7 Best Alternatives to Together AI for LLM Serving & Tuning

Why look beyond Together AI

Together AI provides a specialized platform focused on serving and fine-tuning open-source large language models (LLMs), offering competitive pricing for inference and GPU access for training. Its primary value proposition lies in enabling developers and researchers to deploy and customize a wide range of open models efficiently with a pay-as-you-go model. The platform supports high-throughput inference for models like Llama 2, Mistral, and Stable Diffusion, alongside services for fine-tuning custom datasets.

However, organizations may seek alternatives due to specific requirements not fully addressed by Together AI's current offerings. Some may require access to proprietary, frontier models (e.g., GPT-4, Claude 3) that are not available on an open-source-focused platform. Others might need deeper integration with existing cloud ecosystems like Azure or AWS, or a fully managed service that handles more operational aspects of model deployment and scaling. Additionally, enterprises with stringent data governance, specialized compliance needs beyond SOC 2 Type II, or a preference for a single vendor across their AI and cloud infrastructure might find more comprehensive solutions elsewhere. Developers also consider platforms that offer broader AI capabilities beyond LLMs, such as advanced computer vision, speech recognition, or traditional machine learning services, which Together AI does not emphasize.

Top alternatives ranked

1. 1. OpenAI API — Access to proprietary, frontier AI models

   OpenAI API provides programmatic access to a suite of proprietary large language models, including the GPT series (e.g., GPT-3.5 Turbo, GPT-4) and embedding models. It also offers DALL-E for image generation and Whisper for speech-to-text transcription. Unlike Together AI, which focuses on open-source models, OpenAI specializes in its own state-of-the-art models, often setting benchmarks for performance and capabilities in various natural language processing tasks. The platform supports a wide range of applications from content generation and summarization to code assistance and conversational AI. Developers can interact with the API via Python and Node.js SDKs, with comprehensive documentation and a playground for experimentation. Pricing is token-based, varying by model and context window size. OpenAI also offers enterprise-grade solutions for organizations requiring enhanced security, dedicated capacity, and custom model training.

   Best for: Accessing proprietary, highly capable LLMs, general-purpose natural language tasks, image generation, and speech-to-text conversion.

   OpenAI API Profile | OpenAI API

2. 2. Azure OpenAI Service — Enterprise-grade OpenAI models within Azure

   Azure OpenAI Service provides access to OpenAI's models, including GPT-4, GPT-3.5 Turbo, and DALL-E, within the secure and scalable Azure cloud environment. This offering is distinct from the direct OpenAI API primarily due to its integration with Azure's enterprise capabilities, such as virtual networks, private endpoints, and Azure Active Directory for identity management. It allows organizations to deploy and manage OpenAI models with enhanced security, compliance, and governance features required by large enterprises. Customers can also fine-tune models with their own data, leveraging Azure's machine learning infrastructure. The service supports various programming languages through REST APIs and SDKs (Python, Go, Java, JavaScript, C#), making it suitable for building secure, production-ready AI applications within an existing Azure ecosystem. Pricing is based on tokens and dedicated capacity, aligned with Azure's consumption model.

   Best for: Enterprises requiring OpenAI models with Azure's security, compliance, and existing cloud infrastructure integration.

   Azure OpenAI Service Profile | Azure OpenAI Service

3. 3. Anthropic — Focus on AI safety and long context windows

   Anthropic develops and deploys advanced AI models, with a strong emphasis on AI safety and responsible development. Its flagship model family, Claude 3, includes models like Haiku, Sonnet, and Opus, known for their strong reasoning capabilities, performance in complex tasks, and notably long context windows. This allows Claude models to process and generate responses based on extensive documents or conversations, which can be advantageous for applications requiring deep contextual understanding. Anthropic provides an API for developers to integrate Claude into their applications, offering Python and TypeScript SDKs. While Together AI focuses on open-source model serving, Anthropic offers proprietary models designed with constitutional AI principles, aiming for helpful, harmless, and honest outputs. Pricing is typically token-based, with different rates for input and output tokens.

   Best for: Applications requiring advanced reasoning, long context window processing, and a strong focus on AI safety and responsible AI development.

   Anthropic Profile | Anthropic

4. 4. Google AI — Broad AI services and research capabilities

   Google AI encompasses a wide array of AI tools, platforms, and research initiatives, from foundational models like Gemini to specialized services on Google Cloud. Unlike Together AI's focus on open-source LLM serving, Google AI offers a comprehensive ecosystem for building and deploying AI applications, including generative AI studio capabilities, custom model training, and access to a diverse portfolio of pre-trained models for vision, speech, and language. Developers can leverage Google's AI Platform, Vertex AI, and various APIs and SDKs (Python, Node.js, Go, Java, Ruby, C#) to integrate AI into their products. Google's strengths lie in its extensive research, broad model availability (both proprietary and open-source options), and deep integration within the Google Cloud ecosystem, providing scalable infrastructure for AI workloads. Pricing models vary depending on the specific service, often based on usage (e.g., tokens, compute hours) and managed service fees.

   Best for: Organizations seeking a broad range of AI services, access to Google's foundational models, and deep integration with Google Cloud infrastructure.

   Google AI Profile | Google AI

5. 5. Anyscale — Ray-based platform for distributed AI and LLM serving

   Anyscale provides a managed platform for building, deploying, and managing AI applications at scale, leveraging the open-source Ray framework. While Together AI focuses on specific LLM serving and fine-tuning, Anyscale offers a more general-purpose distributed computing platform that supports a wider range of AI workloads, including LLM inference and fine-tuning. It enables users to run complex AI workflows, from data preprocessing and model training to serving, across distributed clusters. Anyscale's platform is designed for scalability and performance, making it suitable for demanding AI applications that require significant computational resources. It supports both open-source and proprietary models and offers capabilities for managing the entire machine learning lifecycle. Pricing is typically usage-based, often involving compute hours and data transfer, with enterprise-grade support and security features.

   Best for: Developing and deploying large-scale, distributed AI applications, including LLM serving and fine-tuning, using the Ray ecosystem.

   Anyscale Profile | Anyscale

6. 6. RunPod — On-demand GPU cloud for AI inference and training

   RunPod offers a GPU cloud platform that provides on-demand access to GPUs for AI inference, training, and development. Similar to Together AI's GPU Cloud offering, RunPod allows users to rent powerful GPUs at competitive prices, providing flexibility for various AI workloads. However, RunPod emphasizes raw GPU access and customizability, enabling users to deploy any containerized application or model. This gives developers more control over their environment and software stack compared to a more opinionated platform. It supports a wide range of open-source models and frameworks, making it a flexible option for researchers and developers who need bare-metal GPU access without the overhead of managing physical hardware. Pricing is typically hourly or by usage, depending on the GPU type and region, with options for dedicated or spot instances.

   Best for: Developers and researchers needing flexible, on-demand GPU access for custom AI training, inference, and development with full environment control.

   RunPod Profile | RunPod

7. 7. Replicate — Deploy and run open-source models via API

   Replicate simplifies the process of deploying and running machine learning models, particularly open-source ones, via an API. It focuses on making it easy for developers to integrate pre-trained models into their applications without managing infrastructure. While Together AI offers a broader platform for serving and fine-tuning, Replicate excels in quick deployment and consumption of a vast library of public models, including many popular LLMs and image generation models. It provides a straightforward API, often with ready-to-use Docker images, allowing developers to spin up models and get predictions with minimal setup. This platform is particularly useful for prototyping, small-to-medium scale deployments, and accessing specific models without heavy configuration. Pricing is usage-based, typically per prediction or per second of GPU time, making it cost-effective for intermittent or fluctuating workloads.

   Best for: Quickly deploying and running open-source machine learning models via an API for prototyping and integrating specific AI functionalities.

   Replicate Profile | Replicate

Side-by-side

How to pick

Selecting an alternative to Together AI depends on your specific AI project requirements, budget, and operational preferences. Consider the following decision framework:

• If you primarily need access to proprietary, cutting-edge foundation models:
  • Choose OpenAI API for models like GPT-4, DALL-E, and Whisper, suitable for general-purpose NLP, image generation, and speech tasks.
  • Consider Anthropic if your application demands advanced reasoning, exceptionally long context windows, and a strong emphasis on AI safety and responsible development (e.g., Claude 3 models).
• If your organization operates within a specific cloud ecosystem and requires enterprise-grade security and compliance:
  • Opt for Azure OpenAI Service if you are an Azure customer needing OpenAI models with integrated Azure security, networking, and governance features.
  • Explore Google AI if you are integrated with Google Cloud and require a broad suite of AI services, including foundational models like Gemini, alongside other ML capabilities.
• If you require high levels of control over your GPU infrastructure and specific software environments for custom AI training/inference:
  • RunPod is a strong contender, offering on-demand GPU access with full customization for your containers and development stack, ideal for researchers and those with unique workload requirements.
  • While Together AI offers GPU Cloud, RunPod provides more granular control over the underlying compute environment.
• If you are building complex, distributed AI applications beyond just LLM serving, and leverage the Ray ecosystem:
  • Anyscale provides a managed platform for scaling distributed AI workloads, including LLM fine-tuning and serving, making it suitable for intricate ML pipelines.
• If your main goal is to quickly deploy and use a wide variety of open-source models via a simple API for specific tasks:
  • Replicate excels at making it easy to integrate pre-trained open-source models into applications with minimal setup, suitable for prototyping or specific feature integration.
• If cost-efficiency for open-source LLM inference and fine-tuning is your absolute top priority:
  • Together AI remains a competitive option due to its specialized focus and pricing structure for open-source models. However, compare its pricing directly with the GPU costs on platforms like RunPod or the token costs of open-source models served via Anyscale or Replicate for your specific usage patterns.