Fireworks AI vs Together AI
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Fireworks AI | Together AI |
|---|---|---|
| Best for | Production AI applications needing fast, low-cost inference with fine-tuning and multimodal support. | Developers and ML teams running, fine-tuning, and deploying 100+ open-source models with high throughput batch inference. |
| Pricing | Freemium: Free tier with 600 RPM and starter credits; pay-as-you-go usage-based; enterprise custom. | Freemium: Free tier with $5 free credits; pay-as-you-go usage-based; dedicated instances available. |
| Setup complexity | Low to medium: OpenAI-compatible SDK, integrations with LangChain and LlamaIndex, CLI support. | Low to medium: OpenAI-compatible API, integrations with LangChain and Hugging Face, developer environments. |
| Strongest differentiator | Cached input tokens at 50% discount; batch inference at 50% price; broad model library including DeepSeek, Kimi, Qwen. | 4x faster inference via FlashAttention and ATLAS; 100+ models; GPU clusters with B200, H200, H100; managed storage. |
Fireworks AI vs Together AI: For most production AI workloads requiring fast inference, low cost, and easy fine-tuning, Fireworks AI wins because of its 50% discount on cached tokens and batch inference, plus multimodal support. Together AI wins for teams needing the largest model selection and top raw throughput via FlashAttention-4 and GPU clusters. Choose Fireworks if you prioritize cost-efficiency in production; choose Together if you need maximum model variety and dedicated hardware control.
Feature-by-feature
Core Capabilities: Fireworks AI vs Together AI
Both platforms serve high-performance open-source model inference, but Fireworks AI focuses on production-optimized features like JSON mode, grammar-based generation, and function calling for agentic workflows. Together AI emphasizes breadth with 100+ models and high-speed inference via FlashAttention-4 and ATLAS. Fireworks also offers vision-language and audio transcription (Whisper V3), making it more multimodal-ready. Together AI provides GPU clusters (B200, H200, H100) and managed storage for custom model weights. Fireworks AI wins for multimodal production use; Together AI wins for model variety and raw speed.
AI/Model Approach: Fireworks vs Together
Fireworks AI hosts a curated library of top open-source models (DeepSeek, Qwen, Gemma, etc.) and supports fine-tuning (SFT, DPO, RFT) and custom model deployment. Together AI hosts over 100 models including Llama, Mistral, DeepSeek, and Qwen, and offers a fine-tuning platform with longer context support. Both support vision and voice models, but Fireworks explicitly includes image generation (FLUX.1) and audio transcription. Together AI provides evaluations and developer environments for model comparison. Fireworks wins for fine-tuning flexibility; Together wins for model selection breadth.
Integrations & Ecosystem: Fireworks AI vs Together AI
Fireworks AI integrates with LangChain, LlamaIndex, OpenAI SDK, Vercel AI SDK, and has a CLI. Together AI integrates with LangChain, LlamaIndex, Hugging Face, and offers an OpenAI-compatible API. Both support standard AI frameworks. Fireworks provides a richer SDK ecosystem with Vercel AI SDK support, while Together offers Hugging Face integration. Fireworks AI wins for serverless-first integrations; Together AI wins for Hugging Face ecosystem compatibility.
Performance & Scale: Fireworks vs Together
Fireworks AI emphasizes low latency with serverless auto-scaling, cached input tokens at 50% discount, and batch inference at 50% reduced price. Together AI claims up to 4x faster inference using FlashAttention-4 and ATLAS runtime-learning accelerators, and offers dedicated GPU clusters (B200, H200, H100). Public benchmarks are not yet available for direct comparison. Fireworks wins for cost-efficient scale due to caching and batch discounts; Together wins for top raw throughput with dedicated hardware.
Developer Experience: Fireworks AI vs Together AI
Fireworks AI offers an OpenAI SDK-compatible API, CLI, and extensive documentation. Together AI provides developer environments (sandbox), OpenAI-compatible API, and managed storage for model artifacts. Both are developer-friendly with quick setup and clear APIs. Fireworks has a slight edge with Vercel AI SDK integration and simpler fine-tuning APIs. Together AI offers more infrastructure control via dedicated instances and GPU clusters. Fireworks AI wins for ease of use in serverless mode; Together AI wins for teams needing infrastructure control.
Pricing compared
Fireworks AI pricing (2026)
Fireworks AI operates on a freemium model: a Free plan with 600 requests per minute and starter credits, a Pay-as-you-go plan with higher limits and access to all models, and an Enterprise plan with dedicated GPUs, SLA, and on-premise options. Key cost-saving features include cached input tokens at 50% discount and batch inference at 50% price. Custom pricing is available for enterprises.
Together AI pricing (2026)
Together AI also uses freemium: a Free plan with $5 free credits to start, and Pay-as-you-go usage-based pricing covering 100+ models, fine-tuning, and dedicated instances. There is no publicly listed enterprise tier beyond dedicated options. Together AI does not explicitly offer cached token discounts or batch price reductions.
Value-per-dollar: Fireworks AI vs Together AI
Fireworks AI wins for cost-conscious production deployments due to its 50% discount on cached tokens and batch inference. Together AI wins for teams needing maximum model selection and raw performance who are willing to pay for dedicated instances. For a startup with moderate traffic, Fireworks provides lower ongoing costs. For an ML team fine-tuning large models, Together's GPU clusters may offer better throughput but at potentially higher cost. Overall, Fireworks offers better value for most common inference workloads as of 2026.
Who should pick which
- Startup building a production chat app with low latencyPick: Fireworks AI
Fireworks AI offers cached token discounts and batch inference at 50% price, reducing costs for high-volume chat. Multimodal support adds flexibility.
- ML team fine-tuning Llama for custom Q&APick: Together AI
Together AI's fine-tuning platform with longer context support and GPU clusters (H100) is ideal for training custom models. 100+ models allow easy comparison.
- Enterprise deploying RAG with secure retrievalPick: Fireworks AI
Fireworks AI's SOC2 compliance, enterprise plan with dedicated GPUs, and serverless auto-scaling suit enterprise RAG deployments.
- Indie developer needing free credits for prototypingPick: Together AI
Together AI's Free plan gives $5 free credits, enough to test multiple models without upfront cost. Fireworks' free tier has limited RPM.
- Voice agent developer building multilingual botsPick: Fireworks AI
Fireworks AI includes audio transcription (Whisper V3) and speech support, making it suitable for voice applications. Together AI offers voice agent building tools but less audio variety.
Frequently Asked Questions
Which platform is cheaper for large-scale inference?
Fireworks AI is generally cheaper for large-scale inference due to its 50% discount on cached input tokens and batch inference at half price. Together AI does not publicly offer such discounts, making it potentially more expensive for high-volume workloads. As of 2026, choose Fireworks for cost-effective production inference.
Can I use OpenAI SDK with both Fireworks and Together?
Yes, both Fireworks AI and Together AI offer APIs compatible with the OpenAI SDK. This allows you to switch between them with minimal code changes. Fireworks also supports Vercel AI SDK, while Together provides additional developer environments. This makes migration straightforward for developers already using OpenAI's client library.
Do both platforms support fine-tuning of open-source models?
Yes, both Fireworks AI and Together AI support fine-tuning. Fireworks offers SFT, DPO, and full-parameter fine-tuning, while Together provides a fine-tuning platform with longer context support and GPU clusters. For deeper fine-tuning control, Together offers dedicated hardware, but Fireworks is simpler for quick fine-tuning tasks.
How do the model libraries differ between Fireworks and Together?
Together AI hosts over 100 open-source models including Llama, Mistral, DeepSeek, and Qwen. Fireworks AI has a curated library with similar top models plus specialized options like Kimi and Gemma, and also supports image generation (FLUX.1) and audio transcription. Together offers broader selection; Fireworks offers better multimodal coverage.
Which platform is better for non-technical users?
Neither platform is designed for non-technical users; both require programming knowledge. Fireworks AI has a slightly simpler API and CLI, but both expect developer skills. For no-code AI building, consider other platforms like Cohere or hosted GPT services. Both Fireworks and Together are best for developers and ML engineers.
Can I deploy custom models on dedicated hardware?
Yes, both platforms offer dedicated instances. Fireworks AI's Enterprise plan includes dedicated GPUs, and Together AI provides dedicated model inference and GPU clusters (B200, H200, H100). Together offers more granular hardware choices, while Fireworks focuses on simple serverless-to-dedicated upgrade paths.
Is there a free tier to start testing?
Both offer free tiers: Fireworks AI gives 600 RPM and starter credits without requiring payment info; Together AI provides $5 free credits. Fireworks' free tier is more generous for ongoing testing with higher rate limits, while Together's credits expire but allow testing of many models.
Which platform is better for batch processing large datasets?
Fireworks AI offers batch inference at 50% discount compared to real-time, making it more cost-effective for high-volume batch jobs. Together AI also supports batch inference but without a public discount. Fireworks wins for batch processing on a budget, while Together may be faster with dedicated instances if cost is secondary.
Last reviewed: May 12, 2026