Fast inference for open-source AI models
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Reviews onG2
2 reviews tracked·4 press mentionsThe Bottom Line
Entry price
Paid plans only
Biggest pro
No cold starts and automatic scaling across GPU clusters
Biggest con
No free tier beyond the initial $1 credit for new users
TL;DR - Fireworks AI
- Cloud inference platform running 400+ open-source AI models with serverless deployment and no cold starts
- Per-token pricing starts at $0.10 per 1M tokens for small models; on-demand GPUs from $2.90/hour
- Supports fine-tuning with SFT and DPO, plus SOC 2, HIPAA, and GDPR compliance for enterprise use
Pricing: usage_based
Best for: Enterprises & pros
What is Fireworks AI?
Fireworks AI is a cloud inference platform for running open-source generative AI models. It provides serverless API endpoints for 400+ models spanning LLMs, image generation, vision, and audio, with no cold starts or GPU management required. Developers can fine-tune models using supervised learning, preference optimization (DPO), and quantization-aware techniques, then deploy them on shared or dedicated GPU infrastructure. The platform supports on-demand deployments on A100, H100, H200, and B200 GPUs with per-second billing. Fireworks is SOC 2, HIPAA, and GDPR compliant, and offers zero data retention options. Customers like Notion have reported latency reductions from 2 seconds to 350ms after switching to the platform.
Available on: Web
Pros & Cons
Pros
- No cold starts and automatic scaling across GPU clusters
- $1 free credit for new users to test without commitment
- Per-token pricing keeps costs predictable for variable workloads
- Supports latest open-source models including DeepSeek, Qwen, and Llama
- Fine-tuning available directly on the platform without separate tooling
- SOC 2, HIPAA, and GDPR compliance suitable for regulated industries
Cons
- No free tier beyond the initial $1 credit for new users
- Pricing varies significantly by model size and type
- On-demand GPU deployments require minimum hourly spend
- Less suited for teams wanting managed prompt engineering or RAG pipelines
- Smaller community and ecosystem compared to AWS Bedrock or Azure AI
Ratings Across the Web
3.8(2 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
Serverless inference for 400+ open-source AI models with no cold startsSupport for LLMs, image generation, vision, and audio modelsModel fine-tuning with SFT, DPO, and quantization-aware trainingOn-demand GPU deployments on A100, H100, H200, and B200 hardwareOpenAI-compatible API for drop-in replacement workflowsCached input token pricing at 50% discount across all text modelsDistributed virtual cloud for global low-latency inferenceSOC 2, HIPAA, and GDPR compliance with zero data retention optionInteractive model playground for testing before integrationSDKs, CLI tools, and REST API for developer integration
Pricing Plans
Pricing checked Jul 11, 2026
Serverless
Free
- 400+ models available
- No cold starts or GPU setup
- Cached tokens at 50% discount
- High rate limits
- Postpaid billing
On-Demand Deployments
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- A100 80GB at $2.90/hour
- H100 80GB at $4.00/hour
- H200 141GB at $6.00/hour
- B200 180GB at $9.00/hour
- No charges for startup time
Enterprise
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- Dedicated infrastructure
- Bring-your-own-cloud deployment
- Zero data retention
- Custom SLAs and support
- SOC 2, HIPAA, GDPR compliance
Reviews
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Fireworks AI FAQ
How does Fireworks AI help developers deploy generative AI models?
Fireworks AI provides serverless API endpoints for over 400 open-source generative AI models, including LLMs and image generation models. It eliminates cold starts and the need for GPU management, allowing developers to deploy models efficiently.
Which teams would benefit most from using Fireworks AI?
Teams that require fast inference for open-source AI models and need to fine-tune them using techniques like supervised learning or preference optimization would benefit. Its compliance with SOC 2, HIPAA, and GDPR also makes it suitable for regulated industries.
How is Fireworks AI priced?
Fireworks AI offers per-token pricing, which helps maintain predictable costs for variable workloads. While there is no free tier beyond an initial $1 credit, on-demand GPU deployments do require a minimum hourly spend.
Can users fine-tune models directly within the Fireworks AI platform?
Yes, users can fine-tune models directly on the Fireworks AI platform. It supports techniques such as supervised learning, preference optimization (DPO), and quantization-aware methods without requiring separate tooling.
What kind of performance improvements can be expected with Fireworks AI?
Customers have experienced significant latency reductions, with one reporting a decrease from 2 seconds to 350ms after switching to the platform. This is due to features like no cold starts and automatic scaling across GPU clusters.
How does Fireworks AI compare to a competitor like Replicate?
Fireworks AI provides serverless API endpoints for a wide array of open-source models with no cold starts, similar to Replicate. However, Fireworks AI emphasizes direct fine-tuning capabilities on the platform and offers compliance certifications like SOC 2, HIPAA, and GDPR.
What are the main limitations of using Fireworks AI?
Fireworks AI does not offer a free tier beyond an initial $1 credit, and pricing can vary significantly based on model size and type. It is also less suited for teams specifically looking for managed prompt engineering or RAG pipelines, as these are not its primary focus.
Source: fireworks.ai