are unique for model-specific workflows. In 2026, both platforms continue to evolve; Modal is better for custom pipelines, Together AI for model-centric development.
Pricing compared
Modal pricing (2026)
Modal uses a freemium model. The Free plan includes $30/month in free credits — enough for small experiments or light usage. There is no commitment; you pay as you go for GPU compute, storage, and other resources. Pricing is usage-based and covers all GPU types, unlimited concurrency, secrets management, and all features. There are no overage fees per se, but you pay for what you use. No enterprise or team tiers are listed; pricing is transparent and flexible.
Together AI pricing (2026)
Together AI also offers a freemium model. The Free plan grants $5 in free credits. The Pay-as-you-go plan is usage-based and includes access to 100+ models, fine-tuning, and dedicated instances. Pricing varies by model and workload type (e.g., serverless inference, batch inference, dedicated instances). Dedicated GPU clusters (B200, H200, H100, etc.) have their own pricing. Together AI does not publicly list exact per-token or per-hour rates, so exact costs depend on model choice and volume.
Value-per-dollar: Modal vs Together AI
For small-scale experimentation, Modal offers $30/month free credits versus Together AI's $5, making Modal more generous. For heavy inference workloads, Together AI's optimized inference (FlashAttention-4) may reduce cost per token, potentially offering better value for high-throughput LLM serving. For training and fine-tuning, Modal's pay-as-you-go with no commitment is advantageous for variable workloads, while Together AI's dedicated clusters are better for consistent, long-running jobs. Overall, Modal is more cost-effective for versatile compute needs; Together AI may be cheaper for inference at scale on supported models. Teams should benchmark their specific workloads: e.g., running Llama inference on Together AI vs Modal's generic GPU instance to compare costs.
Who should pick which
- Startup ML engineer building custom fine-tuning pipelinePick: Modal
Modal's Python-native environment and serverless GPU scaling allow flexible fine-tuning of any model without managing infrastructure, with $30/month free credits.
- Developer deploying Llama-3 chat app for productionPick: Together AI
Together AI offers pre-deployed Llama-3 with optimized inference (FlashAttention-4) and an OpenAI-compatible API, simplifying deployment.
- Data scientist running batch inference on 10M documentsPick: Together AI
Together AI's batch inference and fine-tuning platform handle high-volume processing with optimized throughput, reducing cost per token.
- Small team needing elastic GPU for varied AI experimentsPick: Modal
Modal's sub-second cold starts and instant autoscaling let teams run diverse experiments (training, inference, data pipelines) without provisioning.
- Enterprise evaluating multiple open-source modelsPick: Together AI
Together AI's 100+ models, evaluations, and single API streamline model comparison; dedicated instances provide consistent performance for governance.
Frequently Asked Questions
Which platform is cheaper for running LLM inference at scale?
Together AI may be more cost-effective for inference on supported models due to its optimized runtime (FlashAttention-4, ATLAS) that reduces token cost. Modal's generic GPU compute could be more expensive per token for high-throughput LLM inference, but provides greater flexibility. You should benchmark your specific model and traffic pattern.
Can I fine-tune a custom model on Modal?
Yes. Modal's serverless GPU lets you run any Python fine-tuning script (e.g., Hugging Face Trainer) with automatic scaling and caching. You define your environment in Python, and Modal handles container orchestration. This is ideal for custom fine-tuning jobs.
Does Together AI support fine-tuning beyond Llama and Mistral?
Together AI's fine-tuning platform supports 100+ models, including Llama, Mistral, DeepSeek, Qwen, and others. You can upload custom datasets and fine-tune for longer contexts. It also offers GPU clusters (B200, H200, H100) for larger jobs.
How do Modal and Together AI compare for non-LLM AI workloads (e.g., Whisper, Stable Diffusion)?
Modal excels for non-LLM workloads because you can run any Python package, including Whisper, Stable Diffusion, or custom models. Together AI primarily focuses on LLMs and may not support these models out of the box. Modal is the better choice for diverse AI tasks.
Which platform is easier to set up for a team already using OpenAI API?
Together AI offers an OpenAI-compatible API, so switching requires minimal code changes. Modal uses a Python SDK and does not provide an OpenAI-compatible API, making it less straightforward for teams wanting to drop-in replace OpenAI. Together AI wins for migration ease.
Can I use Modal for scheduled batch jobs?
Yes. Modal supports scheduled cron jobs, allowing you to run batch inference, data pipelines, or training jobs on a recurring schedule. You define the function and schedule in Python, and Modal handles execution.
Does Together AI provide dedicated instances for consistent performance?
Yes. Together AI offers dedicated model inference on custom hardware and dedicated container inference for custom models. This ensures predictable latency and throughput for production workloads, ideal for enterprises.
Which platform has a better free tier?
Modal's free tier provides $30/month in credits, compared to Together AI's $5. Modal's free tier is significantly more generous for experimentation and small projects.
Can I deploy a web app powered by an LLM using these platforms?
Both offer web endpoints: Modal provides web endpoints with custom domains and static IP proxy, while Together AI offers serverless inference API. For a full web app, Modal's serverless functions can handle both inference and frontend logic, giving more control.
Which platform is better for a team without DevOps expertise?
Modal is designed to eliminate DevOps: no Kubernetes, no YAML, just Python. Together AI's serverless API also abstracts infrastructure, but dedicated instances require some management. Modal has a lower barrier for teams wanting to focus on code, not ops.
Last reviewed: May 12, 2026