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

Per-million-token pricing per model (text models from ~$0.20/M up depending on size; image models per-image)
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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
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Feature Comparison

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FeatureFireworks AITogether AI
CategoryAI Model Hosting & InferenceAI Model Hosting & Inference
Pricing Plans3 listed plans4 listed plans
Starting PricePer-million-token pricing per model (text models from ~$0.20/M up depending on size; image models per-image)$0.02/1M tokens
Key Features
  • • High-Performance Inference Engine: Fireworks has built a custom inference engine optimized for maximum throughput and minimum latency on open-source models. The engine runs on globally distributed infrastructure using the latest GPU hardware. According to Fireworks case studies, Sourcegraph achieved latency reductions from 2 seconds to 350 milliseconds after migrating to the platform. Techniques like quantization with minimal quality degradation and speculative decoding are applied automatically, allowing developers to get production-grade performance without manual optimization.
  • • Advanced Fine-Tuning Pipeline: The fine-tuning system supports multiple advanced techniques beyond standard supervised fine-tuning, including reinforcement learning from human feedback, quantization-aware tuning that maintains quality while reducing model size, and adaptive speculation for faster generation. This allows teams to customize models for domain-specific tasks like code generation, customer support, or document analysis without needing dedicated ML engineers or training infrastructure.
  • • Enterprise-Grade Security and Compliance: Fireworks provides a comprehensive enterprise security posture with SOC2, HIPAA, and GDPR compliance certifications. The platform offers zero data retention policies ensuring that no customer data is stored after inference, bring-your-own-cloud deployment options for organizations with strict data residency requirements, and complete data sovereignty guarantees. This makes it suitable for regulated industries like healthcare and finance.
  • • 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 Residency—US
Data Retention—configurable
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