Fireworks.ai
Fireworks.ai is a platform designed to revolutionize the product innovation process through the use of advanced Generative AI technology. T... SITE_META_DESC: Use state-of-the-art, open-source LLMs and image models at blazing fast speed, or fine-tune and deploy your own at no additional cost with Fireworks AI! SITE_BODY_TEXT: DeepSeek V4 Pro is Live → Try it now.
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What is Fireworks.ai?
Fireworks.ai - Fireworks AI is the fastest inference platform for building with open source generative AI models. It provides production-ready inference and fine-tuning with best-in-class speed, cost, and quality. Users can instantly run popular open-source LLMs and image models like DeepSeek, Llama, Qwen, Mistral, Kimi K2, and GLM with a single line of code using an OpenAI-compatible API, or fine-tune and deploy their own models without additional cost.
Key features include serverless inference with per-token pricing and zero cold starts, three serving paths (Standard, Priority for higher reliability, and Fast for 100+ tokens/second throughput), fine-tuning with supervised/preference/reinforcement learning up to 1T+ parameters, on-demand GPU deployments, structured outputs with JSON mode and grammar-constrained decoding, function calling for agentic workflows, vision models for image/document analysis, embeddings and reranking for search, batch inference at 50% pricing, and prompt caching with cached input tokens priced at 50%. The platform supports 100+ models across text, vision, audio, image, and embeddings.
Fireworks AI is designed for AI natives who want day-0 support for latest models with highest quality and lowest cost, as well as enterprises requiring SOC2/HIPAA/GDPR compliance, zero data retention, complete data sovereignty, and the ability to bring their own cloud. It powers use cases including code assistance (IDE copilots, code generation), conversational AI (customer support bots, multilingual chat), agentic systems (multi-step reasoning and planning), search (enterprise assistants, semantic search), multimedia workflows, and enterprise RAG for secure knowledge base retrieval.
Fireworks.ai pricing
Pricing model: Free
Serverless Inference: Per-token pricing with high rate limits and postpaid billing. New users get $1 in free credits. Input tokens billed per 1M tokens; cached input tokens at 50%; batch inference at 50% of serverless pricing. Text/vision models: Qwen 3.7 Plus at $0.40/$0.08/$1.60 (input/cached/output per 1M), OpenAI GPT OSS 20B at $0.07/$0.035/$0.30, MiniMax M2.7 at $0.30/$0.06/$1.20, Kimi K2.5 at $0.60/$0.10/$3.00, DeepSeek V4 Pro at $1.74/$0.145/$3.48. Size-based pricing: under 4B at $0.10/M, 4B-16B at $0.20/M, over 16B at $0.90/M, MoE up to 56B at $0.50/M, MoE 56.1B-176B at $1.20/M. Embeddings: up to 150M at $0.008/M, 150M-350M at $0.016/M, Qwen3 8B at $0.10/M. Fine Tuning: LoRA SFT for models up to 16B at $0.50/M training tokens, 16.1B-80B at $3.00/M, 80B-300B at $6.00/M, over 300B at $10.00/M. Full Param SFT: up to 16B at $1.00/M, 16.1B-80B at $6.00/M, 80B-300B at $12.00/M, over 300B at $20.00/M. Reinforcement Fine Tuning: priced per GPU hour at on-demand deployment prices. On-Demand: H100 80GB at $7/hour, H200 141GB at $7/hour, B200 180GB at $10/hour, B300 288GB at $12/hour. Enterprise deployments offer faster speeds, lower costs, and higher rate limits via contact.
Fireworks.ai pros
- Blazing fast inference with industry-leading throughput and latency
- Per-token serverless pricing with zero setup and no cold starts
- $1 in free credits for new users to start building instantly
- OpenAI-compatible API for drop-in replacement with same SDK
- 100+ supported open-source models including DeepSeek, Llama, Qwen, Mistral, Kimi
- Structured outputs with JSON mode guaranteeing format compliance
- Function calling capabilities on open models like Llama and Mixtral
- Grammar mode for formal grammar constraints beyond simple JSON
- Three serving paths: Standard, Priority for reliability, Fast for 100+ tokens/second
- Fine-tuning up to 1T+ parameters with LoRA and full-parameter options
- Reinforcement fine-tuning with built-in grader functions (GRPO, DAPO, GPO)
- On-demand GPU deployments paying per GPU second with no startup charges
- H100, H200, B200, B300 GPU options at competitive hourly rates
- Cached input tokens priced at 50% for prompt caching efficiency
- Batch inference at 50% of serverless pricing for input and output tokens
- Enterprise compliance: SOC2, HIPAA, GDPR with zero data retention
- Multi-LoRA capabilities for deploying custom fine-tuned models
- Prompt caching with automatic caching for repeated context
- Vision-language models for image and document analysis
- Embeddings and reranking models for search and retrieval
Fireworks.ai cons
- Priority tier not available on all models (limited selection)
- Fast serving path only available for select models (Kimi K2.6, GLM 5.1)
- Enterprise deployments require contacting sales (not self-service)
- Reinforcement fine-tuning priced per GPU hour (can be expensive at scale)
- On-demand GPU pricing starts at $7/hour for H100/H200 (higher than some competitors)
- $1 free credits are one-time only (not recurring free tier)
- Some premium models like DeepSeek V4 Pro and Kimi K2 have higher per-token costs
- Training with reasoning traces increases total tuned tokens (unrolled conversations)
- Image fine-tuning billed per 1M tokens (additional cost beyond text)
- MoE models in 56B-176B range priced at $1.20/M tokens (higher than dense models)
Frequently asked questions about Fireworks.ai
What is Fireworks AI and what does it do?
Fireworks AI is the fastest platform for building with open source AI models. It provides production-ready inference and fine-tuning with best-in-class speed, cost, and quality. Users can instantly run popular open-source LLMs and image models like DeepSeek, Llama, Qwen, Mistral, Kimi, and GLM with a single line of code using an OpenAI-compatible API, or fine-tune and deploy their own models without additional cost. The platform supports 100+ models across text, vision, audio, image, and embeddings.
What are the three serving paths and which should I use?
Fireworks Serverless offers three serving paths: Standard (default, no service_tier parameter needed), Priority tier (for workloads requiring higher reliability during peak traffic, less likely to get 503 server overloaded errors, at higher price point), and Fast (for workloads requiring higher speeds like interactive applications, aiming for 100+ tokens per second generated throughput). Priority is available on select models only. Fast is available for Kimi K2.6 Fast and GLM 5.1 Fast. Standard is best for most use cases, Priority for mission-critical workloads during peak traffic, and Fast for real-time interactive applications.
How does fine-tuning work on Fireworks AI?
Fireworks offers three fine-tuning approaches: Fireworks Agent (describe what you want in plain English, Agent picks base model, prepares data, sweeps hyperparameters, evaluates, trains, and deploys), Managed Fine-Tuning (give Fireworks your data and configuration, platform handles scheduling/training/checkpointing/model output without custom code), and Training API (write custom Python training loops with full control over loss function, optimizer, checkpointing). Fine-tuning methods include Supervised Fine-Tuning (SFT), Preference Fine-Tuning (DPO), and Reinforcement Fine-Tuning (RFT) with LoRA or full-parameter options for models up to 1T+ parameters.
What is structured output and JSON mode?
Structured outputs on Fireworks enforce JSON schemas and grammar-constrained decoding to guarantee LLM output follows your desired format. JSON mode provides structure to any LLM by specifying a JSON schema, ensuring reliable output that can调用 and pipe to other models, APIs, and components. Grammar mode lets you specify a formal grammar (BNF) constraining output beyond simple JSON, useful for generating code in specific languages or domain-specific syntax with 100% format compliance. These are particularly useful for agentic workflows requiring reliable structured output for function calling.
What GPU options are available for on-demand deployments?
On-demand deployments offer four GPU types: H100 80 GB GPU at $7.00/hour, H200 141 GB GPU at $7.00/hour, B200 180 GB GPU at $10.00/hour, and B300 288 GB GPU at $12.00/hour. You pay per GPU second with no extra charges for start-up times. On-demand provides faster speeds, higher rate limits, and lower costs at scale compared to serverless. Reinforcement fine-tuning jobs are also priced per GPU hour at the same on-demand deployment prices.
How much does fine-tuning cost?
Fine-tuning is priced per 1M training tokens. For LoRA SFT: models up to 16B at $0.50, 16.1B-80B at $3.00, 80B-300B at $6.00, over 300B at $10.00. For LoRA DPO: up to 16B at $1.00, 16.1B-80B at $6.00, 80B-300B at $12.00, over 300B at $20.00. For Full Param SFT: up to 16B at $1.00, 16.1B-80B at $6.00, 80B-300B at $12.00, over 300B at $20.00. For Full Param DPO: up to 16B at $2.00, 16.1B-80B at $12.00, 80B-300B at $24.00, over 300B at $40.00. Fine-tuned models are served for the same price as base models. Training tokens estimated as dataset tokens × epochs.
Is Fireworks AI compliant with enterprise security requirements?
Yes, Fireworks AI is SOC2, HIPAA, and GDPR compliant for enterprise workloads. The platform provides enterprise-grade security and reliability across mission-critical workloads with zero data retention and complete data sovereignty. Enterprises can bring their own cloud or run on Fireworks' cloud. Fireworks AI on Microsoft Foundry brings best-in-class open model inference to Azure within the Azure ecosystem. Security and compliance details including audit reports are available at trust.fireworks.ai.
What use cases does Fireworks support?
Fireworks powers everything from rapid prototyping to mission-critical workloads across multiple use cases: Code Assistance (IDE copilots, code generation, debugging agents), Conversational AI (customer support bots, internal helpdesk assistants, multilingual chat), Agentic Systems (multi-step reasoning, planning, execution pipelines), Search (enterprise assistants, summarization, semantic search, personalized recommendations), Multimedia (text, vision, speech in real-time workflows), and Enterprise RAG (secure, scalable retrieval for knowledge bases and documents). Customer examples include Cursor's Composer 2, Vercel's v0, Notion's AI features, Genspark, Quora, Sourcegraph's Cody, and UiPath's Autopilot.
How does batch inference work and what are the benefits?
Batch inference allows running async inference jobs at scale, faster and cheaper than standard serverless. Batch inference is priced at 50% of serverless pricing for both input and output tokens. This is useful for high-volume evaluations and processing large batches of requests where real-time response isn't required. You can run repeatable, high-volume evaluations through a single endpoint, helping teams move faster from deployment to informed model decisions. Batch inference is particularly beneficial for evaluation workflows, data processing pipelines, and any scenario where you can queue requests rather than requiring immediate responses.