Replicate vs Together AI

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

Replicate

🔴Developer

AI Model Hosting & Inference

Run, fine-tune, and deploy thousands of community AI models with a single HTTP API — covering image, video, audio, language, and embedding models, billed per-second of GPU time.

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Starting Price

Custom

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

Feature Comparison

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FeatureReplicateTogether AI
CategoryAI Model Hosting & InferenceAI Model Hosting & Inference
Pricing Plans158 tiers142 tiers
Starting Price$0.02/1M tokens
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

    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 ResidencyUS
    Data Retentionconfigurable
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