Groq vs Together AI(2026)
Groq is better for teams that need fastest inference available. Together AI is the stronger choice if access to all major open models. Groq is freemium (from $0.05/1M tokens) and Together AI is paid (from $0.20/1M tokens).
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
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Groq
Groq provides ultra-fast LLM inference using LPU hardware, with APIs for Llama, Mistral, and other open models.
Starting at $0.05/1M tokens
Visit GroqTogether AI
Together AI provides fast inference for 50+ open-source models including Llama, Mistral, and CodeLlama.
Starting at $0.20/1M tokens
Visit Together AIHow Do Groq and Together AI Compare on Features?
| Feature | Groq | Together AI |
|---|---|---|
| Pricing model | freemium | paid |
| Starting price | $0.05/1M tokens | $0.20/1M tokens |
| Ultra-fast inference (500+ tokens/s) | ✓ | — |
| Llama 3 | ✓ | — |
| Mistral | ✓ | — |
| Whisper | ✓ | — |
| Function calling | ✓ | — |
| OpenAI-compatible API | ✓ | ✓ |
| 50+ open models | — | ✓ |
| Custom fine-tuning | — | ✓ |
| Fast inference | — | ✓ |
| Dedicated endpoints | — | ✓ |
| Embeddings | — | ✓ |
Groq Pros and Cons vs Together AI
Groq
Together AI
Deep dive: Groq
When to choose Groq
Groq makes sense when inference latency is the primary constraint and the application can work within the boundaries of open-source models. Real-time applications like conversational agents, live coding assistants, and interactive search experiences benefit most from Groq's sub-200ms time-to-first-token and 500+ tokens per second throughput — speeds that make responses feel instantaneous rather than streamed. Choose Groq over OpenAI when the task does not require GPT-4 class reasoning and the speed difference between 50 tokens per second and 500+ tokens per second materially affects user experience. Choose Groq over self-hosted inference when the team lacks GPU infrastructure expertise or when consistent low-latency at scale matters more than per-token cost optimization. Groq is the right fit for teams building chat interfaces where typing indicators feel sluggish, for applications that chain multiple LLM calls sequentially where cumulative latency compounds, and for batch processing where 10x throughput means 10x less wall-clock time. It is a poor fit for tasks requiring proprietary model capabilities like GPT-4o's vision, Claude's extended reasoning, or fine-tuned models. The model selection is limited to popular open-source families — Llama 3, Mistral, Mixtral, and Gemma — so teams needing specialized models or custom fine-tunes must look elsewhere. It is also a weaker choice for cost-sensitive batch workloads where latency does not matter, since providers like Together AI offer lower per-token pricing for throughput-optimized inference without LPU hardware.
Real-world use case
A developer tools company building an AI-powered code review bot integrates Groq for inline suggestions. When a developer pushes a commit, the bot analyzes each changed file and returns line-by-line feedback. Using Groq's Llama 3 70B endpoint, the bot processes a typical 500-line diff in under 2 seconds end-to-end, compared to 8-12 seconds with GPT-4o. This speed difference matters because the feedback appears as a GitHub comment before the developer navigates away from the PR page. The team uses Groq for the initial analysis pass and falls back to Claude for complex architectural suggestions where reasoning quality outweighs speed. The tradeoff is model capability: Llama 3 70B occasionally misses subtle bugs that GPT-4o catches, particularly around type system edge cases and concurrency issues. The team accepts this because 90% of review comments are style, documentation, and obvious logic errors where Llama 3 performs comparably. At 50,000 reviews per month, Groq costs approximately $150 versus $2,000 for equivalent GPT-4o usage — a 13x cost reduction alongside the speed improvement. The rate limit on the free tier (30 requests per minute) was sufficient during development but required upgrading to a paid plan within the first week of production deployment.
Hidden gotchas
The rate limits on Groq's free tier are per-model, not per-account, and change without notice in the documentation. As of mid-2026, Llama 3 70B is limited to 30 requests per minute and 14,400 requests per day on the free tier. These limits are adequate for development but break immediately in any production scenario with more than one concurrent user. The paid tier lifts these limits but pricing is usage-based with no published rate limit guarantees — during peak demand periods, Groq may throttle requests even on paid plans, returning 429 status codes with variable retry-after headers. The OpenAI-compatible API is compatible enough for basic chat completions but diverges on edge cases: streaming with function calling behaves differently, the logprobs parameter is not supported on all models, and system message handling for some Mixtral variants produces different results than the same prompt on other providers. Context window limits are model-dependent and generally smaller than what the same model offers on other providers — Groq may serve Llama 3 70B with a 8K context window while Together AI serves the same model at 32K. This is a hardware constraint of the LPU architecture's memory layout. Groq does not offer fine-tuning, embeddings, or image generation — it is inference-only for text models. Teams that start on Groq for speed and later need these features must integrate a second provider anyway. The Whisper endpoint for audio transcription is available but runs at a fixed quality setting with no ability to tune language detection or timestamp granularity.
Pricing breakdown
Groq's free tier includes 30 requests per minute for Llama 3 70B and higher limits for smaller models like Llama 3 8B (30 RPM). Paid pricing starts at $0.05 per million input tokens and $0.08 per million output tokens for Llama 3 8B, scaling to $0.59 input and $0.79 output per million tokens for Llama 3 70B. Mixtral 8x7B sits at $0.24 input and $0.24 output per million tokens. These prices are 3-5x cheaper than OpenAI's GPT-4o ($2.50 input, $10.00 output per million tokens) for tasks where open-source model quality is acceptable. A typical SaaS processing 1 million chat messages per month at an average of 500 tokens per message would spend approximately $400-600 on Groq versus $3,000-5,000 on GPT-4o. There is no minimum commitment, no reserved capacity pricing, and no annual contract option as of mid-2026.
Should You Use Groq or Together AI?
For most teams, Groq is the better default: it offers fastest inference available and is freemium (from $0.05/1M tokens). Choose Together AI instead if access to all major open models matters more than limited model selection. There is no universal winner — the right pick depends on your budget, team size, and whether you value fastest inference available or access to all major open models more.
Choose Groq if…
- •Fastest inference available
- •Very cheap
- •OpenAI-compatible
Choose Together AI if…
- •Access to all major open models
- •Competitive pricing
- •Fine-tuning available