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AI infrastructure · Tool
Fireworks AI
ResearchedFireworks AI is a generative AI inference platform processing 40T+ tokens daily, offering blazing-fast serverless and on-demand deployment of open-source LLMs and image models with fine-tuning, OpenAI/Anthropic-compatible APIs, and Batch processing.
Official site snapshots
Visit live site See the official site at a glance
Read-only public captures of Fireworks AI’s homepage. Screenshots are dated, never live embeds, and open full-screen.
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In one minute
Start here for the decision-making essentials: what Fireworks AI does, who it is for, how it is accessed, and the first-party sources behind this profile.
Pricing5 options
Serverless pricing is pay per token with high rate limits and postpaid billing
new users
On-Demand Deployments are billed per GPU second, with no extra charges for start-up times
Cached input tokens are priced at 50% and batch inference at 50% of serverless pricing for
50% lower cost compared to typical Serverless (synchronous) API pricing
Platforms
Web
API accessNot public
Founded2022
AvailabilityWeb / remote
LicenseProprietary
Best suited to
Source-backed fit Developers building generative AI applications at scale Teams needing fastest inference for open-source LLMs and image models Organizations requiring custom model fine-tuning and deployment High-volume async batch processing workloads Developers building generative AI applications
Decision support
6 sourced answersCommon questions and adoption checks
Short answers to the questions buyers and builders commonly ask about Fireworks AI. Each answer cites the shared ledger below, where every source is listed once.
01What does Fireworks AI say it can do?
Provides fastest inference for generative AI, processing 40T+ tokens per day · Run bulk async workloads with no rate limits, 50% lower cost, and 24-hour turnaround via B · Blazing-fast inference platform that enables developers to build with generative AI to acc · Can generate 1024x1024 images in 30 steps in just 1 second
Fireworks processes 40T+ tokens per day
02Who is Fireworks AI intended for?
Developers building generative AI applications
enables developers to build with generative AI to accelerate product innovation
03What use cases does Fireworks AI describe?
Evaluations, data generation, data augmentation, ETL pipelines, and daily bulk processing · Computer use agents leverage VLMs to autonomously navigate and interact with software inte · Fireworks AI fine-tuning reduced production AI latency from about 2 seconds to 350 millise · Code Assistance, Conversational AI, Agentic Systems, Search, Multimodal, and Enterprise RA
Evaluations: Benchmark across models to identify the best model for your use case • Data generation: Generate bulk outputs using large models to fine-tune smaller models • Data augmentation: Create paraphrases, sentiment labels, or question-answer pairs at scale • ETL Pipelines and Daily bulk processing
04What should teams verify before adopting Fireworks AI?
Dataset size must be under 500 MB per batch job, with no upper limit on the number of requ
There is no upper limit on the number of requests in a batch job. The dataset must be <500 MB in size.
Ledger citation[2] fireworks.ai/blog/batch-api
05What pricing information is available for Fireworks AI?
Serverless pricing is pay per token with high rate limits and postpaid billing; new users · On-Demand Deployments are billed per GPU second, with no extra charges for start-up times · Cached input tokens are priced at 50% and batch inference at 50% of serverless pricing for · 50% lower cost compared to typical Serverless (synchronous) API pricing
Serverless Pricing Pay per token, with high rate limits and postpaid billing. Get started with $1 in free credits.
06Does Fireworks AI document API access?
Serverless API is OpenAI and Anthropic compatible · Image generation APIs available with comprehensive features · Fireworks offers an OpenAI-compatible API that supports harnesses such as OpenCode and Cli · Fireworks supports an OpenAI-compatible Responses API endpoint with first-class Model Cont
Serverless. Pay per token with Priority and Fast options to meet your requirements. OpenAI and Anthropic compatible.
Decision guide
AI infrastructureCapabilities and operating fit
This profile connects the jobs Fireworks AI is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
Common use cases
- Real-time visual intelligence and computer use agents leveraging VLMs
- Bulk evaluations, data generation, augmentation, and ETL pipelines
- Custom model fine-tuning with supervised, preference, VLM SFT, or reinforcement fine tunin
- Image generation including text-to-image, Image-to-Image, and ControlNet workflows
- High-volume AI evaluations via Azure Foundry endpoints
- Evaluations, data generation, data augmentation, ETL pipelines, and daily bulk processing
Topics mapped
Verified capabilities
- Provides fastest inference for generative AI, processing 40T+ tokens per day
- Run bulk async workloads with no rate limits, 50% lower cost, and 24-hour turnaround via B
- Blazing-fast inference platform that enables developers to build with generative AI to acc
- Can generate 1024x1024 images in 30 steps in just 1 second
- Vision model platform is built on the FireAttention serving stack that also powers LLMs
- Prompt caching support is available for vision models
- Provides speculative decoding for faster inference
- Provides Multi-LoRA technology for scalable inference
Recorded integrations
Deployable through Azure endpoints via Foundry for high-volume evaluationsFireworks offers FireConnect, a way to keep using Claude Code while routing through FirewoFireworks integrates with the open Model Context Protocol (MCP), an open protocol that staFireworks AI partners with Stability AI to host and serve the Stable Diffusion 3 and SD3-t
Intended audiences
Access signals
- Pricing model
- Serverless pricing is pay per token with high rate limits and postpaid billing; new users · On-Demand Deployments are billed per GPU second, with no extra charges for start-up times · Cached input tokens are priced at 50% and batch inference at 50% of serverless pricing for · 50% lower cost compared to typical Serverless (synchronous) API pricing
- API
- Not publicly listed
- Source links
- 17 recorded
Source-backed
Updated July 22, 2026Verified facts
Each fact points to a recorded source, making it easy to distinguish verified product information from claims that need checking.
Fireworks AI - Fastest Inference for Generative AI
Processes 40T+ tokens per day · Supports state-of-the-art open-source LLMs and image models · Fine-tunes and deploys custom models at no additional cost
Provides fastest inference for generative AI, processing 40T+ tokens per day
Supports state-of-the-art, open-source LLMs and image models
Allows fine-tuning and deploying custom models at no additional cost
Serverless API is OpenAI and Anthropic compatible
Fireworks has raised a Series D and reached $1B ARR
View 32 more verified facts
Serverless pricing is pay per token with high rate limits and postpaid billing; new users get $1 in free credits
Serverless Inference offers per token pricing with zero setup and no cold starts
On-Demand Deployments are billed per GPU second, with no extra charges for start-up times
Fine Tuning lets customers customize open models with their own data, including supervised, preference, VLM SFT, and reinforcement fine tuning
Cached input tokens are priced at 50% and batch inference at 50% of serverless pricing for both input and output tokens across all text and vision language models
Run bulk async workloads with no rate limits, 50% lower cost, and 24-hour turnaround via Batch API
Evaluations, data generation, data augmentation, ETL pipelines, and daily bulk processing
50% lower cost compared to typical Serverless (synchronous) API pricing
Dataset size must be under 500 MB per batch job, with no upper limit on the number of requests
Fireworks AI announced its Series D and is at $1B ARR
Blazing-fast inference platform that enables developers to build with generative AI to accelerate product innovation
Developers building generative AI applications
Image generation APIs available with comprehensive features
Segmind Stable Diffusion 1B (SSD-1B), described as one of the fastest diffusion-based text-to-image models
SDXL model with Image-to-Image generation and ControlNet support
Can generate 1024x1024 images in 30 steps in just 1 second
Fireworks.ai, Inc.
Announced Series D funding and $1B ARR
Website at https://fireworks.ai
No AI Training on Your Data without explicit opt-in
Supports open models (with optional opt-in for data retention)
Fireworks announced Series D funding and reached $1B ARR
Vision model platform is built on the FireAttention serving stack that also powers LLMs
Llama 4 Scout & Maverick added to vision model platform
InternVL3 added to vision model platform
Prompt caching support is available for vision models
Computer use agents leverage VLMs to autonomously navigate and interact with software interfaces
Supports state-of-the-art open-source LLMs and image models
Users can fine-tune and deploy their own models at no additional cost
Provides speculative decoding for faster inference
Provides Multi-LoRA technology for scalable inference
Fireworks AI has announced its Series D and reports $1B ARR
Practical capabilities
8 documented areasWhat it helps with
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Real-time visual intelligence and computer use agents leveraging VLMs
Bulk evaluations, data generation, augmentation, and ETL pipelines
Custom model fine-tuning with supervised, preference, VLM SFT, or reinforcement fine tunin
Image generation including text-to-image, Image-to-Image, and ControlNet workflows
High-volume AI evaluations via Azure Foundry endpoints
Evaluations, data generation, data augmentation, ETL pipelines, and daily bulk processing
Computer use agents leverage VLMs to autonomously navigate and interact with software inte
Fireworks AI fine-tuning reduced production AI latency from about 2 seconds to 350 millise
Availability
Source checkedWhere it runs and where to get it
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
Cost / license
Serverless pricing is pay per token with high rate limits and postpaid billing; new users · On-Demand Deployments are billed per GPU second, with no extra charges for start-up times · Cached input tokens are priced at 50% and batch inference at 50% of serverless pricing for · 50% lower cost compared to typical Serverless (synchronous) API pricingSupports state-of-the-art, open-source LLMs and image models · Supports open models (with optional opt-in for data retention) · Supports state-of-the-art open-source LLMs and image models
Platforms
Website at https://fireworks.ai
Adoption notes
DeploymentServerless Inference offers per token pricing with zero setup and no cold starts · Models can be deployed in seconds via a serverless infrastructure. · Frontier-lab Training Infrastructure is available as a Managed Service for GLM 5.2 · Dedicated deployments let users run models on private GPUs and pay based on GPU usage time
LicenseSupports state-of-the-art, open-source LLMs and image models · Supports open models (with optional opt-in for data retention) · Supports state-of-the-art open-source LLMs and image models
Model supportSegmind Stable Diffusion 1B (SSD-1B), described as one of the fastest diffusion-based text · SDXL model with Image-to-Image generation and ControlNet support · Llama 4 Scout & Maverick added to vision model platform · InternVL3 added to vision model platform · MiniMax M3 with long context and native multimodality at 1/20th the price
Data controlNo AI Training on Your Data without explicit opt-in · DeepSeek models hosted securely in the US and EU with zero data retention by default
Learning curveIntermediate
Primary use casesReal-time visual intelligence and computer use agents leveraging VLMs, Bulk evaluations, data generation, augmentation, and ETL pipelines, Custom model fine-tuning with supervised, preference, VLM SFT, or reinforcement fine tunin, Image generation including text-to-image, Image-to-Image, and ControlNet workflows, High-volume AI evaluations via Azure Foundry endpoints, Evaluations, data generation, data augmentation, ETL pipelines, and daily bulk processing, Computer use agents leverage VLMs to autonomously navigate and interact with software inte, Fireworks AI fine-tuning reduced production AI latency from about 2 seconds to 350 millise, Code Assistance, Conversational AI, Agentic Systems, Search, Multimodal, and Enterprise RA
What to verify before adopting
- Dataset size must be under 500 MB per batch job, with no upper limit on the number of requ
Evolution and major updates
5 dated updatesFireworks AI timeline
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
Latest first · exact dates
Showing the newest updates and meaningful milestones. Open an entry for its summary and source.
Fireworks Training Preview Launched
Open detailsFireworks announced a training preview that lets customers transform open models into specialized intelligence using their own data.
View source [16]Fireworks Training Preview Announced
Open detailsFireworks announced a preview of its training platform, enabling customers to own and customize their AI models.
View source [16]Fireworks Training Preview released
Open detailsFireworks launched a preview of its training product, enabling companies to transform open models into specialized intelligence on their own data.
View source [16]Fireworks launches on Microsoft Foundry
Open detailsFireworks integrated its inference platform with Microsoft Foundry, bringing best-in-class open model inference to Azure customers.
View source [16]Fireworks Acquires Hathora
Open detailsFireworks acquired Hathora to accelerate global compute orchestration capabilities.
View source [16]
Citation ledger
18 unique pagesRecorded sources
Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
- 1fireworks.ai 8 facts · 2 answers · Official site
- 2fireworks.ai/blog/batch-api 5 facts · 4 answers · Documentation
- 3fireworks.ai/blog/new-in-fireworks-image-to-image-and-controlnet-support-for-ssd-1b-and-sdxl 6 facts · 3 answers · Official site
- 4fireworks.ai/blog/vision-model-platform-updates 6 facts · 2 answers · Official site
- 5fireworks.ai/blog 6 facts · 1 answer · Official site
- 6fireworks.ai/blog/claude-code-pricing 5 facts · 2 answers · Pricing
- 7fireworks.ai/blog/response-api 6 facts · 1 answer · Documentation
- 8fireworks.ai/blog/Story-Notion 6 facts · 1 answer · Official site
- 9fireworks.ai/models 6 facts · 1 answer · Official site
- 10fireworks.ai/pricing 6 facts · 1 answer · Pricing
- 11fireworks.ai/blog/fireworks-ai-developer-cloud 6 facts · Official site
- 12fireworks.ai/blog/sd3-api-powered-by-fireworks 6 facts · Documentation
- 13fireworks.ai/blog/spring-update-faster-models-dedicated-deployments-postpaid-pricing 6 facts · Pricing
- 14fireworks.ai/customers 6 facts · Official site
- 15fireworks.ai/privacy-policy 5 facts · Security
- 16fireworks.ai/blog/series-d-announcement 5 milestones
- 17fireworks.ai/platform/developer-toolkit 4 facts · Official site
- 18fireworks.ai/blog/best-llm-api-providers 3 facts · Documentation
Research status95 substantive facts · 17 source pages · quality score 100/100