Archil vs Spider Cloud

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-09-01
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At a glance

DimensionArchilSpider Cloud
PricingContact salesFree + usage-based (from $0.03/1k pages)
Best ForAI/ML engineers needing fast data access for trainingAI agents needing real-time web data for RAG
DeploymentSelf-managed (Kubernetes, Docker, hybrid/multi-cloud)Cloud API (with self-hosted open-source fallback)
Key FeaturePOSIX-compatible parallel file system for AI trainingWeb crawling & scraping with AI extraction (e.g., Silk model)
Latest News ImpactNo recent updates; static features applyNew Browser AI commands & data connectors (Feb-Mar 2026)
IntegrationAI frameworks (PyTorch, TensorFlow)LangChain, LlamaIndex, CrewAI, S3, GCS, Supabase

Choose Archil if you need a high-performance file system for AI training on large datasets in a self-managed cloud/HPC environment. Choose Spider Cloud if you want a fast, low-cost scraping API to feed web data into AI agents or RAG pipelines, especially with recent Browser AI commands and data connectors.

Try ArchilTry Spider Cloud
Archil
Archil

Mount live enterprise data as a POSIX filesystem for production AI agents

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Spider Cloud
Spider Cloud

AI web scraping API: crawl, scrape, search any site into markdown or JSON at 10k req/min.

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Pricing
Freemium
Freemium
Plans
$0/mo
$500/mo
Custom
$1/GB + $0.001/min compute
$40/mo (2 concurrency)
$6/mo
Popularity
0 views
7.5k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
APICLI
WebAPICLI
Categories
🧠 Agent Memory & Runtimes
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
POSIX-compatible filesystem mounted at /mnt/archil
Serverless sandboxes for Bash, Python, Node (2 vCPU, 4 GB RAM)
Compose context from multiple sources (S3, GCS, NFS) with per-source permissions
In-place data access without ETL or copying
Strongly-consistent S3 API for human-agent collaboration
Versioning, checkpoints, branches, and rollback
7 GB/s sustained throughput per client
100x faster small-file performance vs S3
Hybrid SSD cache with cold data in customer-owned buckets
Only active compute billed ($0.18/hr), idle $0
Integrations for Vercel AI SDK, eve, Mastra, LangChain
Kubernetes and Docker deployment
HIPAA compliance and SOC 2 Type II
Encryption in transit and at rest
Real-time monitoring and self-healing fault tolerance
Scrape any website into markdown, JSON, or raw HTML
Full-site crawling at 100K+ pages/sec
10,000 core API requests per minute default
Web Search API: SERP + scraping + extraction in one call
/ai/search endpoint with relevance gate to skip irrelevant pages
Silk AI model: HTML-to-structured data and captcha solving on GPUs
Browser Cloud: full browser sessions over CDP
AI commands (Act, Extract, Observe) via WebSocket with AI Studio
Multiple output formats: HTML, raw, plain text, markdown, JSON, JSONL, CSV, XML
Stealth browser layer and Unblocker for anti-bot sites
Proxy pool with 215M+ residential and ISP IPs across 199+ countries
Robots.txt compliance on by default, disable per-request
data_connectors parameter: pipe results to S3, GCS, Google Sheets, Azure Blob, Supabase
extraction_schema parameter: AI output conforms to JSON schema
1,000+ ready-made scraper examples across 32 categories
Integrations
Vercel AI SDK
eve
Mastra
LangChain
LlamaIndex
CrewAI
FlowiseAI
AutoGen
Agno

What real users say: Archil vs Spider Cloud

Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.

Archil

44 mentions across 2 sources · 28% positive — critical

Hacker News, Lemmy

What users praise

  • • Custom protocol delivers higher performance than NFS-based solutions.
  • • Designed specifically for AI data patterns (random reads, streaming).
  • • POSIX-compatible interface works with standard tools and frameworks.
  • • Cloud-native deployment via Kubernetes and Docker.

What frustrates them

  • • Very limited independent community feedback — mostly founder posts.
  • • No real-world performance benchmarks or case studies available.
  • • 'Contact us' pricing may be expensive for small teams.
  • • Proprietary protocol could lock users into the ecosystem.

Researched Jul 3, 2026

Spider Cloud

41 mentions across 2 sources · 0% positive — critical

YouTube, Lemmy

What users praise

  • • Competitive pay-as-you-go pricing at $1/GB with no expiry.
  • • Default rate limit of 10,000 requests per minute is generous.
  • • Broad output formats (HTML, markdown, JSON, CSV) cover diverse needs.
  • • Integrated Web Search API bundles SERP and extraction for AI agents.

What frustrates them

  • • No community feedback to confirm reliability or performance.
  • • Self-reported metrics lack independent verification.
  • • Stealth browser success may vary across real sites.
  • • Potential legal risks from scraping; compliance is user's responsibility.

Researched Aug 26, 2026

Who should pick which

  • ML engineer training large models
    Pick: Archil

    Archil's POSIX-compatible parallel file system delivers high-throughput I/O needed for petabyte-scale training datasets, with native integration with PyTorch/TensorFlow.

  • AI agent developer needing web context
    Pick: Spider Cloud

    Spider Cloud's scraping API with AI extraction and Browser AI commands (Act, Extract, Observe) provides real-time web data for RAG-powered agents, plus connectors to vector stores.

  • HPC researcher with regulated data
    Pick: Archil

    Archil's fine-grained access controls (ACLs, RBAC) and self-healing fault tolerance are critical for regulated environments and high-performance computing.

  • Developer building a content aggregator
    Pick: Spider Cloud

    Spider Cloud's low-cost scraping ($0.03/1k pages), structured output options, and ready-made scraper catalog (1,000+ examples) accelerate building a content pipeline.

  • Enterprise MLOps team
    Pick: Archil

    Archil's multi-tenancy with resource isolation, real-time monitoring, and cloud-native deployment fit enterprise requirements for managing multiple AI pipelines.

Frequently Asked Questions

Archil vs Spider Cloud: which should you choose?

Choose Archil if you need a high-performance file system for AI training on large datasets in a self-managed cloud/HPC environment. Choose Spider Cloud if you want a fast, low-cost scraping API to feed web data into AI agents or RAG pipelines, especially with recent Browser AI commands and data connectors.

Can Archil replace an object store like S3?

No. Archil is a file system (POSIX interface) optimized for AI training; it is not S3-compatible. For object storage needs, you would use S3 or similar alongside Archil.

Does Spider Cloud offer a self-hosted version?

Yes, Spider Cloud has an open-source core available on GitHub for self-hosting, though the cloud API provides additional features like the unblocker and AI models.

What are Browser AI commands in Spider Cloud?

Announced March 2026, Browser AI commands allow sending natural language instructions via WebSocket: Act (click, type, navigate), Extract (pull structured data), and Observe (describe screen). Requires AI add-on.

Is Archil suitable for small datasets?

Not ideal. Archil is designed for large-scale, high-performance workloads. For small datasets, simpler storage solutions are more cost-effective.

How does Spider Cloud handle anti-bot measures?

Spider Cloud uses rotating proxies and automatic retries for unblocking. It also offers a Silk custom AI model for captcha solving. The latest news includes an /ai/unblocker endpoint.

What integrations does Archil have with AI frameworks?

Archil integrates natively with PyTorch and TensorFlow, providing a POSIX interface that standard tools can use without modification.

Can I use Spider Cloud for RAG pipelines?

Yes, Spider Cloud is built for AI agents and RAG pipelines. It outputs markdown and other formats suitable for chunking and embedding, and integrates with LangChain, LlamaIndex, and more.

Does Archil support data versioning?

Yes, Archil includes data versioning and snapshotting features, allowing reproducibility in AI experiments.

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Explore each tool further

Archil
View Archil reviewArchil alternatives
Spider Cloud
View Spider Cloud reviewSpider Cloud alternatives

Browse these categories

Best AI Agent Memory & Runtimes toolsBest AI Web Scraping & Search APIs toolsBest AI Browser & Computer-Use Agents tools
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Last reviewed: July 3, 2026