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.
42 practitioners, 76 sources reviewed, Updated Nov 1, 2026How this is measured
- Operating stack
- AI Application Platform
Comparisons
How does Fireworks AI compare with the alternatives?
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.
Fireworks AI vs Together AI
- Inference Hosting
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?
Aman SangerCo-founderCursor
Cursor (Anysphere), maker of the Cursor AI coding agent and development environment, about 300 employees in 2025, applying planned code edits to whole files with its own fine-tuned 70b model in May 2024.
Cursor blog, published Read the write-up (opens Cursor blog in a new tab)
Observation
Cursor deployed its fine-tuned 70b fast-apply model with Fireworks, which built API support for Cursor's speculative edits, and reports about 1000 tokens per second.
- Takeaway
- When a latency-critical feature runs on your own fine-tuned model, ask a host whether it will support your decoding method in its API, since Cursor's speedup came from that method.
- What happened
- In May 2024 Cursor deployed its own fast-apply model, a fine-tuned 70b model for full-file code edits, with Fireworks, and Fireworks built API support for Cursor's custom speculation logic. Cursor reports about 1000 tokens per second, about 13x faster than vanilla inference using Llama-3-70b, and credits the gain to its own speculative edits method.
- Public engineering write-up
- Measured results reported
Deployment details
Cursor (Anysphere), maker of the Cursor AI coding agent and development environment, about 300 employees in 2025, applying planned code edits to whole files with its own fine-tuned 70b model in May 2024.
- Public engineering write-up
- Measured results reported
- Team
- Cursor (Anysphere), about 300 employees in 2025
- Workload
- Full-file code edits from Cursor's fast-apply model
- Model
- Cursor's own fine-tuned 70b fast-apply model
- Measurement
- About 1000 tokens (about 3500 characters) per second, about 13x faster than vanilla Llama-3-70b inference (May 14, 2024)
Omar QunsulSenior Backend Engineer, AI Coding, at the time of the changeGitLab
GitLab's IDE code completions in the asia-northeast1 and us-east4 regions, served from on-demand Codestral deployments on Fireworks through GitLab's AI gateway. GitLab lists 2,500+ team members (June 2026).
Its own announcement, published Read the merge request (opens the announcement in a new tab)
Observation
GitLab's incident record traced a 12.03% code-completion error rate to Fireworks being unable to scale a new Codestral 2508 deployment, and GitLab redirected the traffic to Codestral 2501.
- Takeaway
- Before a newly launched model becomes a production default, reserve capacity for it and keep a tested redirect to the previous deployment.
- What happened
- On March 24, 2026 GitLab's incident record INC-8707 (Severity 4) reported a 12.03% error rate for code completions in asia-northeast1 and us-east4, and gave the cause as Fireworks being unable to scale up a deployment for the new Codestral 2508 model. GitLab was on on-demand 2508 deployments that used Fireworks' legacy direct routing. Omar Qunsul's merge request redirected Codestral 2508 requests to the older Codestral 2501 deployment, and the incident's 12:42 UTC update said no drop in error rates had been observed yet. GitLab then moved to reserved deployments behind Fireworks' standard API gateway, merged at 19:09 UTC the same day.
- Public engineering write-up (merge request and incident record)
- Measured results reported
Deployment details
GitLab's IDE code completions in the asia-northeast1 and us-east4 regions, served from on-demand Codestral deployments on Fireworks through GitLab's AI gateway. GitLab lists 2,500+ team members (June 2026).
- Public engineering write-up (merge request and incident record)
- Measured results reported
- Team
- GitLab AI Coding
- Duration
- March 24, 2026
- Workload
- IDE code completions in asia-northeast1 and us-east4
- Product version
- On-demand deployments with legacy direct routing, then reserved deployments behind the standard API gateway
- Model
- Codestral 2508, redirected to Codestral 2501
- Measurement
- 12.03% code-completion error rate, Severity 4, in GitLab's incident record INC-8707 (Mar 24, 2026)
Buying and deployment
What a team building on it should verify before production traffic grows.
Sourced from Fireworks AI documentation
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.
Practitioner sentiment, all practitioners
−17 Mixed net
of 47 sources that take a side, each author counted once.
How this figure is madeTeams 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).
| Group | Sources | Positive | Mixed | Negative | No opinion stated and not counted in the score | Documentation, checked and not counted |
|---|---|---|---|---|---|---|
| Positive | 14 | 14 | 0 | 0 | 0 | 0 |
| Mixed | 13 | 0 | 13 | 0 | 0 | 0 |
| Negative | 20 | 0 | 0 | 20 | 0 | 0 |
| Documentation | 29 | 0 | 0 | 0 | 0 | 29 |
| Quarter | Sources |
|---|---|
| Q1 2025 | 2 |
| Q2 2025 | 1 |
| Q3 2025 | 3 |
| Q4 2025 | 0 |
| Q1 2026 | 4 |
| Q2 2026 | 8 |
| Q3 2026 | 21 |
| Q4 2026 | 8 |
| Documentation, checked | 29 |
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.
Most common concerns
Top concern of 3. Each mark is a source on it; filled marks raise it.
All 76 sources behind these findings
- Serverless latency FAQFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Anthropic compatibilityFireworks AI (Official), Product documentation, checked Nov 1, 2026
- IntroductionFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Tool callingFireworks AI (Official), Product documentation, checked Nov 1, 2026
- EmbeddingsFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Batch inferenceFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Vision modelsFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Nexus AutoFireworks AI (Official), Product documentation, checked Nov 1, 2026
- QuickstartFireworks AI (Official), Product documentation, checked Nov 1, 2026
- OpenAI compatibilityFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Agent frameworksFireworks AI (Official), Product documentation, checked Nov 1, 2026
- LLM gatewaysFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Microsoft FoundryFireworks AI (Official), Product documentation, checked Nov 1, 2026
- RegionsFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Data residencyFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Serverless overviewFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Serverless pricing (text)Fireworks AI (Official), Product documentation, checked Nov 1, 2026
- Cost FAQFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Billing and credits FAQFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Serverless rate limits (text)Fireworks AI (Official), Product documentation, checked Nov 1, 2026
- Account quotasFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Inference error codesFireworks AI (Official), Product documentation, checked Nov 1, 2026
- SSO and SCIMFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Data handlingFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Data securityFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Audit logsFireworks AI (Official), Product documentation, checked Nov 1, 2026
- On-demand deploymentsFireworks AI (Official), Product documentation, checked Nov 1, 2026
- PricingFireworks AI (Official), Product documentation, checked Nov 1, 2026
- Secure trainingFireworks AI (Official), Product documentation, checked Nov 1, 2026
- 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
- 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
- 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
NativeOutputfails on Fireworks models despite Fireworks supportingresponse_formatTim Becker, Researcher at Theori (self-described: Typed agents on Pydantic AI with Fireworks-hosted models), GitHub issue, published Oct 12, 2026- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Hacker News comment by ofisboyofisboy, Building frontier.legal (self-described: A production app at low volume), Hacker News comment, published Sep 11, 2026
- Hacker News comment by svachaleksvachalek, Software engineering manager (self-described: Choosing hosts for open-weight models), Hacker News comment, published Sep 11, 2026
- 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
- 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
- Hacker News comment by vijucatvijucat, Algorithmic trading developer (self-described: Long agentic coding projects with prompt caching), Hacker News comment, published Aug 8, 2026
- 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
- [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
- 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
- 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
- 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
- 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
- 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
- Hacker News comment by pimeyspimeys, twin.so (self-described: Heavy daily use and evaluation suites on serverless), Hacker News comment, published Jul 27, 2026
- 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
- 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
- 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
- Hacker News comment by elieli, Co-Founder of Industry Dive (self-described: Picking a serving tier), Hacker News comment, published Jun 30, 2026
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Hacker News comment by extrextr, Hacker News commenter (self-described: Implementing specs on an enterprise TypeScript service), Hacker News comment, published Jan 31, 2026
- Hacker News post by mountainrivermountainriver, Hacker News poster (self-described: A small supervised fine-tuning job), Hacker News post, published Sep 19, 2025
- 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
- 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
- [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
- 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
- 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.
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
Model
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.
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.
Build
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.
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.
Data
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.
Evaluate
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.
Operate
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.
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.
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.
Cloud deployment & capacity
- Amazon Bedrock
- Gemini Enterprise Agent Platform
- Microsoft Foundry
- Together AI
- Microsoft Foundry
- Groq
Model APIs
Model gateways & routing
- LiteLLM
- OpenRouter
- Portkey
- Vercel AI Gateway
Retrieval & grounding
Orchestration & agent frameworks
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.

