Replicate vs Together AI

What Replicate is

Replicate exposes public, official and user-deployed machine-learning models through APIs. Billing depends on the model and deployment mode: public models generally bill active processing time, while private models and deployments can also bill setup and idle time. The practical consequence is that cost tracks how the model is served, not how many requests you make, so an idle private deployment still bills.

What Together AI is

Together AI offers shared serverless inference and dedicated endpoints through the same API surface. Serverless models are billed by token or output unit and use dynamic rate limits; dedicated endpoints reserve hardware and bill by running time. Model availability, license, modality and price must be checked in the current catalog.

What Replicate is built to do

Public model API

Runs public models through a shared queue with usage-based billing rules.

Official models

Offers vendor-maintained models with stable APIs and published input/output pricing.

Custom deployments

Deploys packaged models onto selected hardware with configurable scaling.

Operational controls

Provides deployment monitoring, version updates and rollback support.

What Together AI is built to do

Serverless inference

Shared, per-usage access to supported models without provisioning replicas.

Dedicated endpoints

Reserved hardware with per-endpoint configuration and per-minute billing while running.

Shared API surface

Serverless and dedicated endpoints use the same inference APIs for compatible models.

Multimodal catalog

Current offerings span chat, image, video, audio, embeddings and moderation.

Choose Replicate if

• Bursty inference: hardware is billed per second, from $0.000025/sec ($0.09/hr) on small CPU
• Image and video work priced per output, such as FLUX 1.1 Pro at $0.04 per output image
• Teams that need large GPUs occasionally: Nvidia H100 at $0.001525/sec ($5.49/hr)

Skip Replicate if

• You run steady, high-volume inference — per-second GPU rates beat idle servers only when usage is spiky
• You need a free tier to evaluate; none is published on the pricing page
• You need very large clusters cheaply: 8x H100 runs $0.012200/sec ($43.92/hr)

Choose Together AI if

• Cheap open-model inference: Llama 3 8B Instruct Lite at $0.14 per million tokens in and out
• Prompt-heavy workloads — cached input drops DeepSeek V4 Flash from $0.14 to $0.03 per million tokens
• Teams renting GPUs directly: HGX H100 at $3.99/hour on demand, down to $3.19/hour reserved

Skip Together AI if

• You need a free tier to evaluate; none is published on the pricing page
• You run large frontier models constantly — DeepSeek V4 Pro is $1.32 in and $3.96 out per million tokens
• You want the lowest reserved GPU rate without commitment: those need 7 to 180+ day terms

Replicate: pros & cons

• Public and official catalog: Use community models or vendor-maintained official models.
• Custom deployments: Package and expose your own model with chosen hardware and scaling.
• Usage-based public models: Public model runs generally bill only active processing time.
• Deployment controls: Configure hardware, minimum instances and scale-to-zero behavior.
• Cold starts: Public and scale-to-zero workloads may wait for hardware to boot.
• Idle billing boundary: Private models and deployments can incur idle charges.
• Community variation: Public model ownership, maintenance and outputs are not uniform.
• Workload-specific pricing: Some models bill by compute time and others by input or output.

Together AI: pros & cons

• Two deployment modes: Prototype on serverless and move compatible workloads to dedicated endpoints.
• Multiple modalities: The catalog includes text, image, video, audio, embedding and moderation options.
• No serverless minimum: Supported serverless models bill by actual usage without provisioning.
• Dedicated control: Reserved hardware provides endpoint-specific configuration and avoids shared-fleet limits.
• Catalog changes: Available models, prices and supported deployment modes can change.
• Dynamic limits: Serverless quotas vary by model, capacity and recent successful usage.
• Dedicated idle cost: Reserved endpoints bill while running regardless of request volume.
• Access cost: Together currently documents a minimum credit purchase and no free trial.