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Inference Hosting

Fireworks AI

Fireworks AI serves open models through Serverless Inference, multi-tenant inference on Fireworks-managed infrastructure billed per token, and through on-demand deployments on dedicated GPUs billed per GPU second. It offers supervised and reinforcement fine-tuning and serves fine-tuned models for the same price as base models.

The 30-Second Take

Pick Fireworks AI if you serve open models per token and want Zero Data Retention by default.

You skip running GPUs, and steady traffic can move to dedicated deployments. Cursor and Sourcegraph built features on Fireworks.

Best fit
Teams that serve open models such as GLM, Kimi and DeepSeek through an OpenAI-compatible API and want Zero Data Retention by default.
Main constraint
Serverless shares GPUs, so high traffic brings 429s, 503s and latency variability.
First move
Turn on Auto Reload so a zero balance cannot pause usage.

Why

Reserve capacity before a new model takes production traffic. GitLab redirected Codestral traffic when Fireworks could not scale a new on-demand deployment.

Practitioner sentiment, all practitioners−17 Mixed

42 practitioners, 76 sources reviewed, Updated Nov 1, 2026How this is measured

Read the GILD Review
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Operating stack
AI Application Platform

Comparisons

How does Fireworks AI compare with the alternatives?

  1. Fireworks AI vs Groq

    • Inference Hosting

    Both list gpt-oss-120b at US$0.15 input and US$0.60 output per 1M tokens, so GILD prices 100M input and 20M output tokens a month at US$27 on either. Fireworks lists a US$0.015 cached-input rate that Groq's models page does not, and keeps no data for open models by default, and Groq's batch runs at 50% lower cost within 24 hours to 7 days.

  2. Both list gpt-oss-120b at US$0.15 input and US$0.60 output per 1M tokens. Fireworks has Zero Data Retention by default and bills dedicated GPUs per GPU second, while Together AI supports zero data retention at the organization level, off by default, and bills dedicated inference per minute while a deployment runs.

Operator Insights

What are teams learning from building on Fireworks AI and running it in production?

Buying and deployment

What a team building on it should verify before production traffic grows.

Sourced from Fireworks AI documentation

How you buy it

Per token or GPU second

Serverless draws on prepaid credits, and usage pauses at a zero balance unless Auto Reload is on.

Source 2, Fireworks AI, Billing FAQ: Billing FAQSource 1, Fireworks AI, Pricing: PricingSource 8, Fireworks AI, Cost FAQ: Cost FAQ

Details

Fireworks Serverless is pay per token with pre-paid, usage-based billing. On demand deployments are pay per GPU second. Fireworks bills pre-paid credits, and if your balance reaches zero and Auto Reload is not enabled, usage pauses until you add credits, so usage cannot exceed the balance. Contracted customers may have the option to move to post-paid billing.

Cost drivers

US$8.00 per H100 hour

Batch runs at 50% of serverless prices, and region-restricted deployments cost 1.5x.

Source 11, docs.fireworks.ai, Serverless pricing: Serverless pricingSource 1, Fireworks AI, Pricing: PricingSource 12, Fireworks AI, Pricing table: Pricing tableSource 13, Fireworks AI, Batch: BatchSource 14, Fireworks AI, Introduction: Introduction

Details

Serverless text and vision prices are listed per 1 million tokens in US dollars, for input, cached input, and output. Batch inference is billed at 50% of serverless pricing on both input and output. On demand H100 80 GB GPUs are listed at US$8.00 per hour. Region-restricted deployments are priced at a 1.5x premium. Contact Fireworks sales to get started.

Where it runs

Shared or reserved GPUs

US-only Serverless serves inference from the US on its listed US model IDs.

Source 16, docs.fireworks.ai, US-only Serverless: US-only ServerlessSource 15, Fireworks AI, Latency FAQ: Latency FAQSource 17, Fireworks AI, Deployments: Deployments

Details

With serverless, users are sharing a pool of GPUs with models pre-provisioned. With on-demand deployments, users are reserving GPUs. US-only Serverless serves inference exclusively from the US. To use it, call https://us.api.fireworks.ai and pass one of the US model IDs as model.

Signals

What is the current evidence saying about Fireworks AI?

A current read of what developers and teams report publicly about running open models on Fireworks AI, with each finding linked to its sources.

Whose sources count

Practitioner sentiment, all practitioners

−17 Mixed net

of 47 sources that take a side, each author counted once.

How this figure is made

Teams and leaders have 16 accounts; GILD prints their sentiment from 20.

Current sentiment

Positive, mixed and negative sources

The score counts them as 11 positive, 13 mixed and 18 negative votes, one per author.

Across 47 counted sources the read leans critical: 14 positive, 13 mixed and 20 negative. Speed draws praise in 8 sources and low per-token bills in 8, while price rises, cached-token costs and zero-retention premiums are raised in 9 and API compatibility gaps in 6.

Run a pilot on your own prompts at expected concurrency to measure time to first token, output speed, error rate and cost per million tokens.

Evidence field. All 76 sources, one mark each.

Arrange by

Each mark is one source: positive, mixed, negative, no opinion stated, or documentation (not counted).

By sentiment, All practitioners. Every source GILD read, grouped as the field draws it.
GroupSourcesPositiveMixedNegativeNo opinion stated and not counted in the scoreDocumentation, checked and not counted
Positive14140000
Mixed13013000
Negative20002000
Documentation29000029
Every source GILD read, by the quarter it was published, as the field's time axis counts it. Documentation is dated by the day it was checked.
QuarterSources
Q1 20252
Q2 20251
Q3 20253
Q4 20250
Q1 20264
Q2 20268
Q3 202621
Q4 20268
Documentation, checked29

By sentiment

29 documentation pages check facts and are not counted.

Hover a mark for a quick look, click it or press Enter to open its source.Tap a mark to open its source, then step to its neighbors with Previous and Next.

What practitioners praise

Top praise of 3. Each mark is a source on it; filled marks praise it.

  1. Low Per-Token Bills8 of 10 sources on it praise it

Most common concerns

Top concern of 3. Each mark is a source on it; filled marks raise it.

  1. Price Rises and Cached Tokens9 of 12 sources on it raise it as a concern
All 76 sources behind these findings
  1. Serverless latency FAQFireworks AI (Official), Product documentation, checked Nov 1, 2026
  2. Anthropic compatibilityFireworks AI (Official), Product documentation, checked Nov 1, 2026
  3. IntroductionFireworks AI (Official), Product documentation, checked Nov 1, 2026
  4. Tool callingFireworks AI (Official), Product documentation, checked Nov 1, 2026
  5. EmbeddingsFireworks AI (Official), Product documentation, checked Nov 1, 2026
  6. Batch inferenceFireworks AI (Official), Product documentation, checked Nov 1, 2026
  7. Vision modelsFireworks AI (Official), Product documentation, checked Nov 1, 2026
  8. Nexus AutoFireworks AI (Official), Product documentation, checked Nov 1, 2026
  9. QuickstartFireworks AI (Official), Product documentation, checked Nov 1, 2026
  10. OpenAI compatibilityFireworks AI (Official), Product documentation, checked Nov 1, 2026
  11. Agent frameworksFireworks AI (Official), Product documentation, checked Nov 1, 2026
  12. LLM gatewaysFireworks AI (Official), Product documentation, checked Nov 1, 2026
  13. Microsoft FoundryFireworks AI (Official), Product documentation, checked Nov 1, 2026
  14. RegionsFireworks AI (Official), Product documentation, checked Nov 1, 2026
  15. Data residencyFireworks AI (Official), Product documentation, checked Nov 1, 2026
  16. Serverless overviewFireworks AI (Official), Product documentation, checked Nov 1, 2026
  17. Serverless pricing (text)Fireworks AI (Official), Product documentation, checked Nov 1, 2026
  18. Cost FAQFireworks AI (Official), Product documentation, checked Nov 1, 2026
  19. Billing and credits FAQFireworks AI (Official), Product documentation, checked Nov 1, 2026
  20. Serverless rate limits (text)Fireworks AI (Official), Product documentation, checked Nov 1, 2026
  21. Account quotasFireworks AI (Official), Product documentation, checked Nov 1, 2026
  22. Inference error codesFireworks AI (Official), Product documentation, checked Nov 1, 2026
  23. SSO and SCIMFireworks AI (Official), Product documentation, checked Nov 1, 2026
  24. Data handlingFireworks AI (Official), Product documentation, checked Nov 1, 2026
  25. Data securityFireworks AI (Official), Product documentation, checked Nov 1, 2026
  26. Audit logsFireworks AI (Official), Product documentation, checked Nov 1, 2026
  27. On-demand deploymentsFireworks AI (Official), Product documentation, checked Nov 1, 2026
  28. PricingFireworks AI (Official), Product documentation, checked Nov 1, 2026
  29. Secure trainingFireworks AI (Official), Product documentation, checked Nov 1, 2026
  30. Hacker News comment by switchbak: Comparing Fireworks with Baseten for open-model speedswitchbak, Hacker News commenter (self-described: Comparing Fireworks with Baseten for open-model speed), Hacker News comment, published Oct 26, 2026
  31. Hacker News comment by verdverm: A team that selected Fireworks as its open-model vendorverdverm, Hacker News commenter (self-described: A team that selected Fireworks as its open-model vendor), Hacker News comment, published Oct 19, 2026
  32. Hacker News comment by eknkcEkin Koc (eknkc), kovan studio (self-described: Trying a new open-weight release on launch day), Hacker News comment, published Oct 15, 2026
  33. NativeOutput fails on Fireworks models despite Fireworks supporting response_formatTim Becker, Researcher at Theori (self-described: Typed agents on Pydantic AI with Fireworks-hosted models), GitHub issue, published Oct 12, 2026
  34. Hacker News comment by kelnoskelnos, Core developer at Xfce (self-described: Individual coding use, after a US$100 a month Claude plan), Hacker News comment, published Oct 9, 2026
  35. Hacker News comment by switchbak: Trying Fireworks for speed against DeepSeek's own APIswitchbak, Hacker News commenter (self-described: Trying Fireworks for speed against DeepSeek's own API), Hacker News comment, published Oct 9, 2026
  36. Self-hosting Qwen3.8-Flash-Next (or a smaller alternative) for a heavy multi-agent Hermes setup on $10k of AWS credits, looking for options I've missedar27111994, Reddit poster (self-described: A multi-agent orchestrator moving to self-hosting paid with AWS Activate credits), Reddit post, published Oct 4, 2026
  37. Hacker News comment by verdverm: A development team moving from Claude to open modelsverdverm, Hacker News commenter (self-described: A development team moving from Claude to open models), Hacker News comment, published Oct 2, 2026
  38. Hacker News comment by rapindrapind, Hacker News commenter (self-described: Agentic coding on a zero-data-retention host, with a 98 to 99% cache rate), Hacker News comment, published Sep 29, 2026
  39. Hacker News comment by vardalabvardalab, Hacker News commenter (self-described: An agent setup with a stronger model as an advisor over an open worker model), Hacker News comment, published Sep 24, 2026
  40. Hacker News comment by WinstonSmith84WinstonSmith84, Hacker News commenter (self-described: Replacing a subscription coding model with an open model), Hacker News comment, published Sep 23, 2026
  41. Hacker News comment by pkulakpkulak, Engineer at Vevo (self-described: Paying for a popular open model through Fireworks), Hacker News comment, published Sep 22, 2026
  42. Claude Code subagents with the new feature "Claude Code keeps its own tier models with the Fireworks catalog added"Mikael Koskinen, .NET and Azure developer (adafy.com) (self-described: Running Claude Code subagents on Fireworks models through Fireworks' CLI), GitHub issue, published Sep 22, 2026
  43. Hacker News comment by ofisboyofisboy, Building frontier.legal (self-described: A production app at low volume), Hacker News comment, published Sep 11, 2026
  44. Hacker News comment by svachaleksvachalek, Software engineering manager (self-described: Choosing hosts for open-weight models), Hacker News comment, published Sep 11, 2026
  45. Hacker News comment by josephcooneyJoseph Cooney, software developer (self-described: Testing how a hosted Chinese open-weight model answers), Hacker News comment, published Aug 27, 2026
  46. Hacker News comment by mark_l_watson: Experimenting with open models instead of buying a bigger local machinemark_l_watson, author and AI practitioner (self-described: Experimenting with open models and choosing not to buy a bigger local machine), Hacker News comment, published Aug 25, 2026
  47. Hacker News comment by vijucatvijucat, Algorithmic trading developer (self-described: Long agentic coding projects with prompt caching), Hacker News comment, published Aug 8, 2026
  48. GLM models on Fireworks fail with prompt_cache_retention errorDuc Nguyen, Senior Software Developer at Wealthsimple (self-described: A coding agent that sends OpenAI prompt-cache fields), GitHub issue, published Aug 5, 2026
  49. [Bug]: fireworks session id is reset to a random string when header is not provided causing caching to not workHéctor Luaces (hluaces), Infrastructure engineer at Igalia (self-described: Calling Fireworks through a LiteLLM proxy and checking cache hits), GitHub issue, published Aug 3, 2026
  50. Hacker News comment by bwfan123: Buying zero-data-retention inference directly from Fireworksbwfan123, Hacker News commenter (self-described: Buying zero-data-retention inference directly from Fireworks), Hacker News comment, published Jul 31, 2026
  51. Hacker News comment by __jl____jl__, Hacker News commenter (self-described: A production app that ran open-weight models on Fireworks), Hacker News comment, published Jul 30, 2026
  52. Fireworks providerpimeys, twin.so (self-described: A team that wants Fireworks as a native provider in its coding agent), GitHub issue, published Jul 30, 2026
  53. Hacker News comment by drewnickdrewnick, Hacker News commenter (self-described: A daily sales workflow moved to an open model), Hacker News comment, published Jul 29, 2026
  54. Hacker News comment by lmf4lollmf4lol, building personal AI agents at Bluehouse (self-described: A platform that offers many models to its users), Hacker News comment, published Jul 27, 2026
  55. Hacker News comment by pimeyspimeys, twin.so (self-described: Heavy daily use and evaluation suites on serverless), Hacker News comment, published Jul 27, 2026
  56. Hacker News comment by bwfan123: Zero-data-retention access to DeepSeek modelsbwfan123, Hacker News commenter (self-described: Zero-data-retention access to DeepSeek models), Hacker News comment, published Jul 19, 2026
  57. When a 3060 is faster than a 5060tiMarcusAurelius68, Reddit poster (self-described: Embedding publications for a RAG pipeline, one chunk at a time), Reddit post, published Jul 16, 2026
  58. Hacker News comment by mark_l_watson: Daily work moved from Opus to an open modelmark_l_watson, author and AI practitioner (self-described: Daily work moved from Opus to an open model), Hacker News comment, published Jul 7, 2026
  59. Hacker News comment by elieli, Co-Founder of Industry Dive (self-described: Picking a serving tier), Hacker News comment, published Jun 30, 2026
  60. Billing has created unexpected financial risk while inference works reliably for short trialsreviewer2865477, PeerSpot reviewer (self-described: A few weeks on the Pay-As-You-Go plan through AWS Marketplace), PeerSpot review, published Jun 26, 2026
  61. Error trying to send messages via Fireworks model providerNyasha Chiroro, full stack web developer (self-described: A coding agent with conditional tool schemas), GitHub issue, published Jun 3, 2026
  62. Bug: Inflated Token Usage Reporting for accounts/fireworks/models/qwen3p6-plus with reasoning_effort="none"Hyacinth Ampadu, AI/Machine Learning Engineer (self-described: Cost tracking from Fireworks usage metadata through LangChain), GitHub issue, published May 28, 2026
  63. Model testing has become faster and fine-tuning now supports flexible customizationChaheti Jha, PeerSpot reviewer (self-described: One month of model testing and fine-tuning), PeerSpot review, published May 5, 2026
  64. Fireworks.ai models fail with 400 on tool-enabled requests via anthropic-messages APIHector Garcia (hectahertz), Buffer (self-described: An Anthropic-format coding agent pointed at Fireworks), GitHub issue, published Apr 27, 2026
  65. Gaining faster, flexible AI workflows has made our team ship reliable features with confidenceHussain Gagan, PeerSpot reviewer (self-described: Summarization and internal-search APIs moved to open models on Fireworks), PeerSpot review, published Apr 20, 2026
  66. Hacker News comment by mips_avatarmips_avatar, Building wanderfugl.com (self-described: Serving an application-specific RL or LoRA model), Hacker News comment, published Apr 16, 2026
  67. chore: temporarily redirect codestral 2508 to 2501 due to scalability problemsOmar Qunsul, Senior Backend Engineer, AI Coding, GitLab at the time (self-described: GitLab's code completions on on-demand Codestral deployments), GitLab merge request, published Mar 24, 2026
  68. Hacker News comment by vuldinvuldin, Hacker News commenter (self-described: Moving from a coding subscription to an open model), Hacker News comment, published Feb 18, 2026
  69. Reddit post by eteitaxiveteitaxiv, Reddit poster (self-described: Testing chat presets on open models through Fireworks and official APIs), Reddit post, published Feb 15, 2026
  70. Hacker News comment by extrextr, Hacker News commenter (self-described: Implementing specs on an enterprise TypeScript service), Hacker News comment, published Jan 31, 2026
  71. Hacker News post by mountainrivermountainriver, Hacker News poster (self-described: A small supervised fine-tuning job), Hacker News post, published Sep 19, 2025
  72. invalid_request_error when using models from Fireworks AIHareeshBahuleyan, Mozilla.ai, machine learning and NLP (self-described: Running the any-agent framework's tool loop on Fireworks), GitHub issue, published Aug 26, 2025
  73. Context Length Mismatch for Kimi K2Manan Suri, PhD student in CS at the University of Maryland (self-described: Long prompts to Kimi K2 Instruct on Fireworks through LiteLLM), GitHub issue, published Aug 6, 2025
  74. [Bug] Fireworks fine-tune error "Dataset ID: first character of id must not be a digit"scosman, Building kiln.tech (self-described: Kiln, a fine-tuning and evaluation app that calls Fireworks), GitHub issue, published Apr 19, 2025
  75. Hacker News comment by keithwhorkeithwhor, building Superuser (self-described: A startup that wants a US-hosted open model), Hacker News comment, published Mar 7, 2025
  76. Using Cline with a third-party hosted DeepSeek-V3, spits out junk when editing filescunningjames, Reddit poster (self-described: Cline with DeepSeek-V3 hosted on Fireworks), Reddit post, published Jan 31, 2025

Jan 2025 to Oct 2026.

47 sources from practitioners, by month and sentiment.

The full readExplore all Fireworks AI Signals7 praise themes, 7 concerns, 0 points of agreement and 1 disagreement across all three groups, with all 76 sources open to inspect.

Where It Fits

Where does Fireworks AI belong in the stack?

Fireworks AI is Core across Operate, supports Model and Data, and touches Build and Evaluate.

Business area
Product & Engineering
Function
Software Development
Operating stack
AI Application Platform
Primary role
Open-Model Inference Hosting
Fireworks AI beside the rest of its stackThe Stack Field, every product on every layer
FootprintFireworks AI across the AI Application Platform stack. Bar height is its role on each layer.
  1. Model

    1. Model APIs: Supports

      Model APIs, supports. Fireworks Serverless Inference is multi-tenant inference for popular open models, and the OpenAI Python client library works against it. The models themselves come from their makers, and closed models that Fireworks' auto model ID picks run on your own Anthropic or OpenAI account.

    2. Model gateway: Supports

      Model gateway, supports. Fireworks' auto model ID picks a model for each new user turn. LiteLLM and Helicone connect to a Fireworks account, and routing across several hosts stays with a gateway like them.

  2. Build

    1. Orchestration: Touches

      Orchestration, touches. Fireworks documents building agents with LangChain, LlamaIndex, CrewAI, AutoGen and other open-source frameworks over its OpenAI-compatible API. The framework controls the agent's steps.

    2. Tool use: Touches

      Tool use, touches. Tool calling lets hosted models select and use external tools through OpenAI-compatible tool specifications, and the tools run in your application. On the Anthropic-compatible endpoint, server-side tool execution is not supported.

  3. Data

    1. Retrieval: Supports

      Retrieval, supports. Fireworks hosts embedding and reranking models for RAG and semantic search through an OpenAI-compatible embedding service. The index and search stay with a vector database.

  4. Evaluate

    1. Evaluation: Touches

      Evaluation, touches. Supervised and reinforcement fine-tuning adapt models up to 1T+ parameters on Fireworks, and fine-tuned models serve at base-model prices. Test sets and scoring stay with an evaluation tool.

  5. Operate

    1. Observability: Touches

      Observability, touches. Helicone connects as a Fireworks proxy and provider integration, and Enterprise accounts get audit logs of storage reads, writes and deletes. Request tracing stays with an observability tool.

    2. Cloud deployment: Core

      Cloud deployment, core. This is Fireworks' primary role: Serverless shares a pool of GPUs with models pre-provisioned, and on-demand deployments reserve dedicated GPUs with fast autoscaling. New accounts get GPU quota in the GLOBAL multi-region only, and other regions need quota granted by Fireworks.

    3. Guardrails: Touches

      Guardrails, touches. Content moderation is one of the listed uses of the vision-language models Fireworks hosts. Screening prompts and outputs for attacks stays with a guardrail product.

Market mapThe layers Fireworks AI covers, and who else owns each one.

  1. Cloud deployment & capacity

    CoreOperate

  2. Model APIs

    SupportsModel

  3. Model gateways & routing

    SupportsModel

  4. Retrieval & grounding

    SupportsData

  5. Orchestration & agent frameworks

    TouchesBuild

4 more layers on the full map. Core, Supports and Touches describe coverage. They say nothing about quality.

Models

Which models run in Fireworks AI?

The current releases GILD tracks that public sources record running in Fireworks AI. Each model's page charts its public benchmark scores.

  1. DeepSeek V4.1 FlashDeepSeek's smallest model in its V4.1 architecture family, with native visual understanding, served on the DeepSeek API as deepseek-flash. Since October 17, 2026 that name serves the DeepSeek-V4.1-Flash-1017 build. Weights under the MIT license.DeepSeek V4.1Benchmarks
  2. GLM-5.2Z.ai's flagship model for long-horizon tasks, reading text only, with a 1M-token context window and 128K output tokens. API model glm-5.2 at US$1.40 per million input tokens and US$4.40 per million output tokens; also on Google Cloud, Databricks (which lists it as a legacy model) and in Cursor.GLMBenchmarks
  3. GLM-5.3Z.ai's flagship open-weight model for coding and agentic work, with a 1M-token context window, on the Z.ai API and also served by Mistral AI and OpenRouter. Weights under the GLM-5.3 License.GLMBenchmarks
  4. GLM-5.3-FlashZ.ai's first native multimodal model in the GLM-5 series, for efficient coding and long-horizon agent tasks, with a 1M-token context and a faster FlashX variant. 320B parameters with 18B active; weights under the MIT license.GLMBenchmarks
  5. gpt-oss-120bOpenAI's largest open-weight model, 117B parameters with 5.1B active, with a 131,072-token context window. Weights under the Apache 2.0 license; OpenAI lists no API price for it, so it runs on your own hardware or a host's.gpt-ossBenchmarks
  6. MiniMax M3.1 FlashMiniMax's multimodal reasoning model for agentic work, with a 1M-token context window, on the MiniMax API, M Plan and MiniMax Code, and also on OpenRouter and OpenCode Go.MiniMax M3Benchmarks
  7. Muse Spark OpenMeta's open-weight Muse model for coding and agentic work with image, video and document input, derived from the Muse Spark model Meta released in August with added safety post-training. 816B parameters with 42B active and a 1M-token context; weights under the Muse Model License. Meta does not serve it on its Model API, so it runs on your own hardware or a host's.MuseBenchmarks

More on Fireworks AI

  1. GILD ReviewGILD's evaluation, with the buying facts
  2. Research1 piece
  3. Guides1 guide
  4. Operator Insights3 operator accounts
  5. Where It FitsOn the AI Application Platform stack
  6. Related Products4 products