Fireworks AI
Fastest inference platform for open-source LLMs with sub-second latency and production-grade reliability
250+
Models
<200ms
TTFT Latency
$55M
Series A
Overview
Fireworks AI is a blazing-fast inference platform built by former Meta AI researchers, optimized for running open-source large language models at production scale. Their custom inference engine delivers industry-leading speed with sub-200ms time-to-first-token and throughput up to 300+ tokens per second. Fireworks supports the full spectrum of open models including Llama 3.1 405B, Mixtral, DeepSeek, Qwen, and many more—all through an OpenAI-compatible API that makes migration seamless. The platform excels at compound AI systems where you need to chain multiple model calls with minimal latency overhead.
The Verdict
Who Should Use Fireworks AI?
Best For
- Production apps needing low latency
- Companies wanting open model flexibility
- Developers building multi-step AI pipelines
- Teams doing fine-tuning and custom models
- Cost-conscious scaling (cheaper than OpenAI)
Not Ideal For
- Need GPT-4/Claude specifically (use native)
- Absolute fastest inference (try Groq)
- Simple prototypes (OpenAI simpler)
- Strict compliance requirements
What's Great
- Exceptionally fast inference speeds
- OpenAI-compatible API (easy migration)
- Wide model selection (250+ models)
- Generous free tier ($1 credit)
- Custom fine-tuning support
- Function calling & JSON mode
- Serverless and dedicated options
Watch Out For
- No proprietary frontier models
- Less documentation than OpenAI
- Smaller community than major providers
- Dedicated deployments can be pricey
- Rate limits on free tier
Reddit r/LocalLLaMA · Developer feedback
Pricing
Free Tier
$0
$1 credit, rate limited
Serverless
$0.20/1M tokens
Llama 3.1 8B, pay-as-you-go
Llama 3.1 70B
$0.90/1M tokens
Larger model, better quality
Llama 3.1 405B
$3.00/1M tokens
Frontier open-source model
View all features & details
Key Features
- OpenAI-compatible REST API
- Sub-200ms time-to-first-token
- 300+ tokens/sec throughput
- Function calling support
- JSON mode & structured output
- Streaming responses
- Fine-tuning platform
- Model quantization (FP8, INT4)
Supported Models
- Llama 3.1 (8B, 70B, 405B)
- Llama 3.2 (1B, 3B, 11B Vision)
- Mixtral (8x7B, 8x22B)
- DeepSeek Coder V2
- Qwen 2.5 series
- FireFunction (function calling)
- Stable Diffusion XL
- Whisper (speech-to-text)
Deployment Options
- Serverless (auto-scaling)
- On-demand dedicated
- Reserved capacity
- Custom fine-tuned models
- Private deployments
Developer Tools
- Python SDK
- TypeScript/JavaScript SDK
- REST API
- LangChain integration
- LlamaIndex support
- OpenAI SDK compatible
How It Compares
| Feature | Fireworks AI | Together AI | Groq |
|---|---|---|---|
| Speed (TTFT) | <200ms | ~300ms | <100ms |
| Throughput | 300+ tok/s | 200+ tok/s | 500+ tok/s |
| Model Selection | 250+ models | 100+ models | 15 models |
| Llama 3.1 8B Price | $0.20/1M | $0.20/1M | Free (limited) |
| Llama 3.1 70B Price | $0.90/1M | $0.90/1M | $0.59/1M |
| Fine-tuning | Yes | Yes | No |
| Dedicated Deploy | Yes | Yes | No |
| Free Tier | $1 credit | $5 credit | Generous |
| Best For | Production scale | Fine-tuning | Raw speed |
Company Background
- Founded 2022 by ex-Meta AI team
- $55M Series A (a16z, Benchmark)
- Custom FireAttention engine
- Focus on compound AI systems
Technical Highlights
- Speculative decoding
- Continuous batching
- PagedAttention optimization
- FP8 quantization support
User Reviews
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