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AI Tool Comparison

Cerebras Inference vs Fireworks AI

A side-by-side breakdown to help you pick the right tool for your workflow.

Review Score assesses product quality; Fit Score is specific to a use case. Limited reviews do not establish overall product quality. How reviews work.

Cerebras Inference logo

Cerebras Inference

Add hosted inference through Cerebras’s OpenAI-compatible API, checking the current public model catalog and access limits before you build around a specific model.

Models
freemium
Not yet reviewedVisit site Tool details →
Fireworks AI logo

Fireworks AI

Run Llama, Mixtral, and 50+ open-source models at production speed: model-specific token billing with the same SDK you're already using.

Models
paid
Not yet reviewedVisit site Tool details →

Bottom Line

Catalog updated: August 2026

Cerebras Inference and Fireworks AI both compete in Models, overlapping most directly on coding. Cerebras Inference runs on a freemium model while Fireworks AI runs on a paid-only plan, which alone may settle it if budget or a free tier is a hard requirement.

Choose Cerebras Inference if…

Best for developers building real-time voice agents or interactive applications where inference speed is the bottleneck, and its edge is purpose-built wafer-scale chip architecture designed specifically for high-throughput inference, an approach distinct from typical GPU-based inference providers. Built around high-throughput inference speed as its core differentiator; the model selection is a curated set, not the full open-source catalog. Lean toward Fireworks AI instead if latency that consistently beats cloud providers, with model- and serving-tier-specific token pricing matters more for your use case.

Choose Fireworks AI if…

Best for developers who need fast, cheap inference on open-source models without sacrificing production reliability, and its edge is latency that consistently beats cloud providers, with model- and serving-tier-specific token pricing. A genuinely strong value pick for open-source inference, the free credit is too small to test real production load. Lean toward Cerebras Inference instead if purpose-built wafer-scale chip architecture designed specifically for high-throughput inference, an approach distinct from typical GPU-based inference providers matters more for your use case.

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AttributeCerebras InferenceFireworks AI
CategoryModelsModels
Pricingfreemiumpaid
Pricing DetailFree tier available / Pay-per-tokenGPT OSS 120B Serverless Standard (USD) / $0.15/1M uncached input tokens / $0.015/1M cached-input tokens / $0.60/1M output tokens
TWF Review ScoreNot yet reviewedNot yet reviewed

Key Features

Cerebras Inference

  • High-throughput inference on the current public catalog (gpt-oss-120b, qwen-3.8-27b); check Cerebras's own docs for current per-model performance figures
  • Wafer-scale chip architecture eliminates inter-chip communication overhead
  • Current public catalog includes gpt-oss-120b and qwen-3.8-27b; Llama and DeepSeek R1 may require Cerebras's Dedicated Endpoints rather than the public free tier
  • OpenAI-compatible API with streaming support
  • Trial and usage-based API access subject to current plan terms
  • Designed for real-time use cases like voice and interactive applications

Fireworks AI

  • OpenAI-compatible API for instant drop-in replacement
  • 50+ open-source models including Llama, Mixtral, and Gemma
  • Compound AI system deployment (multiple models in one call)
  • Function calling and JSON mode across all supported models
  • Fine-tuning API for custom model specialization
  • Sub-100ms time-to-first-token on most models

Pros

Cerebras Inference

  • •Purpose-built inference infrastructure
  • •OpenAI-compatible API integration
  • •Public and dedicated model-serving options to evaluate separately

Fireworks AI

  • •Best-in-class latency for open-source model inference
  • •Separate input, cached-input, and output token rates support cost estimation
  • •OpenAI-compatible API means zero migration effort

Cons

Cerebras Inference

  • Model selection is limited to a curated set, not the full open-source catalog
  • Purpose-built hardware means no custom model fine-tuning support
  • Very high throughput can mask context window limitations

Fireworks AI

  • Smaller model selection than OpenRouter
  • Fine-tuning has limited base model options vs dedicated platforms
  • Free credit is small, production workloads require billing setup

Explore Tool Details

Cerebras Inference Details →Fireworks AI Details →

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