Together AI vs Replicate

Side-by-side — pricing, strengths, tradeoffs, and fit.

Together AI

Fast, low-cost inference for open-source LLMs, plus fine-tuning.

Replicate

Run machine learning models in the cloud with a single API call.

At a glance

Free TierEnterprise
● Established·From Usage-based (per token)
Free TierEnterprise
● Established·From From $0.000225/sec (CPU) to $0.001050/sec (A100)

Overview

One-liner

Fast, low-cost inference for open-source LLMs, plus fine-tuning.

One-liner

Run machine learning models in the cloud with a single API call.

Maturity

Established

Maturity

Established

Open source

✕ No

Open source

✕ No

Pricing

Free tier

✓ Available

Free credits for new users

Free tier

✓ Available

Free credits for new users; no ongoing free tier

Starts at

Usage-based (per token)

Starts at

From $0.000225/sec (CPU) to $0.001050/sec (A100)

Model

Usage-based

Model

Usage-based

Enterprise

✓ Available

Enterprise

✓ Available

Best for

✓ Best for

Serving open-source LLMs cheaplyFast API over many open models
Fine-tuning and dedicated hostingCustomize and scale

✓ Best for

Developers prototyping with open-source ML modelsFastest path from model selection to working API without any infrastructure
Applications with bursty, unpredictable ML inference needsScale-to-zero means you only pay when models are actually running
Teams without ML ops expertiseReplicate handles GPU provisioning, scaling, and model versioning

Not ideal for

✕ Not for

—

✕ Not for

High-volume production inference with predictable loadDedicated GPU instances on cloud providers are cheaper at scale
Latency-sensitive real-time applicationsCold start times of 5-30 seconds are unacceptable for interactive use cases

Strengths

↑ Strengths

Huge open-model catalog
Competitive token pricing
Fine-tuning + dedicated endpoints

↑ Strengths

Easiest way to run open-source ML models
Scales to zero — pay only for compute used
Huge model library with community contributions
Cog packaging tool for custom model deployment

Weaknesses

↓ Weaknesses

Cold starts on serverless

↓ Weaknesses

Cold start latency for infrequently-used models
More expensive than self-hosted GPU for sustained workloads
Limited control over infrastructure and networking
Full Together AI profile →Full Replicate profile →