Fireworks AI vs Together AI

Detailed side-by-side comparison to help you choose the right tool

Fireworks AI

🔴Developer

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.

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Starting Price

Custom

Together AI

🔴Developer

AI Model Hosting & Inference

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.

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Starting Price

$0.02/1M tokens

Feature Comparison

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FeatureFireworks AITogether AI
CategoryAI Model Hosting & InferenceAI Model Hosting & Inference
Pricing Plans8 tiers142 tiers
Starting Price$0.02/1M tokens
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

    Fireworks AI - Pros & Cons

    Pros

    • Reliable function calling, JSON mode, and parallel tool calls across the open-model catalog — table stakes for production agents
    • FireFunction-V2 is purpose-built for tool-calling accuracy, materially beating generic Llama tool-use in agentic loops
    • Three pricing tiers (serverless / dedicated GPU-hour / Enterprise) cover prototype-to-scale without rehosting

    Cons

    • Latency is good but typically not as low as Groq's LPU-based inference
    • Per-token pricing is competitive but not always the cheapest — DeepSeek's official API or OpenRouter aggregation can undercut on specific models
    • Serverless rate limits can surprise high-burst workloads and force an earlier-than-expected jump to dedicated deployments

    Together AI - 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

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    🔒 Security & Compliance Comparison

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    Security FeatureFireworks AITogether AI
    SOC2✅ Yes
    GDPR✅ Yes
    HIPAA
    SSO
    Self-Hosted❌ No
    On-Prem❌ No
    RBAC
    Audit Log
    Open Source❌ No
    API Key Auth✅ Yes
    Encryption at Rest✅ Yes
    Encryption in Transit✅ Yes
    Data ResidencyUS
    Data Retentionconfigurable
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