Together AI

What is Together.ai?

Positioning: Together.ai is an AI cloud platform focused on developing, fine-tuning, and deploying open-source large language models (LLMs) and generative AI models. It provides the infrastructure and tools for developers and enterprises to build AI applications efficiently and cost-effectively, emphasizing high-performance inference and training capabilities.

Functional Panorama: The platform covers several core modules, including: Managed Inference (for serving LLMs with low latency), Fine-tuning (to adapt models to specific datasets), Model Library (hosting a wide range of open-source models like Llama, Mistral, Qwen), and a Developer API (for seamless integration). The underlying infrastructure supports distributed training and inference, leveraging advanced GPU clusters to optimize performance.

Together.ai’s Use Cases

• AI Developers can use the platform’s API and model library to quickly integrate state-of-the-art LLMs into their applications without managing complex infrastructure.
• Researchers and Data Scientists can leverage Together.ai’s fine-tuning capabilities to customize existing open-source models with proprietary datasets for specialized tasks like domain-specific content generation or advanced natural language understanding.
• Startups and Enterprises can deploy high-throughput, low-latency AI inference endpoints for user-facing applications, achieving cost efficiencies compared to proprietary model APIs.
• Content Creators can utilize generative AI models for tasks like creative writing, code generation, summarization, and translation by accessing various models via the Together.ai API.

Together.ai’s Key Features

• – Supports batch inference and parallel processing for various open-source LLMs, often boasting inference speeds several times faster than competitors due to optimized kernel implementations.
• – The “Together Model Zoo” expanded significantly in April 2024, adding support for the latest Llama 3 models (8B, 70B, and fine-tuned variants) and offering competitive performance benchmarks.
• – Integrated advanced fine-tuning methods like LoRA and QLoRA, with an updated interface rolled out in May 2024, enabling more efficient and resource-friendly model adaptation.
• – Introduced the “Together Embeddings” API in March 2024, providing a unified endpoint for various leading open-source embedding models, simplifying retrieval-augmented generation (RAG) workflows.
• – Users frequently recommend the platform for its competitive pricing per-token and per-GPU-hour, enabling more cost-effective experimentation and deployment for open-source model use cases compared to some major cloud providers.

How to Use Together.ai?

1. Sign Up and Get API Key: Register on the Together.ai website and generate an API key from your dashboard. This key authenticates your requests to the platform.
2. Choose a Model: Browse the “Together Model Zoo” for a suitable open-source model. Select based on your specific task.
3. Make API Calls for Inference: Use the provided API documentation to send inference requests.

   import togethertogether.api_key = "YOUR_API_KEY"response = together.chat.completions.create(model="mistralai/Mixtral-8x7B-Instruct-v0.1",messages=[{"role": "user", "content": "Tell me a story."}])print(response.choices[0].message.content)

4. Pro Tip: Optimize for Cost and Speed: For high-volume inference, consider utilizing the streaming API for faster initial token generation. Additionally, fine-tune smaller, performant models rather than solely relying on the largest available, as this can significantly reduce inference costs and latency. Many developers recommend pre-filtering or chunking large inputs to stay within token limits and improve response quality.
5. For Fine-tuning: Prepare your dataset in the required JSONL format and use the Together.ai CLI or API to upload it and initiate a fine-tuning job. Monitor job status via the dashboard.

Together.ai’s Pricing & Access

• – Official Policy: Together.ai primarily operates on a pay-as-you-go model. Pricing for inference is based on the number of input and output tokens, varying by model. Fine-tuning and dedicated instance pricing are based on GPU-hours.
• – Free Tier/Credits: New users typically receive initial free credits upon signing up, allowing them to test the platform and experiment with models without immediate cost. This offer was widely available as of Q2 2024.
• – Web Dynamics: While no public limited-time discounts were heavily advertised in the last 6 months, industry analysts note Together.ai’s pricing remains highly competitive, especially for open-source model serving, often being significantly cheaper than similar services from larger cloud providers for comparable performance. This makes it attractive for startups scaling AI applications.
• – Tier Differences: The core “On-Demand” tier provides access to all models and features on a pay-per-use basis. An “Enterprise” tier is available, offering dedicated instances, custom model deployments, priority support, and potentially volume discounts for large-scale operations, catering to specific compliance and performance needs.

Together.ai’s Comprehensive Advantages

• – Competitor Contrasts: Together.ai’s optimized inference stack allows it to serve popular open-source models with up to 2-3x lower latency and often at a fraction of the cost compared to generic GPU cloud providers or even specialized AI platforms that don’t focus exclusively on open-source LLMs.
• – Performance for Open-Source: Unlike platforms focusing on proprietary models, Together.ai excels in providing production-ready infrastructure for open-source LLMs, often being among the first to offer highly optimized serving for newly released models.
• – Market Recognition: Industry publications like The Information and Forbes have highlighted Together.ai as a key player in democratizing access to powerful AI models, particularly for developers who prefer the flexibility and transparency of open-source solutions. User satisfaction ratings for ease of use and API robustness are generally high across developer communities.
• – Developer-Centric Ecosystem: The platform emphasizes developer experience with comprehensive documentation, active community engagement, and a focus on providing simple, yet powerful APIs, reducing the barrier to entry for building complex AI applications.