Fireworks AI

What is Fireworks AI?

Fireworks AI hosts open-source and open-weight language, vision and embedding models behind an API and lets teams fine-tune them. Serverless inference is billed per token and is compatible with OpenAI and Anthropic client formats, so moving over mostly means swapping a URL. Standard, Priority and Fast serving modes trade price for speed, and cached input tokens cost less. For heavier workloads you can rent on-demand GPU deployments by the second, or reserve capacity with higher quotas and bring-your-own-cloud options. Managed Training supports supervised, preference and reinforcement fine-tuning, and a Serverless Training API handles LoRA runs with no idle cost. Billing is prepaid credits with optional auto reload and spend limits. Text models start around $0.05 per 1M input tokens, embeddings from $0.008 per 1M, and H100 GPUs cost $8.00 per hour.

Use cases

• Call open models through an API
• Fine-tune a model on your data
• Deploy a dedicated GPU endpoint
• Generate embeddings for search
• Run batch inference at lower cost
• Serve custom LoRA adapters
• Migrate from an OpenAI-style API

Who it's for

• AI engineers
• Startups
• ML researchers
• Enterprise platform teams

Why use Fireworks AI

• Lets you try new open models soon after release without managing GPUs.
• Keeps migration simple, since the serverless API follows OpenAI and Anthropic formats.
• Charges per token with no commitment, then lets you move to GPU deployments or reserved capacity at higher volume.
• Serves your fine-tuned model at the same price as the base model.
• Offers caching and batch discounts to reduce cost on repeated or non-urgent requests.

Fireworks AI features

• Serverless per-token inference with no cold starts
• OpenAI- and Anthropic-compatible APIs
• Standard, Priority and Fast serving modes
• Prompt caching with lower cached-token rates
• Batch inference at 50% of serverless pricing
• On-demand GPU deployments billed per second
• Managed Training with SFT, DPO and reinforcement fine-tuning
• Serverless Training API for LoRA runs
• Embedding models
• Reserved capacity with bring-your-own-cloud support
• SOC 2 Type 2, ISO 27001, ISO 27701 and ISO 42001

Fireworks AI integrations

• Claude Code
• ChatGPT
• Codex
• Cursor
• Azure Foundry
• NVIDIA

How to get started with Fireworks AI

1. Add a payment method and buy credits, then optionally enable auto reload or a monthly spend cap.
2. Call a serverless model with one request, choosing Standard, Priority or Fast mode.
3. Fine-tune with Managed Training or the Training API using your data or evals.
4. Deploy the checkpoint and serve it on the same stack, on demand or on reserved capacity.
5. Contact sales for enterprise deployments with higher limits.

Fireworks AI limitations

• Billing is prepaid credits that are deducted as you use them
• Region-restricted deployments carry a 1.5x price premium
• Priority serving is not available for every model
• Some features such as the Serverless Training API are in private preview

Fireworks AI pricing

Fireworks AI has no free plan. Plans start at $0.01 usage-based, according to the official pricing page on 24 Oct 2026.

Fireworks AI plans

• Serverless Inference

  From $0.10 per 1M tokens

  Prepaid credits, usage-based

  • Billed on input, cached and output tokens
  • Standard, Priority and Fast modes
  • Batch inference at 50% off
  • No cold starts

• Embeddings

  From $0.008 per 1M tokens

  Input tokens only

  • Up to 150M parameter models
  • Qwen3 8B at $0.10 per 1M

• Managed Training

  From $0.50 per 1M tokens

  Per 1M training tokens

  • LoRA SFT for models up to 16B
  • SFT and DPO options
  • Fine-tuned models served at base-model price

• On Demand Deployments

  From $8.00 per GPU hour

  Billed per GPU second

  • H100 80 GB at $8.00 per hour
  • B200 180 GB at $13.00 per hour
  • No extra charge for start-up time
  • Higher rate limits

• Reserved Capacity

  Custom

  • Guaranteed capacity
  • Higher quotas
  • Bring-your-own-cloud support

Plans as listed on the official pricing page on 24 Oct 2026. Prices can change and may vary by country or exclude tax. Check the official pricing page.

Fireworks AI alternatives

The main Fireworks AI alternatives listed on Debuly are Together AI, Groq, Baseten, Replicate, DeepInfra and OpenRouter. The table compares pricing and platforms; order is A–Z.

1. 

   Baseten

   Inference platform for deploying open-source, custom and fine-tuned AI models, with per-token Model APIs and dedicated GPUs.

   Developer platforms
   PaidVisit Baseten (official site)
2. 

   DeepInfra

   AI inference cloud with an OpenAI-compatible API for 100+ open models, plus private GPU deployments, GPU rental and hosted agents.

   Developer platformsCodingAutomation and agents
   PaidVisit DeepInfra (official site)
3. 

   Groq

   GroqCloud inference API that serves open models such as GPT-OSS and Whisper on Groq's LPU hardware, billed per token.

   Developer platformsAudio and voiceCoding
   FreemiumVisit Groq (official site)
4. 

   OpenRouter

   Unified OpenAI-compatible API for 500+ AI models with provider fallbacks, pay-as-you-go credits and a free tier

   Developer platformsChat assistantsAutomation and agents
   FreemiumVisit OpenRouter (official site)
5. 

   Replicate

   Cloud API for running, fine-tuning and deploying open-source and proprietary AI models, billed by compute time or by output.

   Developer platformsImage generationVideo
   PaidVisit Replicate (official site)
6. 

   Together AI

   AI cloud for running open-source models through serverless and batch inference, dedicated endpoints, GPU clusters and fine-tuning.

   Developer platformsCoding
   PaidVisit Together AI (official site)

Fireworks AI FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from fireworks.ai · fetched 23 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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