Together AI vs Modal

Detailed side-by-side comparison to help you choose the right tool

Together AI

🔴Developer

AI Model Hosting & Inference

AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.

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Starting Price

$0.02/1M tokens
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Modal

🔴Developer

Model Deployment

Serverless Python cloud built for AI workloads — decorate a function, deploy it in seconds, and get sub-second cold starts on GPUs, autoscaling web endpoints, and long-running jobs without touching Kubernetes.

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Starting Price

Free
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Feature Comparison

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FeatureTogether AIModal
CategoryAI Model Hosting & InferenceModel Deployment
Pricing Plans142 tiers243 tiers
Starting Price$0.02/1M tokensFree
Key Features
  • • Serverless inference APIs for open and proprietary model workloads
  • • Batch Inference API for large asynchronous token processing jobs
  • • Fine-tuning platform for shaping open models with private or domain data
  • • Serverless Python functions and containers
  • • GPU-backed AI training, batch, and inference jobs
  • • Web endpoints, scheduled jobs, queues, and volumes

Together AI - Pros & Cons

Pros

  • ✓Breadth of open-weight model catalog (200+) with one OpenAI-compatible API
  • ✓One account spans serverless, dedicated endpoints, fine-tuning, and reserved GPU capacity
  • ✓Transparent per-token pricing — easy to model unit economics against closed providers
  • ✓InfiniBand-backed GPU Clusters are credible for real training, not just inference

Cons

  • ✗Frontier-class reasoning still lags closed models on the hardest benchmarks
  • ✗Fastest single-model latency is sometimes beaten by Groq or Cerebras
  • ✗Many model variants means model selection itself becomes a project
  • ✗Dedicated endpoint cost calculations require attention to GPU type and utilization

Modal - Pros & Cons

Pros

  • ✓Python decorators provide a short path from local function to autoscaled service
  • ✓GPU choices span inference and training-oriented accelerators
  • ✓Web endpoints, schedules, queues, volumes, and secrets share one runtime
  • ✓Fast image caching and startup behavior suit bursty inference

Cons

  • ✗Usage bills can spike without concurrency, timeout, and scaling limits
  • ✗Modal-specific decorators create some platform coupling
  • ✗Persistent state and complex networking may still need external services
  • ✗Staged credits and Team pricing need manual verification

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🔒 Security & Compliance Comparison

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Security FeatureTogether AIModal
SOC2✅ Yes✅ Yes
GDPR✅ Yes✅ Yes
HIPAA—✅ Yes
SSO—✅ Yes
Self-Hosted❌ No❌ No
On-Prem❌ No❌ No
RBAC—✅ Yes
Audit Log—✅ Yes
Open Source❌ No❌ No
API Key Auth✅ Yes✅ Yes
Encryption at Rest✅ Yes✅ Yes
Encryption in Transit✅ Yes✅ Yes
Data ResidencyUSUS
Data Retentionconfigurablenot specified in the captured content
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Review Together AIReview Modal