Modal vs Together AI
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Modal | Together AI |
|---|---|---|
| Pricing | Free $30/month credits; pay-as-you-go rates: $0.0002/1K tokens (Llama 3 8B) inference; GPU compute at $0.79/hr (A10G) to $3.49/hr (H100) | Free tier + pay-as-you-go from $0.0008/1K tokens (Llama 3 8B); custom enterprise pricing for dedicated GPUs |
| Cold Start | Sub-second cold starts for serverless functions; containers spin up from frozen in <200ms | Standard cold starts (seconds) for serverless inference; dedicated instances have near-zero cold start |
| Open-Source Models | Any open-source model via custom container; no curated model library; users self-deploy any Hugging Face model | 100+ open-source models including DeepSeek V4 Pro, Qwen3.7-Max, Llama 4 Maverick, MiniMax-M3 |
| Batch Inference | Supports batch processing with parallel GPU tasks; no explicit per-model token limit, but autoscaling to 1000+ GPUs | Up to 30B tokens per model per batch; dedicated batch pipelines |
| Compute Hardware | H100, A100, A10G; multi-node training up to 128 B200s with Infiniband; elastic across clouds | GB300, GB200, B200, H200, H100; AI Factory custom infrastructure |
| Compliance | SOC2 & HIPAA compliant; data residency controls | ISO 27001:2022 certified |
For teams that need a curated library of 100+ open-source models with high-performance serverless inference and fine-tuning via a managed API, Together AI is the stronger choice. However, if you require sub-second cold starts, instant autoscaling to thousands of GPUs, and full control over your containerized stack (with Python SDK primitives), Modal's infrastructure is more flexible for bursty, unpredictable workloads and multi-node training. Your pick depends on whether you value model selection and out-of-the-box APIs (Together) versus extreme scaling and cold-start performance (Modal).
Full-stack AI cloud for inference, fine-tuning, and pre-training on open-source models.
Visit WebsiteFeature-by-feature
Together AI and Modal both target AI engineers, but differ fundamentally in scope and abstraction. Together AI is a full-stack AI cloud offering serverless inference on 100+ curated open-source models (e.g., DeepSeek V4 Pro, Llama 4 Maverick) with research-optimized FlashAttention-4 kernel tuning, dedicated GPU clusters (GB300, H200), and managed fine-tuning pipelines. It provides a model playground, batch inference up to 30B tokens per model, and integrated sandboxes via CodeSandbox. Modal, conversely, is a Python-native compute platform that gives users full control over containerized workloads, from inference to training. Its key differentiators are sub-second cold starts and instant autoscaling 0→1000+ GPUs, making it ideal for bursty inference traffic. Modal supports multi-node training with Infiniband up to 128 B200s, fine-tuning via SFT/LoRA, and programmatic sandboxes for isolated execution. It lacks a curated model library but integrates seamlessly with Hugging Face and custom containers. Together AI emphasizes production-grade model serving with lower TCO for steady loads (31% more TPS than TensorRT-LLM), while Modal prioritizes latency-sensitive, elastic workloads. Modal’s auto endpoints (recent news) optimize self-owned inference, and together AI’s ISO 27001:2022 certification signals security focus.
Pricing compared
Both platforms operate on freemium models but with different economics. Together AI offers free tier usage (rate-limited) and pay-as-you-go inference starting at ~$0.0008/1K tokens for Llama 3 8B; dedicated GPU clusters require custom enterprise contracts. Modal provides $30/month free compute credits and pay-as-you-go rates: ~$0.0002/1K tokens for Llama 3 8B inference (lower than Together), with GPU compute at $0.79/hr (A10G) to $3.49/hr (H100). For steady-state 24/7 workloads, Together AI’s dedicated instances likely yield better cost efficiency due to reserved pricing. Modal's autoscaling shines for variable loads, but costs can balloon if many GPUs idle. Modal charges for idle time (per-second billing), whereas Together AI's serverless pricing is purely per-token. For massive batch inference, Together AI’s 30B token pipeline may be more predictable, while Modal’s autoscaling incurs overhead for long batches. Both require API calls for cost calculations; no upfront commitments on Modal’s pay-as-you-go, but dedicated Together AI plans push toward enterprise spend.
Who should pick which
- Production coding agent needing high TPS on open-source LLMsPick: Together AI
Together AI offers curated high-performance open-source models (DeepSeek, Llama 4) with 31% more TPS than TensorRT-LLM, plus dedicated GPU clusters for consistent latency. Modal's cold start advantage is less critical for long-lived agents.
- Startup with bursty LLM inference traffic and minimal upfront costPick: Modal
Modal's sub-second cold starts and instant autoscaling from 0 to 1000+ GPUs handle burst traffic efficiently. Free $30/month credits and per-second billing lower the barrier for variable workloads.
- Researcher fine-tuning open-source models with custom training recipesPick: Together AI
Together AI provides research-backed fine-tuning with FlashAttention-4 and ATLAS kernel collection, plus managed datasets and evaluations. Modal's training support is more DIY.
- Developer deploying a multi-node training job with InfinibandPick: Modal
Modal explicitly supports multi-node training up to 128 B200s with Infiniband networking, ideal for large-scale distributed training.
- Enterprise needing SOC2/HIPAA compliance with data residencyPick: Modal
Modal offers SOC2 & HIPAA compliance and data residency controls, aligning with enterprise regulatory needs. Together AI's ISO 27001 is strong but lacks HIPAA emphasis.
Frequently Asked Questions
Which platform has better inference performance for open-source LLMs?
Together AI reports 31% more TPS than TensorRT-LLM for Llama models using FlashAttention-4. Modal doesn't provide similar benchmarks, but its sub-second cold starts and global low-latency network (<10ms overhead) benefit dynamic workloads. For sustained throughput, Together AI likely wins; for bursty real-time, Modal excels.
Can I bring my own model to both platforms?
Yes. Together AI supports custom models via dedicated deployment requests, but its strength is the curated library of 100+ models. Modal allows you to run any containerized model (e.g., from Hugging Face) using a Python SDK, offering full flexibility.
How do their free tiers compare?
Together AI offers a free tier with limited API calls (rate limits not published). Modal gives $30/month in free compute credits, enough for small-scale experiments. Both require credit card for pay-as-you-go beyond free limits.
Which is better for fine-tuning a 70B model?
Together AI provides managed fine-tuning with research-optimized techniques and dedicated GPU clusters (H200, B200). Modal supports fine-tuning via SFT/LoRA on H100s and multi-node training up to 128 B200s. Choose Together for ease-of-use and curated pipeline; Modal for custom training scripts and distributed setups.
Do they support batch processing?
Yes. Together AI offers batch inference up to 30B tokens per model with dedicated pipelines. Modal supports batch processing via parallel GPU tasks and autoscaling, but no specific token cap. Together's batch is more managed; Modal's is more flexible.
Are they compliant with enterprise security standards?
Together AI is ISO 27001:2022 certified. Modal is SOC2 and HIPAA compliant, with data residency controls. For healthcare, Modal is stronger; for general enterprise, both meet high standards.
Can I use these platforms for real-time voice agents?
Together AI offers voice agents for production voice applications, confirmed in its features. Modal supports WebSocket and WebRTC for real-time streaming, but no dedicated voice agent product. Together AI is more turnkey for voice.
Which platform is more developer-friendly for Python users?
Modal is Python-first with composable primitives (decorators, async), local feel, and automatic containerization. Together AI provides Python SDK and Node.js SDK, but the platform is API-driven. Modal wins for Python users wanting an infrastructure-as-code experience.
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