Groq vs Together AI

What Groq is

GroqCloud hosts a defined catalog of production and preview models behind Groq and mostly OpenAI-compatible APIs. Its model table publishes estimated token speed, pricing, context windows and developer limits. Actual workload latency and output quality still depend on the selected model, prompt, load and account limits.

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 Groq is built to do

Hosted model API

Calls active production and preview models using documented model IDs.

OpenAI compatibility

Supports OpenAI client libraries with a Groq base URL, subject to documented differences.

Published model metrics

Lists indicative token speed, price, context and limits for supported models.

Rate and spend controls

Provides quota headers, usage monitoring, spend limits and budget alerts.

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 Groq if

• Interactive workloads where measured response latency is a primary requirement
• Teams migrating an OpenAI-style integration while accepting documented compatibility differences
• Projects that fit GroqCloud's current production model catalog

Skip Groq if

• A required model or OpenAI API parameter is unsupported
• Your production design depends on a preview model remaining available
• Your organization cannot operate within model-specific rate limits

Choose Together AI if

• Prototyping or variable traffic on a supported serverless model
• Steady workloads that justify reserved dedicated hardware
• Teams that want one API surface across serverless and dedicated deployment

Skip Together AI if

• You require a free trial before purchasing platform credits
• A required model is unavailable in the chosen serverless or dedicated catalog
• You cannot monitor dynamic rate limits or dedicated endpoint runtime cost

Groq: pros & cons

• Published model data: The catalog lists indicative token speed alongside price and limits.
• OpenAI client migration: Groq documents compatibility through an alternative base URL.
• Free and developer limits: Current quotas are documented by model and organization.
• Usage controls: Billing dashboards, spend limits and budget alerts are available.
• Catalog boundary: Applications can only call models and systems currently hosted by GroqCloud.
• Compatibility gaps: Some OpenAI request fields and output formats are not supported.
• Rate limits: Requests can hit per-minute, per-day, token or audio limits at the organization level.
• Preview risk: Preview models may be removed on short notice and are not documented for production use.

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.