Decision comparison
Fireworks AI wins on inference speed and per-token cost efficiency for small-to-mid-size models with rates from a vendor-specific amount/1M tokens, batch discounts, and hardware-level latency optimizations. Together AI wins on platform breadth, onboarding generosity with a vendor-specific amount free credits, and dedicated GPU economics at a vendor-specific amount/GPU/hour for sustained workloads.
| Decision factor | Fireworks AI | Together AI |
|---|---|---|
| Best For | Latency-sensitive production inference with aggressive per-token pricing on small-to-mid models | Unified platform for training, fine-tuning, and serving with broader model catalog |
| Pricing Model | Fireworks AI uses pay-per-token serverless pricing with $1 free credits for new accounts. Models <4B: $0.10/1M tokens. 4B-16B: $0.20/1M tokens. >16B: $0.90/1M tokens. MoE 0-56B: $0.50/1M tokens. DeepSeek V3: $0.56/$1.68 per 1M input/output. Cached input: 50% discount. Batch inference: 50% discount. Fine-tuning LoRA SFT: $0.50-$10.00/1M training tokens by model size. On-demand GPU: H100 $7.00/hr, B200 $10.00/hr. Image generation: FLUX.1 Kontext Pro $0.04/image. Embeddings from $0.008/1M tokens. | Serverless inference: from $0.10/M tokens (small models) to $2.50/M tokens (large models). Dedicated endpoints: from $0.80/GPU/hour (A100). Fine-tuning: from $3/M tokens. Free tier: $5 in credits. Pay-as-you-go with no minimum. |
| Free Credits | a vendor-specific amount for new accounts | a vendor-specific amount for new accounts |
| Fine-Tuning | LoRA SFT from a vendor-specific amount-a vendor-specific amount/1M training tokens depending on model size | From a vendor-specific amount/M tokens with supervised fine-tuning support |
| Dedicated GPU | H100 at a vendor-specific amount/hr, B200 at a vendor-specific amount/hr on-demand | A100 from a vendor-specific amount/GPU/hour dedicated endpoints |
| Model Catalog | Curated set of optimized models including Llama, Mixtral, DeepSeek V3, Qwen | Broad catalog spanning Llama, Mistral, DBRX, Stable Diffusion and more |
| Feature | Fireworks AI | Together AI |
|---|---|---|
| Inference Capabilities | ||
| Serverless Inference | Pay-per-token with three model-size tiers from a vendor-specific amount/1M tokens | Pay-per-token from a vendor-specific amount/M to a vendor-specific amount/M tokens per model |
| Batch Inference | 50% discount on batch processing jobs | Available through dedicated endpoint allocation |
| Cached Input | 50% discount on cached prompt tokens | No separately listed cached input discount |
| Function Calling | Native function calling support in serverless endpoints | Supported on compatible model architectures |
| Image Generation | FLUX.1 Kontext Pro at a vendor-specific amount per image | Stable Diffusion family models available |
| GPU and Compute | ||
| Dedicated GPU Hardware | H100 at a vendor-specific amount/hr and B200 at a vendor-specific amount/hr on-demand | A100 from a vendor-specific amount/GPU/hour for dedicated endpoints |
| GPU Pricing Model | On-demand hourly billing for reserved compute | Dedicated endpoint hourly billing with reserved capacity |
| Embeddings | From a vendor-specific amount/1M tokens for embedding models | Available through serverless API at model-specific rates |
| Fine-Tuning and Training | ||
| Fine-Tuning Method | LoRA SFT with pricing from a vendor-specific amount-a vendor-specific amount/1M training tokens | Supervised fine-tuning from a vendor-specific amount/M tokens |
| Model Size Impact on Cost | Training cost scales with base model size across tiers | Flat per-token rate starting at a vendor-specific amount/M regardless of model size |
| Pricing and Onboarding | ||
| Free Tier | a vendor-specific amount in free credits for new accounts | a vendor-specific amount in free credits for new accounts |
| Minimum Commitment | No minimum; pay-as-you-go serverless billing | No minimum; pay-as-you-go with no commitment required |
| MoE Model Pricing | a vendor-specific amount/1M tokens for MoE models in the 0-56B range | Per-model pricing for MoE architectures |
| DeepSeek V3 | a vendor-specific amount input / a vendor-specific amount output per 1M tokens | Available at model-specific serverless rate |
Serverless Inference
Batch Inference
Cached Input
Function Calling
Image Generation
Dedicated GPU Hardware
GPU Pricing Model
Embeddings
Fine-Tuning Method
Model Size Impact on Cost
Free Tier
Minimum Commitment
MoE Model Pricing
DeepSeek V3
Fireworks AI wins on inference speed and per-token cost efficiency for small-to-mid-size models with rates from a vendor-specific amount/1M tokens, batch discounts, and hardware-level latency optimizations. Together AI wins on platform breadth, onboarding generosity with a vendor-specific amount free credits, and dedicated GPU economics at a vendor-specific amount/GPU/hour for sustained workloads.
Choose Fireworks AI if:
Choose Fireworks AI for latency-sensitive production inference on sub-16B models where per-token cost (a vendor-specific amount-a vendor-specific amount/1M), batch discounts (50% off), and hardware-optimized speed are top priorities.
Choose Together AI if:
Choose Together AI for a unified training-to-serving platform with a vendor-specific amount free credits, dedicated A100 GPUs at a vendor-specific amount/hr, and a broader model catalog for experimentation and fine-tuning.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Yes, many teams use a multi-provider strategy where they route different model sizes or workload types to the most cost-effective platform. Both expose OpenAI-compatible API endpoints, making switching straightforward.
Together AI generally offers a broader model catalog spanning language, image, and embedding models. Fireworks AI focuses on a curated set optimized for its inference engine including Llama, Mixtral, DeepSeek V3, and Qwen families.
Fireworks AI charges a vendor-specific amount-a vendor-specific amount/1M training tokens for LoRA SFT depending on model size. Together AI charges from a vendor-specific amount/M tokens. For small models under 4B, Fireworks is cheaper. For larger models, Together AI's flat rate is more competitive.
On Fireworks AI, after the a vendor-specific amount credit is consumed, usage bills at standard rates. On Together AI, after a vendor-specific amount is consumed, pay-as-you-go billing applies. Neither cuts off access; both transition to paid billing automatically.