Fireworks AI vs Together AI
Detailed side-by-side comparison to help you choose the right tool
Fireworks AI
AI Platform
Fast inference platform for open-source AI models with optimized deployment, fine-tuning capabilities, and global scaling infrastructure.
Was this helpful?
Starting Price
CustomTogether AI
đ´DeveloperAI Models
Cloud platform for running open-source AI models with serverless inference, fine-tuning, and dedicated GPU infrastructure optimized for production workloads.
Was this helpful?
Starting Price
$0.02/1M tokensFeature Comparison
Scroll horizontally to compare details.
Fireworks AI - Pros & Cons
Pros
- âExceptionally fast inference speeds with an optimized engine delivering industry-leading throughput and latency, with customers like Sourcegraph reporting latency reductions from 2 seconds to 350 milliseconds according to published case studies
- âBroad model catalog with over 50 serverless models including Llama 3.1/3.3, DeepSeek V3, Qwen 2.5, Gemma 2, and Mixtral, accessible via OpenAI-compatible API calls
- âAdvanced fine-tuning capabilities including reinforcement learning, quantization-aware tuning, and adaptive speculation without requiring deep ML infrastructure knowledge
- âEnterprise-grade compliance with SOC2, HIPAA, and GDPR certifications, zero data retention, bring-your-own-cloud options, and data sovereignty guarantees
- âServerless deployment with no cold starts and automatic GPU scaling, eliminating infrastructure management overhead
Cons
- âLimited to open-source models only â no access to proprietary models like Claude, GPT-4, or Gemini, requiring separate providers for those
- âPer-token pricing can become expensive at very high volumes compared to self-hosting the same open-source models on dedicated GPU infrastructure
- âTraining capabilities are still in preview and not yet production-ready, so the platform is primarily an inference and fine-tuning service for now
- âDocumentation and community resources are smaller compared to major cloud providers like AWS Bedrock or Google Vertex AI
Together AI - Pros & Cons
Pros
- âDramatically lower costs (5-20x) compared to proprietary models while maintaining quality
- âSuperior inference performance through custom optimizations and ATLAS acceleration
- âComprehensive fine-tuning capabilities with automatic deployment and scaling
- âOpenAI-compatible API enables seamless migration from existing applications
- âAccess to latest open-source models often before other hosting platforms
- âFull-stack platform covering inference, training, and GPU infrastructure
Cons
- âOpen-source models may not match GPT-4/Claude on highly complex reasoning tasks
- âOccasional capacity constraints during peak usage on popular models
- âFine-tuning requires ML expertise to achieve optimal results for specialized use cases
- âLimited proprietary model access (no GPT-4 or Claude integration)
- âDocumentation and community support less extensive than major cloud providers
Not sure which to pick?
đ¯ Take our quiz âđ Security & Compliance Comparison
Scroll horizontally to compare details.
đĻ
đ
Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.
Ready to Choose?
Read the full reviews to make an informed decision