Decision comparison
Fireworks AI vs Together AI
Fireworks AI wins on inference speed and per-token cost efficiency for small-to-mid-size models with batch discounts and hardware-level latency optimizations. Together AI wins on platform breadth, onboarding generosity with $5 free credits, and dedicated GPU economics at $0.80/GPU/hour for sustained workloads.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are model hosting platforms.
Quick Comparison
| 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 bills per token for serverless inference, with $1 in free credits for new accounts; per-model rates across its Standard, Priority and Fast serverless tiers are published in its documentation rather than on the pricing page. Embeddings are $0.008 per 1M input tokens up to 150M parameters, $0.016 from 150M to 350M, and $0.1 for Qwen3 8B. Managed training is priced per 1M training tokens, with LoRA SFT at $0.50 for models up to 16B, $3.00 from 16.1B to 80B, $6.00 from 80B to 300B and $10.00 above 300B; DPO and full-parameter tuning are multiples of those. On-demand GPU deployments from 1 September are $8.00 per hour for H100 and H200, $13.00 for B200, $15.00 for B300 and $20.00 for GB300, with a 1.5x premium on region-restricted deployments. | 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 | $1 for new accounts | $1 for new accounts |
| Fine-Tuning | LoRA SFT from $0.50-$10.00/1M training tokens depending on model size | From $3/M tokens with supervised fine-tuning support |
| Dedicated GPU | H100 at $8.00/hr, B200 at $13.00/hr on-demand | A100 from $0.80/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 |
Fireworks AI
- Best For:
- Latency-sensitive production inference with aggressive per-token pricing on small-to-mid models
- Pricing Model:
- Fireworks AI bills per token for serverless inference, with $1 in free credits for new accounts; per-model rates across its Standard, Priority and Fast serverless tiers are published in its documentation rather than on the pricing page. Embeddings are $0.008 per 1M input tokens up to 150M parameters, $0.016 from 150M to 350M, and $0.1 for Qwen3 8B. Managed training is priced per 1M training tokens, with LoRA SFT at $0.50 for models up to 16B, $3.00 from 16.1B to 80B, $6.00 from 80B to 300B and $10.00 above 300B; DPO and full-parameter tuning are multiples of those. On-demand GPU deployments from 1 September are $8.00 per hour for H100 and H200, $13.00 for B200, $15.00 for B300 and $20.00 for GB300, with a 1.5x premium on region-restricted deployments.
- Free Credits:
- $1 for new accounts
- Fine-Tuning:
- LoRA SFT from $0.50-$10.00/1M training tokens depending on model size
- Dedicated GPU:
- H100 at $8.00/hr, B200 at $13.00/hr on-demand
- Model Catalog:
- Curated set of optimized models including Llama, Mixtral, DeepSeek V3, Qwen
Together AI
- Best For:
- Unified platform for training, fine-tuning, and serving with broader model catalog
- Pricing Model:
- 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:
- $1 for new accounts
- Fine-Tuning:
- From $3/M tokens with supervised fine-tuning support
- Dedicated GPU:
- A100 from $0.80/GPU/hour dedicated endpoints
- Model Catalog:
- Broad catalog spanning Llama, Mistral, DBRX, Stable Diffusion and more
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Fireworks AI | Together AI |
|---|---|---|
| GitHub commits, 90d(Developer adoption) | 31 | 360 |
| GitHub stars(Developer adoption) | 10 | 10 |
| Search interest(Market interest) | 6 | 8 |
| Hacker News mentions, 90d(Community interest) | 0 | 3 |
| Hugging Face downloads(Product adoption) | 11.4k | 22.2k |
| Hugging Face likes(Product adoption) | 341 | 2.3k |
| PyPI weekly downloads(Developer adoption) | 260.8k | 354.8k |
| npm weekly downloads(Developer adoption) | Not available | 92.3k |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Fireworks AI
September 21, 2026Package vulnerabilities
PyPI · fireworks-ai@1.2.14
0 vulnerabilities
across 1 package
Repository security score
Not available
Together AI
September 21, 2026Package vulnerabilities
PyPI · together@2.35.0 · npm · together-ai@0.55.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Feature Comparison
| Feature | Fireworks AI | Together AI |
|---|---|---|
| Inference Capabilities | ||
| Serverless Inference | Pay-per-token with three model-size tiers; per-model rates are published in the documentation | Pay-per-token from $0.10/M to $2.50/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 | Stable Diffusion family models available |
| GPU and Compute | ||
| Dedicated GPU Hardware | H100 at $8.00/hr and B200 at $13.00/hr on-demand | A100 from $0.80/GPU/hour for dedicated endpoints |
| GPU Pricing Model | On-demand hourly billing for reserved compute | Dedicated endpoint hourly billing with reserved capacity |
| Embeddings | From $0.008/1M tokens for embedding models | Available through serverless API at model-specific rates |
| Fine-Tuning and Training | ||
| Fine-Tuning Method | LoRA SFT with pricing from $0.50-$10.00/1M training tokens | Supervised fine-tuning from $0.50/M tokens |
| Model Size Impact on Cost | Training cost scales with base model size across tiers | Flat per-token rate regardless of model size |
| Pricing and Onboarding | ||
| Free Tier | $1 in free credits for new accounts | $1 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 | MoE models in the 0-56B range | Per-model pricing for MoE architectures |
| DeepSeek V3 | Billed per 1M input and output tokens at the model's published rate | Available at model-specific serverless rate |
Inference Capabilities
Serverless Inference
Batch Inference
Cached Input
Function Calling
Image Generation
GPU and Compute
Dedicated GPU Hardware
GPU Pricing Model
Embeddings
Fine-Tuning and Training
Fine-Tuning Method
Model Size Impact on Cost
Pricing and Onboarding
Free Tier
Minimum Commitment
MoE Model Pricing
DeepSeek V3
Which to choose
Fireworks AI wins on inference speed and per-token cost efficiency for small-to-mid-size models with batch discounts and hardware-level latency optimizations. Together AI wins on platform breadth, onboarding generosity with $5 free credits, and dedicated GPU economics at $0.80/GPU/hour for sustained workloads.
Best-fit scenarios
Choose Fireworks AI if:
Choose Fireworks AI for latency-sensitive production inference on sub-16B models where 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 $5 free credits, dedicated A100 GPUs at $0.80/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.
Frequently Asked Questions
Can I use both Fireworks AI and Together AI simultaneously?
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.
Which platform supports more open-source models?
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.
How do fine-tuning costs compare between the two platforms?
Fireworks AI charges $0.50-$10.00/1M training tokens for LoRA SFT depending on model size. Together AI charges from $0.10/M tokens. Fireworks publishes its per-model serverless rates in its documentation. For larger models, Together AI's flat rate is more competitive.
What happens when I exhaust the free credits?
On Fireworks AI, after the $1 credit is consumed, usage bills at standard rates. On Together AI, after $5 in credits is consumed, pay-as-you-go billing applies. Neither cuts off access; both transition to paid billing automatically.