- Visibility26
- Citations4
- Sentiment71
- Site quality73
- AI readiness36
AI assistants name Backblaze in 28.3% of answers, which is much more often than the typical company in its group at 3.3%. Its overall score is 39 out of 100, ranking 453rd of 2,047 companies in the study. The Backblaze website passes 5 out of 14 technical checks for readiness for AI tools.
We asked 15 unbranded questions about its market: none named Backblaze. AI assistants named it in 17 of 60 answers (28.3%). The typical company in its group is named in 3.3% of answers. By how often it is named, Backblaze ranks 9 of 61 in its sector.
Every dot is a company in the study. Hover one to see it; click to open its report.
This companySame sectorEvery other companyNever named
Five parts, each scored out of 100.
How often AI assistants name the company
How often AI uses the company's own website as a source
How positively AI describes the company
How well built the website's own pages are
How easily AI tools can read and use the website
Visibility is calculated from the answers on this page. How the score is built.
61 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“Which cloud storage options work well for data-heavy application development?”
I'll search for current information about cloud storage options suitable for data-heavy application development.Based on current information, here are the best cloud storage options for data-heavy application development:
Object Storage Solutions
Amazon S3, Azure Blob Storage, and Google Cloud Storage provide cloud-based object storage solutions designed to store large volumes of unstructured data like images, videos, and backups, and they are accessible via APIs for applications.
Amazon S3 stands out as an industry leader. It offers object storage with industry-leading scalability, …
The AI’s answer as we received it, shortened and not checked for accuracy.
How the mentions in these answers were split between this company and its rivals
Answers naming it, out of answers received
What this website publishes for an AI agent to read, use and buy from, on a scale of six levels.
- 0
- 1
- 2
- 3
- 4
- 5
Level 1: Basic web presence. Two of robots.txt, a sitemap and Link headers are in place.
- Content Signals
Show every check, for your technical teamHide the checks
- study: 92.0%robots.txtPassed
A valid robots.txt with crawl rules at the site root.
- study: 86.8%SitemapPassed
An XML sitemap listing the pages, ideally referenced from robots.txt.
- study: 0.3%Link headersNot met
Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.
- study: 5.5%DNS for AI DiscoveryNot present
Advertise agent endpoints with SVCB records under _agents.
- study: 1.9%Markdown negotiationNot met
Return a markdown version when asked with Accept: text/markdown, for example at the CDN.
- study: 25.1%llms.txtPassed
An llms.txt that tells language models what the site offers.
- study: 93.4%AI bot rulesPartly met
Add explicit robots.txt groups for the main AI crawlers.
- study: 1.4%Content SignalsNot met
Add a Content-Signal line for search, ai-input and ai-train to robots.txt.
- study: 0.2%Web Bot AuthNot present
Only relevant if you run your own agents or crawlers: publish a signing key directory.
- study: 0.3%API CatalogNot met
List public APIs in a linkset at /.well-known/api-catalog.
- study: 11.8%OAuth discoveryNot met
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourceNot met
Serve protected resource metadata at /.well-known/oauth-protected-resource.
- study: 0.0%auth.mdNot met
Publish an auth.md describing how agents sign in.
- study: 0.2%MCP Server CardNot met
Offer an MCP server and publish its server card under /.well-known.
- study: 0.1%A2A Agent CardNot met
Publish an agent card at /.well-known/agent-card.json.
- study: 0.2%Agent SkillsNot met
Publish a skills index with the main tasks agents can do.
- study: 33.7%WebMCPCould not check
Register key actions such as search or cart as WebMCP tools.
- study: 0.1%ARD manifestNot met
Publish an ai-catalog.json listing every agent interface.
Score 36 of 100 · 97% of checks could run · checked on October 5, 2026 · www.backblaze.com · 2,047 companies scanned
5 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.
These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.
Show the 14 checksHide them
- Passedrobots.txt publishedstudy: 85.0%
- PassedXML sitemapstudy: 73.2%
- PassedHTTP Link headers (RFC 8288)study: 29.0%
- Not metMarkdown content negotiationstudy: 8.3%
- Passedllms.txt publishedstudy: 21.7%
- PassedToken budget (page weight)study: 95.2%
- Not metExplicit AI bot rulesstudy: 13.0%
- Not metAPI Catalog (.well-known/api-catalog)study: 0.2%
- Not metOAuth Authorization Server discoverystudy: 10.1%
- Not metOAuth Protected Resource discoverystudy: 9.3%
- Not metMCP Server Cardstudy: 0.0%
- Not metA2A Agent Cardstudy: 0.0%
- Not metWebMCP toolsstudy: 4.2%
- Not metAgent Skills declaredstudy: 0.0%
This website sets no rules for any AI crawler by name, like 84% of the websites whose rules we could read. Every AI crawler follows its general rules.
- GPTBotpartly
- ClaudeBotpartly
- Anthropic-AIpartly
- Google-Extendedpartly
- Applebot-Extendedpartly
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespiderpartly
- CCBotpartly
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBotpartly
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-Userpartly
- Claude-Userpartly
- Perplexity-Userpartly
- DuckAssistBotpartly
- MistralAI-Userpartly
Read from the website’s own rules file (robots.txt). An asterisk marks a crawler the file names. The rest follow its general rules. “Partly” means only some pages are blocked.
How many times each website was listed as a source in these answers
In the study as US-listed.
- How to resolve slow data transfer issues with cloud object storage used in machine learning pipelines?
- Which cloud storage options work well for data-heavy application development?
- What’s the best way to store and serve massive datasets for AI model training?
- What are alternatives to S3 APIs for managing cloud data in AI-forward environments?
- Are there lower-cost alternatives to high-performance cloud object storage for research teams?
- How does S3-compatible object storage compare to traditional NAS for AI workflows?
- Should enterprises prioritize capacity or performance when choosing storage for neocloud platforms?
- How do pricing tiers for enterprise cloud object storage typically work in the US?
- Which storage solutions can replace classic object storage for neocloud workloads?
- What factors most impact the cost of high-throughput cloud storage for large-scale AI projects?
- Is premium high-throughput object storage worth the investment for AI development teams?
- How can object storage support disaster recovery for enterprise application data?
- Cloud object storage vs block storage for enterprise data—what are the trade-offs?
- What are common integration challenges with S3-compatible storage in multi-cloud environments?
- What are the best high-performance object storage solutions for AI workloads?
Columns, left to right: OpenAI, Claude, Perplexity, Gemini. The AI assistants often disagree. Across the study, on 40% of the questions where a company is named, only one of the four names it.
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<a href="https://aeo-audit.rezolve.com/us/backblaze.com"><img src="https://aeo-audit.rezolve.com/us/badges/backblaze.com.svg" alt="Overall score 39 of 100, US AI Visibility Study 2026" width="280" height="64"></a>