Together AI operates high-performance GPU clusters optimized for AI inference and training. It offers dedicated GPU capacity for organizations needing guaranteed resources, as well as serverless inference that efficiently shares GPU resources across users for cost-effective model serving.
Together AI hosts and serves hundreds of open-source AI models on optimized infrastructure. Developers can deploy models through the shared inference API for cost-effective serving or provision dedicated endpoints for guaranteed capacity, with the platform handling all infrastructure management.
Together AI provides managed fine-tuning and training infrastructure for customizing open-source models. Users can fine-tune models using full fine-tuning, LoRA, or QLoRA methods through a simple API, with Together handling GPU provisioning, distributed training, and optimization.
Together AI provides API access to a wide catalog of open-source language models with OpenAI-compatible endpoints, competitive pricing, and fast inference speeds. Developers can access models like LLaMA, Mistral, and DeepSeek through a standardized API with support for streaming, function calling, and JSON mode.
Together AI specializes in hosting and serving open-source language models, providing fast and affordable API access to models from Meta, Mistral, DeepSeek, and other open-source providers. Its platform makes it easy to run, compare, and integrate open-source LLMs without managing GPU infrastructure.
Together AI has established itself as a leading platform for accessing open-source LLMs through a fast, developer-friendly API. Their inference engine delivers impressive speed, often outperforming competitors on throughput for popular models like Llama 3, Mixtral, and Qwen. The pay-per-token pricing is competitive and transparent, making it accessible for both prototyping and production workloads.
The platform excels at model hosting with an extensive catalog of open-source models available out of the box, plus support for custom fine-tuning and dedicated deployments. Their fine-tuning pipeline is straightforward, though advanced training customization options are somewhat limited compared to dedicated MLOps platforms. GPU cloud offerings are solid but less flexible than pure infrastructure providers like Lambda or CoreWeave.
Strengths include exceptional inference speed, OpenAI-compatible API endpoints for easy migration, and strong open-source model support. Limitations include less granular control over infrastructure, and costs can escalate at very high volumes compared to self-hosting. Overall, Together AI is an excellent choice for teams wanting fast, reliable access to the best open-source models without managing infrastructure.
You are Claude Opus 4.6, an AI technology reviewer for Diraitory.com - an AI tools directory that features curated AI tool listings with AI-generated reviews. Your task is to write a thoughtful review of the AI tool or platform provided. Guidelines: - Evaluate the tool's capabilities, ease of use, and value proposition - Consider pricing, API availability, and integration options - Compare implicitly to alternatives in the same space - Be balanced: mention both strengths and limitations - Provide a rating for EACH category the item belongs to (scale 1-5, can include .1 increments like 3.1, 4.8) - Consider the item's performance/fit within each specific category when giving ratings - Keep the review between 80-200 words - Write in a professional but accessible tone for tech users User Prompt: Please review the following: Name: Together AI Website: https://www.together.ai Categories: AI GPU Cloud, AI Model Hosting, AI Training Platforms, LLM APIs, Open Source LLMs Tool Info: - Pricing Model: Paid - Full Pricing: Pay-per-token API pricing (varies by model) - API Available: Yes
Together AI has established itself as a premier platform for accessing open-source LLMs through a unified API. Their inference speeds are consistently impressive, often outperforming competitors on popular models like Llama, Mixtral, and Qwen. The pay-per-token pricing is competitive and transparent, making it accessible for projects of varying scales.
The platform excels at model hosting with excellent uptime and low latency. Their fine-tuning capabilities are solid, though the training platform could benefit from more advanced customization options. GPU cloud access is available but feels secondary to their core API offering.
Strengths include an extensive model library, excellent documentation, and seamless API integration. The Together Embeddings and reranking models add significant value. Limitations include less flexibility for custom deployments compared to dedicated GPU providers, and enterprise features could be more robust.
For developers prioritizing fast, cost-effective access to open-source models, Together AI delivers exceptional value and remains a top choice in the inference API space.
You are Claude 4.5 Opus, an AI technology reviewer for Diraitory.com - an AI tools directory that features curated AI tool listings with AI-generated reviews. Your task is to write a thoughtful review of the AI tool or platform provided. Guidelines: - Evaluate the tool's capabilities, ease of use, and value proposition - Consider pricing, API availability, and integration options - Compare implicitly to alternatives in the same space - Be balanced: mention both strengths and limitations - Provide a rating for EACH category the item belongs to (scale 1-5, can include .1 increments like 3.1, 4.8) - Consider the item's performance/fit within each specific category when giving ratings - Keep the review between 80-200 words - Write in a professional but accessible tone for tech users User Prompt: Please review the following: Name: Together AI Website: https://www.together.ai Categories: AI GPU Cloud, AI Model Hosting, AI Training Platforms, LLM APIs, Open Source LLMs Tool Info: - Pricing Model: Paid - Full Pricing: Pay-per-token API pricing (varies by model) - API Available: Yes
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