Replicate vs Together AI

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

Replicate

🔴Developer

AI infrastructure

Replicate helps teams with API access to hosted machine-learning models; live pricing and feature details require manual verification.

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Starting Price

Usage based
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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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Feature Comparison

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FeatureReplicateTogether AI
CategoryAI infrastructureAI Model Hosting & Inference
Pricing Plans2 listed plans4 listed plans
Starting PriceUsage based$0.02/1M tokens
Key Features
  • • Thousands of open-source models via a single API
  • • FLUX, Stable Diffusion, Whisper, Kling, Hunyuan, SDXL, LLMs
  • • Cog format for packaging and publishing your own models
  • • 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

Replicate - Pros & Cons

Pros

  • ✓Largest catalog of community models — FLUX, Whisper, MusicGen, SVD all live here first
  • ✓Cog gives an honest portability story: same container runs locally, on Replicate, or on your own infra
  • ✓Per-output pricing for popular models hides GPU complexity for product teams
  • ✓Deployments let you trade cold-starts for predictable latency without leaving the platform

Cons

  • ✗Per-token text inference is usually cheaper on dedicated LLM providers like Together AI or Groq
  • ✗Cold-start latency on rare models can be 10–30s without a Deployment
  • ✗Quotas and per-account concurrency limits surprise teams that scale fast
  • ✗No built-in fine-tuning UI for most model families — you bring training to a Cog container

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

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🔒 Security & Compliance Comparison

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Security FeatureReplicateTogether AI
SOC2—✅ Yes
GDPR—✅ Yes
HIPAA——
SSO——
Self-Hosted—❌ No
On-Prem—❌ No
RBAC——
Audit Log——
Open Source—❌ No
API Key Auth—✅ Yes
Encryption at Rest—✅ Yes
Encryption in Transit—✅ Yes
Data Residency—US
Data Retention—configurable
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Review ReplicateReview Together AI