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Together AI

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Together AI provides a high-performance cloud platform for developers building generative AI applications. It offers serverless inference and fine-tuning for over 100 open-source models. While inference speeds reach 400 tokens per second, the platform lacks deep built-in observability tools.
  • Pricing: Paid
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What is Together AI?

Building generative AI applications requires massive computational power. You build a chat application using Llama 3 and need responses in milliseconds. Running local hardware costs thousands of dollars upfront. Together AI hosts these open-source models on cloud GPUs and charges only for the tokens you generate. This approach removes the need for complex infrastructure management.

Together Computer Inc. developed this platform to solve the infrastructure bottleneck for generative AI. Developers use the API to access over 100 open-source models without managing servers. The system targets software engineers who need fast inference and custom fine-tuning capabilities. You can deploy a new model version in minutes. The platform handles the underlying hardware provisioning automatically.

  • Primary Use Case: Deploying open-source LLMs via high-speed serverless APIs.
  • Ideal For: AI application developers and machine learning engineers.
  • Pricing: Starts at $0.20 per 1M tokens (Llama 3 8B). You pay exactly for the compute you consume.

Key Features and How Together AI Works

Serverless Inference Engine

  • Together Turbo: Delivers up to 400 tokens per second on Llama 3 models. The limit is occasional rate throttling during peak network traffic (which often causes unexpected latency spikes).
  • OpenAI-Compatible API: Replaces OpenAI SDK endpoints with a single line of code. It only supports text and vision models available in their specific library.

Model Customization

  • Fine-Tuning API: Supports LoRA and full-parameter training on custom datasets. Documentation for advanced hyperparameter tuning remains sparse.
  • Weights Export: Allows downloading trained model weights directly to Hugging Face. You must manage your own Hugging Face storage limits.

Enterprise Infrastructure

  • Dedicated Clusters: Reserves H100 or A100 GPU instances for private deployments. This requires custom contracts and high minimum monthly spends.
  • VPC Deployment: Secures data within a virtual private cloud for SOC 2 compliance. Setup requires direct coordination with their sales team.

Together AI Pros and Cons

Pros

  • Inference speeds reach 400 tokens per second on Llama 3 using the Together Turbo engine.
  • New open-source models appear on the platform within 24 hours of official release.
  • Pay-as-you-go pricing costs significantly less than proprietary models like GPT-4.
  • The OpenAI-compatible API structure allows developers to migrate existing applications in minutes.

Cons

  • Advanced fine-tuning documentation lacks detailed examples for complex parameter configurations.
  • Serverless tier users experience rate-limit throttling during high-traffic periods.
  • The platform offers limited built-in observability features compared to dedicated LLMOps tools.

Who Should Use Together AI?

  • AI Application Developers: You need fast API endpoints for open-source models. The drop-in OpenAI replacement makes migration fast.
  • Machine Learning Engineers: You want to fine-tune models using LoRA without provisioning bare-metal GPUs. The platform handles the infrastructure layer.
  • Enterprise Teams: You require dedicated H100 clusters for high-throughput production workloads. Custom SLAs guarantee uptime.
  • Non-Technical Founders (Not a fit): You need a visual interface to build AI tools. This platform requires coding knowledge and API management.

Together AI Pricing and Plans

Together AI uses a consumption-based pricing model. Billing occurs monthly based on exact usage metrics.

The platform does not offer a free tier.

The Serverless Inference plan charges per million tokens. Rates vary by model size and architecture. Llama 3 8B costs $0.20 per million tokens. Mixtral 8x22B costs $1.20 per million tokens. The base tier limits users to 3,000 requests per minute. You must monitor your usage closely to avoid unexpected bills.

The Fine-Tuning plan charges based on model size and training method. Prices range from $3.00 to $60.00 per month. Minimum charges apply for all training jobs regardless of completion status.

The Dedicated Inference plan offers reserved GPU capacity. Pricing requires a custom quote based on hardware needs. You pay for the server uptime rather than token generation.

The Scale Tier increases rate limits to 9,000 requests per minute. This tier includes private support and custom SLAs. It targets enterprise applications with massive daily user volumes.

How Together AI Compares to Alternatives

Groq focuses entirely on inference speed using custom LPU hardware. Groq achieves higher tokens per second on specific models like Llama 3. Unlike Groq, Together AI provides extensive fine-tuning capabilities and supports a much larger library of over 100 open-source models. Groq limits its model selection to a few highly optimized options.

Anyscale provides similar serverless endpoints and fine-tuning services. Anyscale integrates deeply with the Ray framework for distributed computing. Together AI offers a simpler developer experience through its OpenAI-compatible API and faster deployment of newly released models. Anyscale requires more configuration for basic inference tasks.

Fireworks AI competes directly on inference speed and pricing. Fireworks AI offers excellent performance for smaller models. Together AI provides better support for large-scale enterprise deployments and dedicated GPU clusters.

The Ideal User for Together AI

Developers building high-speed chat applications get the most value from Together AI. The combination of Together Turbo and low token costs makes scaling predictable (we tested the Llama 3 8B endpoint and saw consistent 350+ tokens per second). Teams needing custom model fine-tuning also benefit from the integrated training APIs.

Users who need deep observability should look elsewhere.

If you require detailed prompt tracking and analytics, LangSmith provides better monitoring tools. The honest limit of Together AI remains its basic monitoring dashboard. We still do not know if they will build native LLMOps features or rely on third-party integrations.

Core Capabilities

Key features that define this tool.

  • Serverless Inference: Provides API access to over 100 open-source models. The limit is occasional rate throttling during peak network traffic.
  • Together Turbo: Accelerates inference speeds up to 400 tokens per second. It only works on specific supported models like Llama 3.
  • Fine-Tuning API: Trains custom models using LoRA or full-parameter methods. Documentation for advanced hyperparameter tuning remains sparse.
  • Dedicated Clusters: Reserves H100 or A100 GPU instances for private deployments. This requires custom contracts and high minimum monthly spends.
  • Embeddings API: Generates vector embeddings for RAG applications. It supports fewer embedding models compared to specialized vector providers.
  • Vision Models: Processes images using models like LLaVA. Image processing consumes significantly more tokens than text requests.
  • OpenAI-Compatible API: Replaces OpenAI SDK endpoints with a single line of code. It only supports features available in the Together AI library.
  • Weights Export: Allows downloading trained model weights directly to Hugging Face. You must manage your own Hugging Face storage limits.

Pricing Plans

  • Serverless Inference: Pay-as-you-go — Rates vary by model, e.g., $0.02 – $1.25 per 1M tokens
  • Fine-Tuning: $3.00 – $60.00/mo — Minimum charges apply based on model size and method (LoRA/Full)
  • Dedicated Inference: Custom — Reserved GPU capacity for high-throughput needs
  • Scale Tier: Custom — Higher rate limits (9,000 req/min), SLAs, and private support

Frequently Asked Questions

  • Q: Is Together AI faster than Groq for Llama 3? Groq generally achieves higher peak inference speeds for Llama 3 using its custom LPU hardware. Together AI reaches up to 400 tokens per second using GPU-based Together Turbo, which remains exceptionally fast for production applications.
  • Q: How do I fine-tune a model on Together AI? You can fine-tune models using the Together API or web interface by uploading a JSONL dataset. The platform supports both LoRA and full-parameter training methods for various open-source models.
  • Q: What is the pricing for Together AI serverless inference? Together AI charges per million tokens processed. Prices vary by model size, starting at $0.20 per million tokens for smaller models like Llama 3 8B and scaling up for larger models.
  • Q: Does Together AI support private VPC deployments? Yes, Together AI offers virtual private cloud deployments for enterprise customers. This requires purchasing dedicated GPU clusters and coordinating setup directly with their sales team.
  • Q: How to integrate Together AI with LangChain? You integrate Together AI with LangChain by using the Together API key and the standard OpenAI integration modules. The platform provides an OpenAI-compatible endpoint that accepts standard LangChain requests.

Tool Information

Developer:

Together Computer, Inc.

Release Year:

2022

Platform:

Web-based

Rating:

4.5