Fireworks AIvs
DeepInfra
DeepInfra: Both offer pay-per-token hosting of open-source models with competitive pricing.
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Fast inference and fine-tuning platform for open and custom AI models
Overview
Fireworks AI is an AI infrastructure platform for training, fine-tuning, and deploying machine learning models, built by former PyTorch engineers. It hosts 30+ optimized open and closed models, including DeepSeek, Qwen, and Kimi, with context windows exceeding 1 million tokens. Deployment options include serverless pay-per-token inference, on-demand dedicated instances, and reserved capacity, all compatible with OpenAI and Anthropic API formats. Its Nexus product routes coding-assistant requests across models to cut AI coding costs, and customers include Cursor, Vercel, Notion, and Sourcegraph.
The honest half
Concrete and checkable, so you can decide whether any of them matter to you. This is the half of a review a vendor will not write about Fireworks AI.
Cross-shopped
Each pairing was judged by two reviewers asking whether a buyer would genuinely weigh the two against each other. The ones that failed were deleted rather than published.
DeepInfra: Both offer pay-per-token hosting of open-source models with competitive pricing.
Anyscale: Both provide training and inference infrastructure for large-scale AI workloads.
Pricing
Taken from the vendor's own pricing page. Prices move, so check before you buy.
Serverless
On request
On-Demand
$7 /mo
Reserved
On request
Capabilities
Serverless inference
Pay-per-token access to 30+ optimized open and closed models
On-demand and reserved deployments
Dedicated GPU instances with multi-region support and priority hardware
Managed fine-tuning
LoRA and full-parameter SFT/DPO training priced per token
OpenAI/Anthropic API compatibility
Drop-in replacement for existing model API integrations
Nexus router
Intelligently routes AI coding requests to cut costs by 50-75%
Long context models
Supports context windows beyond 1 million tokens
Answered, with sources
Each answer names the page it came from, so you can check it rather than take our word for it.
Serverless inference uses postpaid, pay-per-token billing across Standard, Priority, and Fast tiers, with rates from $0.07 to $1.74 per million tokens depending on model.
SourceNew accounts receive $1 in free credit to try serverless inference before adding a payment method.
SourceDedicated on-demand instances range from $7-8/hour for H100/H200 GPUs up to $18-20/hour for GB300, billed per GPU second with no start-up surcharge.
SourceManaged training is billed per 1 million training tokens for supervised or preference tuning, while reinforcement tuning is billed per GPU hour.
SourceYes, region-restricted on-demand deployments carry a 1.5x premium over standard regional pricing.
SourceKeep looking
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