Fireworks AI

What Fireworks AI does

Fireworks AI provides a platform for building, tuning, and scaling AI on open models, offering state-of-the-art training and inference. It processes over 40 trillion tokens per day and provides globally distributed cloud infrastructure optimized for various use cases. The platform supports a full spectrum of model training methods, from guided runs to custom logic, and offers optimized inference with serverless, on-demand, and reserved deployment options. Fireworks AI also features a model library with instant access to popular open-source models, optimized for cost, speed, and quality. The company emphasizes owning your model and future by transforming open models into specialized intelligence, with a focus on speed, lower latency, and higher concurrency compared to closed models.

Products

• TrainingOffers a full spectrum of ways to train models, from guided paths and configuration-led training to custom training logic on Fireworks' GPUs. Every checkpoint deploys to production in seconds.
• InferenceServes the latest open models or custom-trained versions with an engine optimized for industry-leading throughput and latency. Options include Serverless (pay per token), On-Demand (dedicated deployments), and Reserved (guaranteed capacity, newest hardware).
• Model LibraryProvides instant access to popular open-source models, optimized for cost, speed, and quality, with a single line of code. Supports text, vision, audio, and embeddings models.
• Distributed Inference EngineCustomizes model quality, speed, and costs using FireOptimizer, adaptive speculative decoding, custom quantization, and dynamic workload handling. Includes Reinforcement Learning, Supervised Fine Tuning, and Multi-LoRA capabilities.
• Code AssistanceAccelerates developer output with context-aware code generation, inline fixes, and real-time autocomplete. Fine-tunes models on internal codebases for idiomatic, architecture-aware output with low-latency performance and scalable infrastructure.
• Conversational AIDeploys fine-tuned models for reasoning, research, and writing to accelerate insights, maintain context, and help teams make smarter decisions. Offers deep research automation, enterprise AI assistants, and fast, scalable reasoning.
• Virtual Cloud InfrastructureProvides best-in-class infrastructure delivered globally across 18+ regions and 8 providers. Handles bare-metal GPU deployments, offers massive scalability (5 trillion tokens/day, 100,000+ requests/sec), and intelligent scheduling for peak AI performance.
• Voice Agent PlatformOffers sub-500ms responses for natural conversations by running all voice agent components in one co-located, streaming deployment. Provides configurable quality with state-of-the-art transcription and voice models, and an integrated platform for building and scaling voice agents.
• Agentic SystemsEnables tool-using, voice-enabled agents with low-latency function calls to streamline workflows and boost operational efficiency. Features fast, reliable tool use, multi-function and nested workflows, voice-to-action pipelines, and enterprise governance.
• Enterprise AI Search & Knowledge UnderstandingProvides real-time query understanding and long-context summarization to surface critical information, reduce manual work, and power enterprise-scale workflows. Offers fast, accurate query understanding, domain & enterprise-tuned models, and structured summaries at scale.
• Developer ToolkitFacilitates rapid deployment, fine-tuning, and tool calling. Allows experimentation with serverless, scaling to production with on-demand, evaluating 100x faster with Multi-LoRA, and building powerful agents with tool use and memory. Leverages thousands of models across multiple modalities.

Key capabilities

• State-of-the-art training and inference
• Globally distributed cloud infrastructure
• Optimized for specific use cases
• Processes 40T+ tokens per day
• 15x faster speed, 4x lower latency, 4x more concurrency than closed models
• Serverless, On-Demand, and Reserved inference options
• Model library with popular OSS models
• FireOptimizer for model quality, speed, and cost optimization
• Reinforcement Learning for model behavior improvement
• Supervised Fine Tuning with own data
• Multi-LoRA for serving personalized models at scale
• Context-aware code generation
• Inline fixes and refactors
• Real-time autocomplete
• Deep code understanding
• Low-latency performance (sub-100ms response times)
• Scalable GPU autoscaling and batching
• Unified platform for research, reasoning, and writing
• Deep research automation
• Enterprise AI assistants
• Fast, scalable reasoning (sub-2s latency)
• Latest hardware (NVIDIA B200s, AMD MI300X)
• Intelligent scheduling for peak AI performance
• Sub-500ms responses for voice agents
• Configurable quality for voice agents
• Integrated platform for voice agent building
• Fast, reliable tool use for agents
• Multi-function & nested workflows for agents
• Voice-to-action pipelines
• Enterprise governance for agents (audit trails, monitoring, controls)
• Real-time query understanding
• Long-context summarization
• Domain & enterprise-tuned models
• Structured summaries at scale
• Batch Inference
• Function Calling
• Structured Outputs
• Vision Models
• Embeddings & Reranking
• SOC 2, HIPAA compliance

Use cases

• Building, tuning, and scaling AI on open models
• Transforming open models into specialized intelligence
• Deploying production-ready AI
• High-volume evaluations through a single Azure endpoint
• Rolling out recommended model options with reliability
• Real-time streaming and high-concurrency handling for code edits
• Reducing latency for AI features in all-in-one workspace platforms
• Powering real-time, context-aware guidance for contact center agents
• Delivering tailored, lightning-fast proposals for freelancers
• Hosting open source models like SDXL, Llama, and Mistral
• Fine-tuning, AI-powered code search, and deep code context for coding assistants
• Automating workflows with domain-specific assistants
• Accelerating research and content generation
• Ensuring outputs align with brand voice, tone, and style
• Delivering low-latency, high-throughput AI at enterprise scale
• Optimizing cost and performance with scalable GPU inference
• Accelerating time-to-insight and smarter decision-making
• Freeing teams from repetitive research, writing, and analysis tasks
• Scaling AI adoption enterprise-wide
• Building agents that take action and align with tools/APIs
• Meeting SLAs with high-concurrency, low-latency inference
• Controlling cost with GPU autoscaling and predictable usage
• Automating complex workflows and chaining decisions across systems
• Converting speech into structured, real-time actions
• Powering real-time agents for summarizing meetings, drafting next steps, and automating workflows
• Real-time query understanding and long-context summarization for faster insights
• Parsing, classifying, and summarizing data in real time for next-step actions
• Powering real-time, domain-specific guidance across support teams
• Prototyping and evaluating models rapidly
• Building powerful agents with tool use and memory
• Creating rich, multimedia experiences (image understanding/generation, speech transcription, voice agents)

AI approach

Fireworks AI provides a platform for building, tuning, and scaling AI on open models, offering optimized inference and fine-tuning capabilities. They focus on speed, cost-efficiency, and quality, supporting various modalities and offering tools like FireOptimizer for model customization and Multi-LoRA for serving personalized models.

Tech named: LLMs, Generative AI, inference engine, speculative decoding, custom quantization, dynamic workload handling, Reinforcement Learning, Supervised Fine Tuning, Multi-LoRA, FireOptimizer, GPU autoscaling, batching, function calling, structured outputs, embeddings, reranking, batch inference, proprietary inference engine, speculative streaming

Industries served

• Software Development
• Contact Centers
• Freelance Marketplaces
• Workspace Platforms

What it says sets it apart

• From the creators of PyTorch
• Processes 40T+ tokens per day
• 15x faster speed, 4x lower latency, 4x more concurrency than closed models
• Optimized at every layer for industry-leading throughput and latency
• FireOptimizer tailors inference to exact needs
• Multi-LoRA allows deploying hundreds of fine-tuned models without added infra or costs
• Context-aware, streaming code assistance
• Sub-100ms response times for code assistance
• GPU autoscaling and batching for cost-efficient scalability
• Unified platform for research, reasoning, and writing
• Sub-2s latency for conversational AI
• 50% higher GPU throughput for conversational AI
• Proven to launch and scale seamlessly at viral demand (1.8M+ users in 24 hours)
• Latest hardware (NVIDIA B200s, AMD MI300X)
• Intelligent scheduling for peak AI performance
• Sub-500ms responses for voice agents
• Integrated platform for voice agent building
• Fast, reliable tool use for agentic systems
• Enterprise governance for agentic systems
• 90%+ domain coverage with fine-tuned models for enterprise search
• 100x cost reduction for enterprise search using Multi-LoRA
• 2.6x faster responses for enterprise search
• 30-50% fewer repetitive queries for enterprise search
• Drop-in replacement for OpenAI inference and fine-tuning (same API, SFT data format)
• Supports 100+ models across text, vision, audio, image, and embeddings

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.