Fireworks AI vs Together AI

Side-by-side comparison of features, pricing, and ratings

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At a glance

DimensionFireworks AITogether AI
PricingPay-per-token (serverless) / prepaid billing from July 1, 2026Freemium (free tier + pay-as-you-go / dedicated plans)
Inference Performance3x speedups, sub-second latency, 30T+ tokens/day31% more TPS than TensorRT-LLM, FlashAttention-4
Model AccessDeepSeek V4, GLM 5.2, Qwen 3.7 Plus, MiniMax M3 (day-0 access)100+ open-source models including DeepSeek V4 Pro, Qwen3.7-Max, Llama 4 Maverick
Training CapabilitiesFull-spectrum: guided, config-led, custom RL; Multi-LoRAFine-tuning with research-backed techniques, pre-training on GPU clusters
Target AudienceAI product teams, enterprises, startups building coding assistantsDevelopers, researchers, enterprises scaling from sandbox to AI Factory
Key DifferentiatorExclusive early access to frontier models, RL inference scalingZero egress storage, CodeSandbox SDK, ISO 27001 certification

If you need the absolute lowest latency and earliest access to frontier open-weight models for real-time coding assistants, Fireworks AI is the clear winner — especially with its newer models like GLM 5.2 and MiniMax M3. However, if you want a broader model library, a freemium entry point, and enterprise-ready certifications without vendor lock-in, Together AI's zero-egress storage and ISO 27001 compliance make it a safer bet for compliance-heavy teams.

Fireworks AI
Fireworks AI

Fastest inference and training for open-weight generative AI models

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Together AI
Together AI

Full-stack AI cloud for inference, fine-tuning, and pre-training on open-source models.

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Pricing
Paid
Freemium
Plans
Pay per token (varies by model)
$7.00/hr (H100), $12.00/hr (B300)
Varies by model size ($0.50-$40.00/1M training tokens)
Custom
Usage-based
Popularity
3.8k views
3.6k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
WebAPI
WebAPI
Categories
⚙️ Developer Infrastructure
⚙️ Developer Infrastructure
Features
Serverless 2.0 with Priority, Fast, Standard tiers
On-demand dedicated GPU deployments (H100, H200, B200, B300)
Reserved capacity with guaranteed quotas
Full-spectrum training: guided, config-led, custom RL
Multi-LoRA fine-tuning for multiple adapters
OpenAI and Anthropic API compatibility
Cached input tokens at 50% price
Batch inference at 50% serverless pricing
RL inference scaling elastic across global traffic
FireConnect for agentic integrations
Multi-region deployment support
Quantized models with minimal quality degradation
Exclusive early access to frontier open-weight models
Seamless production handoff for every checkpoint
Vision, audio, and image generation models
Serverless inference APIs for 100+ open-source models
Batch inference up to 30B tokens per model
Dedicated model inference on custom hardware
GPU clusters with GB300, GB200, B200, H200, H100
AI Factory custom infrastructure at frontier scale
Fine-tuning with research-backed techniques
Managed storage with zero egress fees
Sandbox dev environments via CodeSandbox SDK
Evaluations for model quality measurement
Model library with playground and chat
Voice agents for production voice applications
FlashAttention-4 kernel optimization
ATLAS kernel collection for accelerated compute
Pre-training speed up to 90% faster (Together Kernel Collection)
Dedicated container inference for generative media
Integrations
OpenAI API
Anthropic API
Azure Foundry
NVIDIA Foundry
PyTorch ecosystem
GitHub Copilot
Claude Code
Codex
OpenCode
MCP
CodeSandbox
Hugging Face
Weights & Biases
LangChain
LlamaIndex
Python SDK
Node.js SDK
REST API
WebSocket
Jupyter Notebooks

Feature-by-feature

Fireworks AI focuses on inference speed and training flexibility. It processes over 30 trillion tokens daily and offers Serverless 2.0 with Priority, Fast, and Standard tiers via a single API. Its inference engine delivers 3x speedups and sub-second latency, crucial for coding assistants like Cursor and Notion. Fireworks also provides full-spectrum training (guided, config-led, custom RL) with Multi-LoRA support, and exclusive early access to models like GLM 5.2, Qwen 3.7 Plus, and MiniMax M3. Notably, it offers managed training infrastructure for GLM 5.2 itself. Together AI counters with serverless inference for 100+ open-source models and batch inference up to 30B tokens per model. It boasts FlashAttention-4 and ATLAS kernel collections for 31% more TPS than TensorRT-LLM. Together AI also offers dedicated GPU clusters (GB300, GB200, B200, H200, H100), fine-tuning with research-backed techniques, managed storage with zero egress fees, sandbox dev environments via CodeSandbox SDK, and ISO 27001:2022 certification. Its model library includes DeepSeek V4 Pro, Qwen3.7-Max, and Llama 4 Maverick. Fireworks lacks a no-code GUI and sandboxed dev environments, while Together AI may not provide day-0 access to the very latest models.

Pricing compared

Fireworks AI operates on a pay-per-token model for serverless inference (e.g., GLM 5.2 launch with pay-per-token pricing) and will transition to prepaid billing starting July 1, 2026. It also offers on-demand dedicated GPU deployments and reserved capacity with guaranteed quotas. This pricing suits high-volume, latency-sensitive workloads but may lead to unpredictable costs for variable traffic. Together AI has a freemium model: a free tier for experimentation, pay-as-you-go for serverless inference, and dedicated plans for GPU clusters with minimum commitments. Its batch inference is priced per token, with no mention of prepaid or reserved capacity. Together AI's zero egress fees on managed storage can reduce total costs for large datasets. For budget-sensitive users, Together AI's free tier is attractive, while Fireworks' upcoming prepaid model might appeal to planned high-traffic projects. Both platforms require careful budget management for large-scale production.

Who should pick which

  • Solo developer building a real-time coding assistant
    Pick: Fireworks AI

    Fireworks AI's sub-second latency and 3x speedups are critical for real-time code suggestions, and it offers early access to models like Kimi K2.7 Code optimized for agentic tasks.

  • Enterprise with compliance requirements
    Pick: Together AI

    Together AI's ISO 27001:2022 certification and zero egress fees on storage make it suitable for enterprises needing auditable security and data portability.

  • AI researcher fine-tuning open-source models
    Pick: Together AI

    Together AI offers research-backed fine-tuning techniques, FlashAttention-4, and a wide library of 100+ models, providing more flexibility for experiments.

  • Startup needing latest models with minimum cost
    Pick: Fireworks AI

    Fireworks AI provides day-0 access to frontier models like MiniMax M3 at 1/20th the cost of comparable models, and its serverless free tier may be used for prototyping before prepaid billing kicks in.

  • Team running batch inference on large corpora
    Pick: Together AI

    Together AI supports batch inference with up to 30B tokens per model and managed storage with zero egress fees, making it ideal for processing massive datasets efficiently.

Frequently Asked Questions

Which platform has the lowest latency for real-time applications?

Fireworks AI claims 3x speedups and sub-second latency, ideal for coding assistants. Together AI also offers high throughput (31% more TPS than TensorRT) but Fireworks' focus on latency gives it an edge.

Can I try either platform for free?

Together AI offers a freemium model with a free tier for experimentation. Fireworks AI currently has pay-per-token pricing, but prepaid billing starts July 1, 2026; there is no mention of a free tier.

Do they support fine-tuning?

Yes. Fireworks offers guided, config-led, and custom RL training with Multi-LoRA. Together AI provides fine-tuning with research-backed techniques and pre-training on GPU clusters.

Which platform gives early access to new open-weight models?

Fireworks AI explicitly offers 'exclusive early access to frontier open-weight models' and recently launched GLM 5.2, Qwen 3.7 Plus, and MiniMax M3 on its platform day-zero.

Is either platform ISO 27001 certified?

Yes, Together AI is ISO 27001:2022 certified. Fireworks AI does not mention any similar certification.

What integrations do they support?

Fireworks integrates with OpenAI API, Anthropic API, Azure Foundry, NVIDIA Foundry, PyTorch, GitHub Copilot, Claude Code, etc. Together AI integrates with CodeSandbox, Hugging Face, W&B, LangChain, LlamaIndex, and offers Python/Node.js SDKs.

Can I deploy dedicated GPU instances?

Fireworks offers on-demand dedicated GPU deployments and reserved capacity with guaranteed quotas. Together AI provides dedicated model inference on custom hardware and GPU clusters (GB300, GB200, B200, H200, H100).

Which platform is better for batch processing?

Together AI is specifically built for batch inference up to 30B tokens per model, with managed storage and zero egress fees. Fireworks does not emphasize batch inference as a core feature.

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