Development & Code

Fireworks AI

Production AI infrastructure

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Visit fireworks.ai

Your team's burning through API credits running open-source models on standard platforms. Fireworks AI changes that equation with optimized inference that cuts response times by 3x and drops latency from 2 seconds to 350 milliseconds. Built for AI developers and enterprises, it handles the complete model lifecycle without the infrastructure headaches.

Fireworks AI runs open-source AI models through serverless deployment. It fine-tunes them too. It scales them automatically. No GPU setup required. No cold starts either. Auto-scaling handles demand spikes automatically while globally distributed infrastructure keeps things running smoothly.

Fine-tuning gets serious attention here. You can use reinforcement learning. Quantization-aware tuning works. Adaptive speculation customizes models further. Enterprise security boxes are checked with SOC2, HIPAA, and GDPR compliance — plus zero data retention if that matters to your legal team.

A machine learning engineer at a fintech startup could deploy Whisper V3 Large for transcription at $0 per million tokens, then scale up to FLUX.1 Kontext Pro image generation at $0.04 per image as usage grows. The pricing structure varies wildly between models. OpenAI gpt-oss-20b costs $0.07 per million input tokens but jumps to $0.3 for output.

Context windows range from 4096 to 262144 tokens depending on the model. Integrations with Sourcegraph, Notion, and Cursor keep it connected to existing workflows. Customer testimonials claim it beats competitors on performance, though there's no free tier to test that claim yourself.

Frequently asked

7 questions

Which open-source AI models does Fireworks AI support?

They've got Whisper V3 Large for transcription, FLUX.1 Kontext Pro for image stuff, and OpenAI gpt-oss-20b for text. It's all open-source models -- no proprietary ones. Context windows? They're all over the place, from 4,096 tokens up to 262,144 depending on what you pick.

How much does fine-tuning cost on Fireworks AI?

They don't actually say what fine-tuning costs. Just inference pricing, which is pretty wild between models. Whisper V3 Large is free ($0 per million tokens), but OpenAI gpt-oss-20b hits you with $0.07 for input and $0.3 for output. You'll have to ask them directly about fine-tuning prices.

Can I test Fireworks AI before paying?

Nope, no free tier. You've gotta pay upfront to try their platform. Makes it tough to check if their speed claims are legit before you commit your money.

What's the difference between Fireworks AI's fine-tuning and standard fine-tuning?

They do reinforcement learning for fine-tuning -- plus quantization-aware tuning and adaptive speculation. Goes way beyond just tweaking parameters. They handle the technical mess while you focus on your training data and how you want the model to behave.

Does Fireworks AI store my data or API requests?

Enterprise customers can get zero data retention policies if privacy's a concern. They're SOC2, HIPAA, and GDPR compliant too (good for regulated industries). Your legal team can work with them to make sure nothing gets stored on their servers.

How does Fireworks AI handle traffic spikes without cold starts?

It's serverless with auto-scaling that responds to demand changes automatically. No cold starts because models stay warm on their distributed infrastructure. Your API calls get consistent response times even when traffic goes crazy.

Which development tools integrate with Fireworks AI?

It connects with Sourcegraph for code search, Notion for docs, and Cursor for AI-assisted coding. You can use their models right in your existing workflow -- no platform switching needed.