Fireworks AI is the closest peer to Together AI in serverless LLM inference, broad OSS model coverage, competitive per-token pricing, dedicated endpoints when serverless scales out, and a focus on production-grade speed. The team claims 250% higher throughput and 50% faster than open-source serving engines, which matters for chat UX. The trade is parity: pricing and coverage are close enough to Together that the decision usually comes down to specific model availability and pricing on the models you care about most.
- High-performance serverless inference, 50% faster than open-source engines
- Postpaid billing with $1 free credit to start
- Embeddings pricing tiered by model size, $0.008-$0.10/M tokens
- Fine-tuning workflows with managed infrastructure
- On-demand GPU pricing from $7/hr (H100/H200) to $12/hr (B300)
- Specific model availability shifts vs Together, check both before committing
- Pricing for top-tier OSS models is comparable, not dramatically cheaper
- Smaller catalog than Together (200+ vs Fireworks' core lineup)
- Documentation depth trails Together AI's editorial polish
- Newer than Together, less institutional usage at scale
Fireworks AI is a serverless inference platform for open-source LLMs, built around a custom serving stack the team claims is 250% higher throughput and 50% faster than open-source inference engines. The catalog covers the OSS LLM lineup most teams actually reach for, Llama, Qwen, DeepSeek, Mistral, embeddings, with serverless per-token pricing and dedicated endpoints for production scale.
Where it fits
Fireworks is the right pick when you want a Together-like serverless platform and the specific models you’re targeting have better availability or pricing on Fireworks. For most workloads Together and Fireworks are interchangeable, the decision usually comes down to model coverage and per-token cost on the specific models that dominate your traffic.
For fine-tuning specifically, Fireworks ships managed workflows that make custom model training accessible without operating infrastructure. For function calling and JSON-mode inference, the platform has invested in production-grade tool-use primitives.
Avoid Fireworks when the broader Together catalog is the better match, when Groq’s speed advantage matters more on supported models, or when you need the deepest multimodal coverage (Replicate fits better).
Pricing in practice
Serverless: pay per token across the catalog with rate limits and postpaid billing, $1 in free credits to start. Embeddings are tiered by model size: base models up to 150M params at $0.008/M tokens, 150M-350M at $0.016/M, Qwen3 8B at $0.10/M.
Dedicated GPU endpoints: H100/H200 from $7/hr, B300 up to $12/hr. The pricing model is similar to Together, serverless for variable workloads, dedicated when sustained throughput makes the math work.
How it compares
Together AI, Closest peer with broader catalog and slightly more mature ecosystem. Pick when you want the most options and a Together-deep platform.
Groq, Dramatically faster on supported models via custom LPU hardware. Pick when raw speed dominates and the model you need is supported.
Replicate, Multimodal coverage beyond just LLMs. Pick when you also need image, video, or voice.
What changed recently
Fireworks has continued to invest in serving-stack performance through 2025-2026 with the speed claims (250% throughput, 50% faster than OSS engines) reflecting kernel-level optimization work. Fine-tuning workflows have expanded to support more model families and adapter formats. Function calling and structured output have become first-class primitives for tool-use applications. The dedicated endpoint tier added B300 GPU support, matching Together’s frontier hardware availability.
Sources
- Fireworks AI Pricing, fireworks.ai
- Fireworks AI Docs, fireworks.ai
- Fireworks Models, fireworks.ai
- Fireworks Fine-tuning, fireworks.ai
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