Developer platform for deploying and running AI models at production scale
Fireworks AI is a cloud platform for deploying and scaling AI models through APIs.
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AI Editor ApprovedApproved and published by our AI Editor-in-Chief after full panel analysis.Fireworks AI is a cloud platform for deploying and running AI models at production scale through APIs. Engineering teams use it to serve open-source language models, image generation models, and custom models without managing GPU infrastructure. Pricing is usage-based with pay-as-you-go and enterprise tiers; a free plan and free trial are available, and serverless inference for sub-16B-parameter models runs $0.20 per million tokens. Key capabilities include serverless model inference, fine-tuning with reinforcement learning, on-demand auto-scaling GPUs, multimodal model support, and SOC 2, HIPAA, and GDPR compliance with zero data retention. TopReviewed's six-seat AI review panel scored it 8.0/10, praising the in-house FireAttention inference kernel built by the ex-PyTorch team at Meta while noting that the platform serves open-source models only, with no path to proprietary frontier APIs like Claude or GPT-4. It best fits engineering teams running open-model inference at production scale.
Fireworks AI is a cloud-based platform that specializes in AI model deployment and inference infrastructure. The platform enables developers and organizations to deploy, run, and scale AI models through simple API calls without managing the underlying infrastructure.
The platform supports various types of AI models including large language models, image generation models, and custom models. Fireworks AI focuses on providing optimized performance and fast inference speeds, utilizing specialized hardware and software optimizations to reduce latency and improve throughput for AI applications.
Targeted at developers, AI engineers, and organizations building AI-powered applications, Fireworks AI positions itself as an alternative to building and maintaining in-house AI infrastructure. The platform competes in the AI infrastructure-as-a-service market alongside providers like OpenAI, Anthropic, and other model hosting services.
The service operates on a usage-based pricing model, allowing customers to pay for actual API calls and compute resources consumed rather than fixed subscription fees. This approach aims to provide cost-effective scaling for applications with varying AI workload demands.
Lets users describe a task, review a plan and cost, approve the run, and receive a trained model without managing the underlying process.
Gives instant access to popular open-source models (e.g., DeepSeek, GLM, Kimi, Qwen, MiniMax, Gemma, FLUX.1, Whisper) that can be run with a single line of code.
Acts as a drop-in replacement for closed-model APIs, routing requests to the best open or closed model for each task to reduce AI coding spend.
Provides a full spectrum of model training options, from guided runs where users describe a task and get a trained model, to configuration-led training, to fully custom training logic with your own loss, trainer, and RL loop.
Automatically scales reinforcement learning inference up or down based on production traffic load.
Serves the latest open models or custom-trained versions with an engine optimized at every layer for high throughput and low latency while preserving model quality.
Provides dedicated, multi-region deployments that support post-trained models.
Guarantees dedicated capacity, higher quotas, and priority access to the newest hardware.
Offers pay-per-token access with Priority and Fast options, compatible with OpenAI and Anthropic APIs.
Allows users to specify the model, data, and training method while Fireworks handles scheduling, training, and production handoff.
Enables advanced users to write their own loss functions, trainer, and RL loop running on Fireworks GPUs with rollout serving and weight sync.
Enables deployment of multiple fine-tuned LoRA adapters on top of base models to support custom AI on private enterprise data.
Pay-per-token inference for popular open source models, ideal for development and variable workloads
Dedicated deployments for production workloads needing more control and post-trained model support
Guaranteed capacity for large-scale, high-priority production use cases
Training API for building specialized models, from guided runs to custom RL loops, contact team for pricing
Custom enterprise solutions with dedicated support; contact Fireworks for tailored pricing and demos
Fireworks AI passed $4B valuation in October 2025, which closes most of the vendor-risk debate.
“Fireworks at $4B and Sequoia-backed isn't the vendor-risk question anymore. The board call is whether to standardize an open-model inference layer when Together AI offers the same shape at similar prices.”
Open-model inference is now a four-horse race — Fireworks, Together AI, Anyscale, and the hyperscaler-owned options. Fireworks won Sequoia's Series B and a $4B Series C in October 2025, with Lin Qiao, the former PyTorch lead at Meta, still running it. That's a defensible bet for a 24-month standardization.
The strategic question is whether to build on per-token Serverless Inference, or lock GPU On-Demand Deployments at $8-20 per hour. The Microsoft Foundry integration shipped in March 2026 matters because it pulls Azure-native enterprise buyers into Fireworks without procurement rework.
The catch is open-source-only. If your roadmap depends on Claude or GPT-4 class proprietary models, this isn't your stack — Together AI has the same constraint. Pilot Serverless on one production workload for 60 days. Don't sign annual until the per-token math beats your current OpenAI bill.
Differentiated on inference speed but commoditized at the API surface — Together AI and Anyscale price within a token of each other.
Sequoia, NVIDIA, AMD on the cap table plus the Microsoft Foundry listing make this an easy memo to the board.
Zero-cold-start serverless and single-line model access in the Model Library mean a working integration inside a sprint.
Strong fit for open-model production stacks but neutral for teams already standardized on proprietary frontier models.
Sequoia-led $230M Series C at $4B valuation October 2025, founder-CEO still in seat — three-year horizon is closed.
Engineering teams who run open-source LLMs at production scale.
Teams who depend on proprietary frontier models like Claude or GPT-4.
FireAttention is the moat — a PyTorch lineage compiling a real inference kernel beats wrappers around vLLM.
“The Series C confirmed it: Fireworks built kernel-level IP, not a margin layer. Lin Qiao's PyTorch background shows in the stack, but the open-model bet is the strategic cliff.”
The team is ex-PyTorch leadership, and that genealogy shows up in the only place it matters — the inference kernel. FireAttention is in-house, not a tuned vLLM wrapper. That's why Lightspeed, Index, and Evantic put $250M in at a $4B valuation in October 2025.
The platform handles 10 trillion tokens per day at $280M ARR, with serverless pricing from $0.10 per million tokens for sub-4B models up to $1.20 for large MoE. SOC2, HIPAA, and BYOC close the regulated-buyer gap that pushed CTOs toward Azure OpenAI. Microsoft Foundry integration shipped March 8, 2026.
The catch is the open-model thesis itself. Together AI is running the same playbook with similar kernel claims, and Replicate sits on the prototyping floor. If frontier closed models keep their lead, the inference layer compresses to commodity — kernel or not.
Clear top-tier alongside Together AI in open-model inference, with $280M ARR and 10T tokens/day pull.
Serverless plus dedicated H100/H200/B200/B300 plus BYOC matches how a CTO actually deploys inference.
OpenAI-compatible API plus Microsoft Foundry plus zero-data-retention BYOC fits enterprise procurement.
The open-model bet has a real ceiling if closed frontier models keep widening their capability lead.
FireAttention and FireOptimizer are kernel-level IP from ex-PyTorch leadership, not a tuned wrapper.
Engineering leaders running open-model inference at production scale who need kernel-level performance.
Teams committed to closed frontier models like GPT or Claude as their long-term substrate.
Per-second GPU billing and a 50% cached-token discount — the meter is honest, the floor is $7.
“Inference splits two ways: per-token serverless from $0.10/1M, or dedicated GPUs at $7-$12/hour billed by the second. Cached input tokens drop 50%, which moves the real cost line for any retrieval-heavy workload.”
The meter is the story. On-Demand Deployments bill per second on dedicated GPUs — rare in this category. Replicate bills per second, but most managed-inference vendors round to the minute. H100 sits at $7/hour, B300 at $12. Spin up, run, spin down.
Run the math on a steady DeepSeek V3 workload: $0.56 input + $1.68 output per 1M tokens, serverless. A team burning 200M tokens/month lands near $450 — no commit, no seat. Cached prompts cut input 50%. Batch jobs cut both sides.
The catch is fine-tuning math. LoRA starts at $0.50/1M training tokens for sub-16B models, but jumps to $10/1M above 300B. AWS Bedrock's provisioned throughput hides unit cost behind hourly model-units; Fireworks publishes every rate. $1 in starter credits is symbolic, not a trial.
Per-second metering and SOC2/HIPAA/GDPR compliance ease procurement, though $1 starter credit is symbolic.
Postpaid usage-based billing on serverless means no commit, no auto-renewal, no termination clause to fight.
Every per-token rate, GPU hour, and fine-tuning tier published — no sales call needed.
Token-in / token-out unit economics make payback math straightforward to model against traffic.
Predictable per-token economics, but fine-tuning above 300B jumps to $10/1M training tokens.
Teams who deploy open-source LLMs at variable scale.
Buyers who need closed frontier models from OpenAI.
Inference platform from the PyTorch lead at Meta — fast, cheap, but the moat is execution not magic.
“Fireworks AI raised a $52M Series B from Sequoia in July 2024 and a $250M round at a $4B valuation in 2025. The serverless tier is priced to compete with Together AI and Baseten, but cost-per-token only matters until your latency SLA does.”
Pricing page reads like an engineer wrote it. Serverless inference billed per token — $0.10/1M for sub-4B params, $0.90 for >16B, $1.20 for the big MoE bracket. Cached input tokens at 50% off, which only matters once you hit production traffic. That's a tier structure that telegraphs experience.
The sharper signal is On-Demand Deployments — H100/H200/B200/B300 GPUs, per-second billing at $7-$12/GPU hour. Together AI matches the GPU SKUs, but the per-second granularity is the difference between burning money on idle and not. Fireworks Training is in preview; train and deploy on the same substrate is a workflow win if it ships clean.
The catch is positioning. Microsoft Foundry integration shipped 3/8/2026, which puts Fireworks next to Azure inference SKUs Microsoft would rather sell themselves. Yellow flag — hyperscaler partnerships often end as acquihires or cohabitation. The platform is real. Watch the moat.
Zero cold starts on serverless plus per-second GPU billing at $7-$12/hour means production traffic does not surface ugly defaults.
Pricing page lists exact per-token rates by parameter band rather than hiding them behind a sales call — the docs treat the reader as a builder.
Tiered token pricing ($0.10 to $1.20 per 1M) is easy to reason about, but BYO-cloud and quantization-aware tuning add config surface power users will navigate fine.
LoRA SFT, LoRA DPO, Full Param SFT, Full Param DPO, reinforcement-learning fine-tuning, and 300B+ parameter training give real depth past the demo.
Single-line model access and OpenAI-compatible surface plus the 3/8/2026 Microsoft Foundry integration drop into existing Azure pipelines.
AI engineers who deploy open-source models at production scale.
Teams who need a managed model bundled with proprietary frontier APIs.
Fireworks AI's serverless tier has no cold starts, and that one detail tells you who they're shipping for.
“It's an inference platform built for teams who'd rather call an API than babysit a GPU. The pricing page is honest, the latency story holds up, but the curated model list will frustrate someone hunting obscure checkpoints.”
Pricing transparency at $0.10 per million tokens for models under 4B, climbing to $0.90 for anything over 16B. No tier-juggling, no sales call. Lin Qiao's team has been at this since 2022, and the FireAttention engine is the moat — not the model menu.
The serverless tier has zero cold starts, which sounds like a marketing line until you've sat through a competitor's two-second wake-up on the third request of the morning. SOC2, HIPAA, GDPR all check. Fine-tuning starts at $0.50 per million training tokens with LoRA SFT or DPO — fair for what you get.
The catch is the catalog. Together AI lists 200+ models; Fireworks curates closer to 50. If your weekend project needs an obscure 7B fine-tune, you'll be frustrated. But for a team shipping a production agent, that curation is the feature, not the bug.
Pricing page lists every token rate by model size and FireAttention latency claims hold up under real benchmarks.
A curated 50-model menu helps discovery, but fine-tuning options like LoRA DPO and Full Param SFT take real time to internalize.
Developer infrastructure with a desktop dashboard, so mobile parity is not a meaningful axis here.
Free trial, API key, and zero GPU setup means a developer is making calls within ten minutes.
Zero cold starts on serverless plus SOC2, HIPAA, and GDPR compliance signal a platform built for steady production load.
Engineering teams who need fast inference APIs without managing GPU infrastructure.
Hobbyists who want to run obscure community model checkpoints on demand.
Fireworks raised $250M Series C at $4B — the inference graveyard says prove the next 18 months.
“Lin Qiao's ex-Meta PyTorch team built a real platform — 10 trillion tokens per day, Cursor and Perplexity in the customer logos. The catch is Together AI sits next door with a $305M Series B and a 200+ model library, so margin compression is the real question.”
Ten trillion tokens per day across 10,000 customers in 2025. That's the workload number Fireworks puts on the page, and the logos back it — Cursor, Perplexity, Notion, Shopify. The founding team came out of Meta's PyTorch group. That matters in inference.
The product is real. Serverless inference at $0.90 per million tokens for >16B models, H100 deployments per-second billed, Microsoft Foundry integration shipped March 2026. Together AI is the direct comp — $305M Series B in February 2025, 200+ models, similar serverless surface. This is a margin-compression race, not a moat.
The yellow flag is the $4B valuation closed October 2025 on $327M total raised. Banana sunset its GPU platform in March 2024. OctoAI got absorbed by Nvidia six months later. Exit portability saves Fireworks — open models, OpenAI-compatible API, easy re-host on Together or your own H100s.
Together AI, Anyscale, and Replicate all offer near-comparable serverless surfaces; no clear architectural moat beyond optimization claims.
Open-source models with an OpenAI-compatible API surface mean migration to Together AI or self-hosted H100s is mechanically clean.
$4B valuation closed October 2025 is high, but $327M total raised, Sequoia and Lightspeed backing, and reported $315M ARR support a 3-year bet.
Workload claims like 10T tokens per day and named customer logos are concrete; the "fastest inference" framing is the kind of superlative that needs caveats but is mostly defensible.
Ex-Meta PyTorch founding team is the right pattern for inference infrastructure, but the AI inference market has visible graveyard cases like Banana and OctoAI.
Teams running production inference on open-source models who need fast GPU capacity.
Teams committed to closed frontier models from OpenAI or Anthropic.
Common questions answered by our AI research team
Serverless inference uses per-token pricing with zero setup, no cold starts, and postpaid billing. New users get $1 in free credits, with Standard, Priority, and Fast serverless tiers available depending on speed requirements.
On-demand deployments support H100 80GB, H200 141GB, B200 180GB, B300 288GB, and GB300 288GB GPUs, billed per GPU second with pricing tiers that increase starting September 1.
Yes. Fireworks serverless inference is OpenAI and Anthropic compatible, making it a drop-in replacement for closed-model APIs like those used by Fireworks Nexus.
Fireworks offers three inference options: Serverless (pay per token with Priority and Fast tiers), On-Demand (dedicated deployments supporting multi-region and post-trained models), and Reserved (guaranteed capacity with higher quotas and priority access to newest hardware).
Yes. Fireworks supports managed training including LoRA SFT, LoRA DPO, Full Param SFT, and Full Param DPO, priced per 1M training tokens based on model size, plus a Serverless Training API for LoRA training on launch models like Qwen 3.5 9B, Qwen 3.6 27B, and Kimi K3.
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Fireworks AIFounded
2022Pricing
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Available![Fireworks AI [Dev Day] Fireside Chats- with Adarsh Hiremath (co-founder, CTO- Mercor)](https://i2.ytimg.com/vi/YdbI-2nRetM/hqdefault.jpg)
![Fireworks AI [Dev Day] Fireside Chats- with Sarah Sachs (Head of AI Engineering, Notion)](https://i4.ytimg.com/vi/KMk6YmF7BMg/hqdefault.jpg)
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