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AI Model Hosting & Inference🔴Developer🏆Editor's Choice
T

Together AI

AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.

Starting at$0.02/1M tokens
Visit Together AI →
💡

In Plain English

AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.

OverviewFeaturesPricingGetting StartedUse CasesIntegrationsLimitationsFAQSecurityAlternatives

Overview

Together AI is one of the largest independent inference providers focused on open-weight models. Its catalog spans 200+ models — Llama 3 and 4, Mixtral, Qwen, DeepSeek, Mistral, Gemma, FLUX image models, plus embedding and rerank models — all served behind an OpenAI-compatible API with serverless pay-per-token pricing. Beyond serverless, Together sells two adjacent products that distinguish it from pure inference clouds: dedicated endpoints (you pin a model to a private GPU pool with predictable throughput and no rate limits) and GPU Clusters (reserved H100, H200, B200, and GB200 instances with InfiniBand interconnect, sold as the Together Instant Cluster product for training, fine-tuning, and large-scale batch inference). Together's fine-tuning service supports LoRA and full-parameter tuning on most catalog models, with deployment back to a serverless or dedicated endpoint in one step.

🦞

Using with OpenClaw

▼

Configure Together AI as LLM provider in OpenClaw for cost-effective open-source model access. Use API key authentication and OpenAI-compatible interface.

Use Case Example:

Reduce LLM costs dramatically while maintaining agent capabilities by using optimized open-source models through Together AI's performance-optimized infrastructure.

Learn about OpenClaw →
🎨

Vibe Coding Friendly?

▼
Difficulty:beginner
No-Code Friendly ✨

OpenAI-compatible API makes integration straightforward - just change the base URL and model name in existing code.

Learn about Vibe Coding →

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Editorial Review

Together AI is highly regarded for democratizing access to powerful open-source models through production-ready infrastructure. Users consistently praise the dramatic cost savings (5-20x less than GPT-4) while maintaining quality, plus the superior performance optimizations that make open-source models competitive with proprietary alternatives. The OpenAI-compatible API makes migration seamless. Some users note occasional capacity constraints and the inherent complexity of choosing optimal models for specific use cases.

Key Features

Serverless inference APIs for open and proprietary model workloads+
Batch Inference API for large asynchronous token processing jobs+
Fine-tuning platform for shaping open models with private or domain data+
Dedicated Model Inference and Dedicated Container Inference options+
GPU Clusters, managed storage, evaluations, cookbooks, demos, and developer docs+

Pricing Plans

Serverless inference

Per-million-token pricing per model (open models from sub-$0.20/M input typical)

    Dedicated endpoints

    Per-hour GPU pricing for pinned model deployments

      GPU Clusters / Instant Clusters

      Reserved H100/H200/B200/GB200 capacity, hourly and contracted

        Enterprise

        Custom

          See Full Pricing →Free vs Paid →Is it worth it? →

          Ready to get started with Together AI?

          View Pricing Options →

          Getting Started with Together AI

          1. 1Sign up for Together AI account and obtain API key from the dashboard for immediate access to the platform
          2. 2Replace OpenAI base URL with api.together.xyz in existing code while keeping the same OpenAI SDK and request format
          3. 3Select optimal open-source model for your use case and test performance comparing different model sizes and capabilities
          4. 4Implement fine-tuning if needed for specialized tasks or quality improvements using the platform's managed training infrastructure
          Ready to start? Try Together AI →

          Best Use Cases

          🎯

          Production inference on open-weight models with one consistent API

          ⚡

          Fine-tuning a Llama, Qwen, or Mixtral variant and deploying it in the same account

          🔧

          Reserved GPU capacity for training without negotiating a hyperscaler contract

          🚀

          Multi-model agentic stacks that switch between text, embedding, rerank, and image models

          Integration Ecosystem

          16 integrations

          Together AI works with these platforms and services:

          🧠 LLM Providers
          openai-sdklangchainllamaindexhuggingfacelocal
          📊 Vector Databases
          PineconeWeaviateChromaQdrant
          ☁️ Cloud Platforms
          AWSGCPAzure
          📈 Monitoring
          LangSmithLangfuseHeliconeweights-biases
          View full Integration Matrix →

          Limitations & What It Can't Do

          We believe in transparent reviews. Here's what Together AI doesn't handle well:

          • ⚠Exact 2026 per-model rates were not reliably visible in curl and must be verified before procurement
          • ⚠Not a no-code assistant; teams need developers who understand model selection, rate limits, evals, and observability
          • ⚠GPU and token costs can rise quickly if prompts, batch jobs, or retries are not monitored
          • ⚠Open-model flexibility also means buyers must test quality instead of assuming one default model is best
          • ⚠Enterprise security, data retention, and committed-use discounts require direct vendor confirmation

          Pros & Cons

          ✓ Pros

          • ✓Breadth of open-weight model catalog (200+) with one OpenAI-compatible API
          • ✓One account spans serverless, dedicated endpoints, fine-tuning, and reserved GPU capacity
          • ✓Transparent per-token pricing — easy to model unit economics against closed providers
          • ✓InfiniBand-backed GPU Clusters are credible for real training, not just inference

          ✗ Cons

          • ✗Frontier-class reasoning still lags closed models on the hardest benchmarks
          • ✗Fastest single-model latency is sometimes beaten by Groq or Cerebras
          • ✗Many model variants means model selection itself becomes a project
          • ✗Dedicated endpoint cost calculations require attention to GPU type and utilization

          Frequently Asked Questions

          How does Together AI compare to using OpenAI's API directly?+

          Together AI provides access to open-source models (Llama, Mistral, DeepSeek) through an OpenAI-compatible API. Key advantages include 5-20x lower costs per token, faster inference speeds through custom optimizations, and access to specialized models. The tradeoff is that even the best open-source models may lag behind GPT-4 on complex reasoning tasks, though the gap is rapidly narrowing with models like Llama 3.3 and DeepSeek-V3.

          Does Together AI support function calling for AI agents?+

          Yes, Together AI implements OpenAI-compatible function calling across supported models including Llama, Mistral, and other major families. The implementation uses the same tools/function_call API format, so existing agent code using OpenAI SDK works with minimal changes. Function calling quality varies by model size - larger models (70B+) generally produce more reliable tool calls than smaller ones.

          Can I fine-tune models on Together AI for my specific use case?+

          Yes, Together AI provides comprehensive fine-tuning capabilities for customizing open-source models on your data. You can fine-tune Llama, Mistral, and other supported base models using instruction tuning, domain adaptation, or full fine-tuning. The platform supports advanced techniques like LoRA and QLoRA for efficient training. Fine-tuned models are automatically deployed for inference through the same API with usage-based pricing.

          What are dedicated endpoints and when should I use them?+

          Dedicated endpoints provide reserved GPU capacity with guaranteed performance and sub-100ms latency SLAs. They're ideal for production applications requiring consistent performance, high-volume workloads, or custom model hosting. Unlike serverless inference which shares resources, dedicated endpoints give you isolated infrastructure. Pricing is based on hourly GPU reservations rather than per-token usage.

          How reliable is Together AI for production workloads?+

          Together AI offers 99.9% uptime SLA on dedicated endpoints and maintains high availability on serverless infrastructure. The platform is SOC 2 Type II certified with enterprise security features. For mission-critical applications, dedicated endpoints provide the most reliable option with guaranteed capacity and consistent performance. Enterprise plans include priority support and custom SLAs.

          🔒 Security & Compliance

          🛡️ SOC2 Compliant
          ✅
          SOC2
          Yes
          ✅
          GDPR
          Yes
          —
          HIPAA
          Unknown
          —
          SSO
          Unknown
          ❌
          Self-Hosted
          No
          ❌
          On-Prem
          No
          —
          RBAC
          Unknown
          —
          Audit Log
          Unknown
          ✅
          API Key Auth
          Yes
          ❌
          Open Source
          No
          ✅
          Encryption at Rest
          Yes
          ✅
          Encryption in Transit
          Yes
          Data Retention: configurable
          Data Residency: US
          📋 Privacy Policy →🛡️ Security Page →
          🦞

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          What's New in 2026

          •Launched ATLAS acceleration system delivering up to 4x faster inference with runtime learning optimizations
          •Added DeepSeek-V3.1, Llama 3.3 70B, and GLM-5 with cutting-edge reasoning capabilities
          •Introduced dedicated endpoints with sub-100ms latency SLAs and enterprise-grade isolation
          •Released GPU Cloud with Together Kernel Collection optimization for 90% faster pre-training

          Alternatives to Together AI

          Fireworks AI

          AI Model Hosting & Inference

          Production inference platform for open-weight LLMs, multimodal models, and custom fine-tunes — known for very fast serving (FireAttention/FireOptimizer), reliable function calling, and JSON mode at low per-token prices.

          Groq

          AI Model Hosting & Inference

          AI inference cloud built on Groq's own LPU (Language Processing Unit) chips that serves open-weight LLMs, Whisper, and vision models at the lowest latency in the market, with an OpenAI-compatible API.

          Replicate

          AI Model Hosting & Inference

          Run, fine-tune, and deploy thousands of community AI models with a single HTTP API — covering image, video, audio, language, and embedding models, billed per-second of GPU time.

          Anyscale

          AI Infrastructure

          Anyscale is the managed Ray platform from the original creators of Ray, providing production-scale infrastructure for distributed AI workloads — model training, batch inference, RAG pipelines, agent orchestration, and reinforcement learning — running on any cloud with autoscaling GPU and CPU clusters.

          View All Alternatives & Detailed Comparison →

          User Reviews

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          Quick Info

          Category

          AI Model Hosting & Inference

          Website

          www.together.ai/
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          More about Together AI

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