agentspeed.

fireworks.ai

scanned 7/25/2026, 10:41:14 PM · cached · refresh in 22h · rubric 2026.07.4
Built with Next.js
robots.txt present: passrobots.txt allows AI agents: passContent-Signal directives: failllms.txt present: warnSitemap present: passLink response headers: warnCanonical URL: passMCP server card: failOAuth authorization metadata: failOAuth resource metadata: failAPI catalog / OpenAPI: failWeb Bot Auth: failMarkdown negotiation: failText-to-markup ratio: failContent without JavaScript: skipHeading hierarchy: warnCookie wall blocks content: passPage title: passMeta description: passJSON-LD present: passStructured data validates: passSchema type coverage: passPaywall / login wall: passAgent Skills manifest: failWebMCP actions: failPrimary action reachable: passReachability: passTime to first byte: passFull render time: skipPage weight: pass76/ 100 · C
Good

This is how AI agents (not browsers) experience fireworks.ai. The score weights five categories of machine-readability; the ticks on the arc are the 30 individual checks behind it.

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Top fixes

Checks tagged “emerging” are 2026 agent-protocol standards most of the web hasn’t adopted yet — adopting early is an edge, not a defect.

warnllms.txt presentDiscoverability

Found /llms.txt but missing H1 header and markdown links.

Fix · Next.js: Add /llms.txt via app/llms.txt/route.ts (or public/llms.txt). Generate one free at /tools/llms-txt-generator.

failText-to-markup ratioReadability

Visible text is 1.9% of HTML weight (10504/562670 bytes). Low. Markup may be crowding out agent-usable text.

Fix: Heavy ad or navigation markup crowds out agent-usable text. Aim for a text-to-DOM ratio above 15%.

warnLink response headersDiscoverability

A Link: header is present but carries none of canonical/alternate/describedby — the relations agents consume.

Fix: Emit `Link:` headers for canonical, alternate-language, and describedby relations. Agents that fetch HEAD-only or stream-render rely on these.

warnHeading hierarchyReadability

Heading issues: 3 <h1> tags (expected 1).

Fix: Use a single H1 and avoid skipping heading levels (H1 → H3).

failContent-Signal directivesemergingDiscoverability

No Content-Signal directives. Add e.g. `Content-Signal: ai-train=no, ai-summarize=yes` to declare granular AI usage policy beyond binary allow/disallow.

Fix: Add `Content-Signal:` directives to your robots.txt. This is the emerging standard (Cloudflare-driven) for declaring fine-grained AI usage policy beyond a binary allow/disallow.

failMCP server cardemergingDiscoverability

No valid MCP Server Card at /.well-known/mcp/server-card.json. Publish one so agents can discover your tools without HTML scraping.

Fix: Publish an MCP Server Card describing the tools your site exposes. Agents discover capabilities via `/.well-known/mcp/server-card.json`. See modelcontextprotocol.io for the schema.

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Fix it

Generated, ready-to-ship files for the gaps above — copy them or download and drop them into your repo.

Add an llms.txt
Publish this at https://fireworks.ai/llms.txt
# Fireworks AI - Fastest Inference for Generative AI

> Use state-of-the-art, open-source LLMs and image models at blazing fast speed, or fine-tune and deploy your own at no additional cost with Fireworks AI!

## Key pages
- [Home](https://fireworks.ai/)
- [Own your model . Own your future .](https://fireworks.ai/)
- [Own your model . Own your future .](https://fireworks.ai/)
- [Own your model . Own your future .](https://fireworks.ai/)
- ["Fireworks is the TSMC of AI Factories..."](https://fireworks.ai/)
- [Foundational Infrastructure for Specialized Intelligence](https://fireworks.ai/)
- [Run the latest open models with a single line of code](https://fireworks.ai/)

## About
Describe what fireworks.ai does, who it's for, and the primary action you want an
agent to be able to complete. Keep it factual and current.
Qualifies for certificationBronze

This score clears the Bronze threshold (70+). Certification turns it into a dated, publicly verifiable attestation — a verification URL, an embeddable badge, and weekly monitoring for a full year. Scores under 70 can’t buy this, which is what makes displaying it mean something.

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What the agent receives

The agent’s-eye view
Fireworks AI - Fastest Inference for Generative AI Announcing our Series D and $1B ARR Product Solutions Models Pricing Resources Log In Get Started FROM THE CREATORS OF PYTORCH Own your model . Own your future . Fireworks’ SoTA training and inference take you beyond the frontier, transforming open models into your specialized intelligence. Fireworks processes 40T+ tokens per day Get Started Contact Us FROM THE CREATORS OF PYTORCH Own your model . Own your future . Fireworks’ SoTA training and inference take you beyond the frontier, transforming open models into your specialized intelligence. Fireworks processes 40T+ tokens per day Get Started Contact Us FROM THE CREATORS OF PYTORCH Own your model . Own your future . Fireworks’ SoTA training and inference take you beyond the frontier, transforming open models into your specialized intelligence. Fireworks processes 40T+ tokens per day Get Started Contact Us NVIDIA GTC 2026, Jensen Huang &quot;Fireworks is the TSMC of AI Factories...&quot; Jensen Huang explains why Fireworks is unique in the market in a conversation with our CEO, Lin Qiao. Build your frontier Foundational Infrastructure for Specialized Intelligence Define the frontier of your craft with compounding specialized intelligence. Own the learning loop that transforms the leading open source models and your data into an edge that sharpens with every iteration. From guided runs to frontier RL Training The full spectrum of ways to train a model. Move down the stack as your workload matures. Every checkpoint deploys to production in seconds. Choose from the following: • Get a guided path. Describe the task, review the plan and cost, approve the run, and get a trained model. • Configuration-led. You know the model, data, and method. We handle scheduling, training, and the production handoff. • Write your training logic. Your own loss, trainer, and RL loop, on our GPUs, rollout serving, and weight sync. Learn more Talk to our team For development to Cursor-scale Inference Serve the latest open models, or your own trained versions. Our inference engine is optimized at every layer for industry-leading throughput and latency while preserving model quality. • Serverless. Pay per token with Priority and Fast options to meet your requirements. OpenAI and Anthropic compatible. • On-Demand. Dedicated deployments. Multi-region, supports post-trained models. • Reserved. Guaranteed capacity, higher quotas, get the newest hardware first. Learn more Talk to our team Model library Run the latest open models with a single line of code Get instant access to the most popular OSS models, optimized for cost, speed, and quality. View all models Deepseek v3.2 163840 Context LLM New GLM 5.2 $1.4/M Input • $4.4/M Output • 1048576 Context LLM Kimi K2.7 Code $0.95/M Input • $4/M Output • 262144 Context Vision New Minimax M3 $0.3/M Input • $1.2/M Output • 512000 Context Vision New Qwen3.7 Plus $0.4/M Input • $1.6/M Output • 262144 Context Vision DeepSeek-V4-Pro $1.74/M Input • $3.48/M Output • 1048576 Context LLM DeepSeek-V4-Flash $0.14/M Input • $0.28/M Output • 1048576 Context LLM Kimi K2.6 $0.95/M Input • $4/M Output • 262144 Context Vision GLM 5.1 $1.4/M Input • $4.4/M Output • 202752 Context LLM Gemma 4 31B IT NVFP4 262144 Context Vision Gemma 4 26B A4B IT 262144 Context Vision Qwen3.6 Plus Vision MiniMax M2.7 $0.3/M Input • $1.2/M Output • 196608 Context LLM OpenAI gpt-oss-20b $0.07/M Input • $0.3/M Output • 131072 Context LLM FLUX.1 Kontext Pro Image Whisper V3 Large Audio Deepseek R1 05/28 163840 Context LLM Kimi K2.5 262144 Context Vision Deepseek v3.2 163840 Context LLM New GLM 5.2 $1.4/M Input • $4.4/M Output • 1048576 Context LLM Kimi K2.7 Code $0.95/M Input • $4/M Output • 262144 Context Vision New Minimax M3 $0.3/M Input • $1.2/M Output • 512000 Context Vision Customer Love What our customers are saying “Using Fireworks AI on Foundry, we can run repeatable, high-volume evaluations through a single Azure endpoint, which helps our team move faster from deployment to informed model decisions with more confidence.” Hanbin Jung | Partnership Lead at Motif &quot;The aha moment was when we were deciding whether to roll out GLM-5.2 as one of our recommended model options. We felt Fireworks gave us the confidence to do this without reliability concerns. We have a main agent that has access to all of our data that everyone across the company interacts with and asks questions. When we moved this agent from Opus 4.8 to GLM-5.2, nobody noticed a difference in the experience. The outputs were consistent with what we expected, which gave us the confidence to make GLM-5.2 a recommended model option.&quot; Gonzalo Soto Mallqui | Chief Product Officer at Gum Loop why did Cursor rollout Composer 2 with @FireworksAI_HQ? &quot;...because it&#x27;s way more performant than the open source engines and is what we use in production. our rl inference scales elastically and globally because of it. when we have low prod traffic we scale up RL, when we have high prod traffic, we scale down RL.&quot; Federico Cassano | AI Researcher at Cursor &quot;Vercel’s v0 model is a composite model. The SOTA in this space changes every day, so you don’t want to tie yourself to a single model. Using a fine-tuned reinforcement learning model with Fireworks, we perform substantially better than SOTA.&quot; Malte Ubl | CTO at Vercel &quot;By partnering with Fireworks to fine-tune models, we reduced latency from about 2 seconds to 350 milliseconds, significantly improving performance and enabling us to launch AI features at scale. That improvement is a game changer for delivering reliable, enterprise-scale AI.&quot; Sarah Sachs | AI Lead at Notion &quot;Fireworks enabled us to own our AI journey , and unlock better quality in just four weeks.&quot; Kay Zhu | CTO at Genspark &quot;We&#x27;ve had a really great experience working with Fireworks to host open source models, including SDXL, Llama, and Mistral. After migrating one of our models, we noticed a 3x speedup in response time, which made our app feel much more responsive and boosted our engagement metrics.&quot; Spencer Chan | Product Lead at Quora &quot;Fireworks has been a fantastic partner in building AI dev tools at Sourcegraph. Their fast, reliable model inference lets us focus on fine-tuning, AI-powered code search, and deep code context, making Cody the best AI coding assistant. They are responsive and ship at an amazing pace.&quot; Beyang Liu | CTO at Sourcegraph By running Fireworks AI on Azure Foundry, UiPath powers both Autopilot and Delegate with open models that are significantly faster and more cost-efficient for Computer Use, all while matching the quality of Claude&#x27;s Sonnet 4.6. It&#x27;s a step-change in how we deliver AI at scale to our customers. Mircea Neagovici-Negoescu | SVP, Head of AI at UiPath &quot;Fireworks has been a key partner in helping us train and serve the models behind Cursor at scale. Their platform supports the high-throughput RL workloads and production inference required for Composer, giving us the speed, reliability, and efficiency to keep pushing the frontier of AI coding.&quot; Sualeh Asif | CPO at Cursor &quot;Fireworks enabled us to own our AI journey, and unlock better quality in just four weeks. This resulted in a better user experience for our customers.&quot; Kay Zhu | CTO at Genspark &quot;The rLLM team is dedicated to pushing the boundaries of autonomous AI, which means our time is best spent on innovation rather than managing backend clusters. The Fireworks Training SDK lets us focus on our research instead of wrestling with infrastructure. The platform is fast, well-optimized, and just works.&quot; Kyle Montgomery &amp; Sijun Tan | Core Contributors, rLLM at rLLM &quot;Fireworks&#x27; Multi-LoRA capabilities align with Cresta&#x27;s strategy to deploy custom AI through fine-tuning cutting-edge base models. It helps unleash the potential of AI on private enterprise data.&quot; Tim Shi | Co-Foun

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AgentSpeed score for fireworks.ai
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Full breakdown

15 pass3 warn10 fail2 skip
Discoverability
pass
robots.txt present
A robots.txt is reachable at the site root.
100/100
pass
robots.txt allows AI agents
All 8 answer-time access agents allowed.
100/100
fail
Content-Signal directivesemerging
No Content-Signal directives. Add e.g. `Content-Signal: ai-train=no, ai-summarize=yes` to declare granular AI usage policy beyond binary allow/disallow.
0/100
warn
llms.txt present
Found /llms.txt but missing H1 header and markdown links.
60/100
pass
Sitemap present
sitemap.xml reachable and referenced from robots.txt.
100/100
warn
Link response headers
A Link: header is present but carries none of canonical/alternate/describedby — the relations agents consume.
60/100
pass
Canonical URL
Canonical points to self: https://fireworks.ai/.
100/100
fail
MCP server cardemerging
No valid MCP Server Card at /.well-known/mcp/server-card.json. Publish one so agents can discover your tools without HTML scraping.
0/100
fail
OAuth authorization metadataemerging
No RFC 8414 metadata at /.well-known/oauth-authorization-server. Agents that act on behalf of users need this to discover your authorization endpoints.
0/100
fail
OAuth resource metadataemerging
No RFC 9728 metadata at /.well-known/oauth-protected-resource. Publish it so agents can discover required scopes without hand-coded credentials.
0/100
fail
API catalog / OpenAPIemerging
No /.well-known/api-catalog (RFC 9727) and no /openapi.json|yaml. Publish one so agents that integrate with APIs can discover your endpoints.
0/100
fail
Web Bot Authemerging
No Web Bot Auth JWKS at /.well-known/http-message-signatures-directory.json. Publish one to allow trusted agents while keeping a default-deny posture for the rest.
0/100
Readability
fail
Markdown negotiationemerging
Accept: text/markdown returns HTML, not markdown. Serve a markdown variant of primary content when requested; agents summarize and cite it more reliably.
0/100
fail
Text-to-markup ratio
Visible text is 1.9% of HTML weight (10504/562670 bytes). Low. Markup may be crowding out agent-usable text.
14/100
skip
Content without JavaScript
Browser pass unavailable; cannot measure JS dependency.
warn
Heading hierarchy
Heading issues: 3 <h1> tags (expected 1).
75/100
pass
Cookie wall blocks content
No common consent-modal markers detected.
100/100
pass
Page title
A concise <title> is present (50 chars).
100/100
pass
Meta description
A well-sized meta description is present (152 chars).
100/100
Structured data
pass
JSON-LD present
Found 1 JSON-LD block.
100/100
pass
Structured data validates
1/1 checkable block(s) validated cleanly against schema.org.
100/100
pass
Schema type coverage
Page intent unclear; no specific schema.org type expected.
100/100
Actionability
pass
Paywall / login wall
No paywall or login-wall detected on the landing URL.
100/100
fail
Agent Skills manifestemerging
No Agent Skills manifest at /.well-known/agent-skills.json. Enumerate the tasks agents can perform (search, add-to-cart, contact-support) so they pick the right one without scraping.
0/100
fail
WebMCP actionsemerging
No WebMCP detected. On pages with first-class actions (cart, support, account), embed a WebMCP server so on-page agents invoke tools directly.
0/100
pass
Primary action reachable
Primary offering identified ("AI model training and inference platform") with price/CTA ("Get Started").
100/100
pass
Reachability
HTTP 200.
100/100
Performance
pass
Time to first byte
TTFB 423ms. Healthy.
80/100
skip
Full render time
Full-render time unavailable (browser pass skipped or failed).
pass
Page weight
Initial document is a lean 549 KB.
100/100
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