AI Tool Comparison
Comparing as AI LLM APIs (Foundation Models)Together AI vs Groq
Together AI
VS
Groq
Verdict by Category
Detailed Comparison
Feature
Together AI
Groq
Pricing
PaidTogether AI uses pay-as-you-go pricing across its products. Serverless inference is billed per model, priced per 1M tokens for text (e.g., MiniMax M3 at $0.30 input/$1.20 output, GLM-5.2 at $1.40 input/$4.40 output, gpt-oss-120B at $0.15 input/$0.60 output), per image for image generation (e.g., FLUX.1 [schnell] at $0.0027/image), per video for video models (e.g., ByteDance Seedance 2.5 at $0.115/video, Google Veo 3.0 at $1.60/video), and per audio minute or character for speech models. Dedicated Inference runs on single-tenant GPUs starting at $5.49/GPU/hour on-demand for NVIDIA HGX H100 and $8.99/hour for HGX B200, with reserved options available via sales. GPU Clusters offer on-demand rates from $3.99/hour (H100) to $8.19/hour (B200), with reserved pricing dropping as low as $3.19/hour for 181+ day H100 commitments. Sandbox compute costs $0.0446/vCPU/hour and $0.0149/GiB RAM/hour, with Code Interpreter sessions at $0.03 per 60-minute session. Fine-tuning is priced per 1M tokens processed, ranging from $0.48 (LoRA, up to 16B parameters) to $8.00 (full fine-tuning, 70-100B parameters) for standard models, with specialized model pricing (e.g., DeepSeek-R1, GLM-5) ranging $5-$40 per 1M tokens plus a minimum job charge. Managed Storage costs $0.16/GiB/month.
FreemiumGroqCloud uses pay-as-you-go pricing per million tokens with no seat license or minimum spend. Rates range from roughly $0.05 input / $0.08 output for Llama 3.1 8B Instant up to about $1.00 input / $3.00 output for Kimi K2, with the flagship Llama 3.3 70B Versatile priced at $0.59 input / $0.79 output and GPT-OSS 120B at $0.15 input / $0.60 output. Whisper v3 Turbo transcription is priced at $0.04 per hour of audio. A free tier is available to all registered users with no credit card required, offering access to every model at 30 requests per minute. The Batch API and prompt caching each cut rates by roughly 50%, and can be combined for an effective rate of about 25% of on-demand pricing on eligible workloads. Enterprise pricing, including GroqAssured governance features and dedicated GroqMetal infrastructure, is available by contacting Groq's sales team.
Categories
AI Developer APIs & PlatformsLarge Language Models (LLMs)
AI Developer APIs & PlatformsLarge Language Models (LLMs)
Summary
Full-stack AI cloud for inference, fine-tuning, and GPU clusters
The fastest inference cloud for open-source LLMs, powered by custom LPU chips
Together AI Pros & Cons
Pros
- OpenAI-compatible API makes migrating from closed-model providers straightforward
- Transparent per-model, pay-as-you-go pricing across 200+ open-source models
- Vertically integrated GPU cloud offers competitive on-demand and reserved rates
- Backed by deep systems research, including FlashAttention and other efficiency breakthroughs
- Full-stack coverage from inference to fine-tuning to raw GPU compute in one platform
- Proven at scale with customers like Cursor, Zoom, Quora, and ElevenLabs
Cons
- Pricing spans many separate model and product pages, making total cost estimation more complex than flat-rate competitors
- Dedicated GPU and reserved cluster pricing largely requires contacting sales rather than transparent self-serve rates
- Focus on open-source models means access to closed frontier models like GPT or Claude isn't the platform's core strength
- Fine-tuning costs vary significantly by model size and technique, requiring careful comparison before committing
- Provisioned throughput and PTU-based pricing has a learning curve for teams new to capacity-based billing
Groq Pros & Cons
Pros
- Consistently ranks among the fastest LLM inference providers thanks to purpose-built LPU hardware
- OpenAI-compatible API makes migration from existing integrations fast
- Generous free tier with no credit card required and access to every hosted model
- Batch API and prompt caching can stack to roughly 25% of on-demand pricing
- Proven at scale with 3M+ developers and demanding real-time customers like McLaren F1
Cons
- Only hosts open-source models (Llama, Mixtral, Gemma, Qwen, DeepSeek distills), so there's no access to proprietary models like GPT or Claude through the platform
- The December 2025 NVIDIA licensing deal and departure of founder Jonathan Ross as CEO introduce some uncertainty about the platform's long-term technical direction
- No self-serve fine-tuning; customization requires contacting Groq's sales team or submitting an Enterprise request
- Free tier is limited by requests-per-minute (30 RPM) rather than a generous token allowance, which can bottleneck bursty workloads
- Full pricing isn't published for every capability, and Enterprise/GroqAssured governance features require a custom conversation