Archil vs Spider Cloud

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

Analysis reviewed Live tool data as of 2026-10-18
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At a glance

DimensionArchilSpider Cloud
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

Archil mounts S3, GCS, or NFS live at /mnt/archil and adds serverless sandboxes, tool calls, and forkable disks for AI agents.

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Spider Cloud
Spider Cloud

Spider Cloud is a web scraping, crawling, and search API that streams live pages as markdown or JSON for AI agents and RAG pipelines.

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Pricing
Freemium
Freemium
Plans
$0/month
$10/month minimum
$500/month minimum
Custom
$1/GB + $0.0001/CPU-min
From $6/mo
From $40/mo
Custom
Popularity
1 views
7.5k views
Skill Level
Advanced
Intermediate
API Available
Platforms
APICLIWeb
WebAPIPluginCLIDesktop
Categories
🧠 Agent Memory & Runtimes
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
Mount S3, GCS, or NFS live at /mnt/archil as a POSIX filesystem with zero copies
Serverless tool calls run Bash, Python, or Node against persistent files on demand
Files written by one tool call are available to the next — no sandbox to manage
Persistent sandboxes with persistent disk and hosted network services
Forkable, versioned disks: checkpoint before a run, branch attempts, roll back writes
One disk per user, session, or task, mounting on demand in about 100 ms
Shared SSD cache in front of your bucket at 10 GB/s default throughput per disk
Cached read and write latency under 1 ms for git clone and small-file workloads
Strongly consistent S3 API so human-facing apps read the same files agents write
POSIX mounts on Linux, macOS, containers, and Kubernetes
TypeScript SDK with createDiskTools exposes the filesystem to agents as tool calls
Python SDK for filesystem access from agent code
Connect from outside Archil with external mounts and mount tokens
Custom networking policy for sandboxes on paid plans
Encrypted at rest and in transit; SOC 2 Type II and HIPAA on Enterprise
Scrape a single page into markdown, JSON, HTML, raw, or plain text
Crawl entire sites with each page streamed as one JSONL line in order as it finishes
Web search endpoint returns SERP results plus the scraped pages behind them
Custom browser renders like a user: scripts run, lazy images load, infinite scroll reaches the end
Load protected pages through a real browser engine with geo checks, returning a 200
Run full browser sessions over WebSocket with anti-detection and rotating exits
Send AI commands (Act, Extract, Observe) over the Browser API WebSocket
Send a prompt on a scrape or crawl request and get named fields back as JSON
Two-phase AI extraction: a fast model for most pages, a stronger model for complex layouts
Provider router sends scrape and crawl requests to outside providers on your own keys
Data connectors pipe crawl results into S3, GCS, Google Sheets, Azure Blob, or Supabase
Proxy network with 215M+ residential and ISP exits in 199 countries, rotated per request
MCP server at mcp.spider.cloud for Claude Code, Codex, Cursor, and Claude Desktop
1,000+ ready-made scraper examples across 32 categories, each with working code
API responses support JSON, JSONL, CSV, and XML; up to 10,000 core API requests per minute by default
Integrations
Vercel AI SDK
LangChain
Mastra
eve
Claude Code
OpenCode
Codex
LanceDB
Daytona
E2B
Superserve
Modal
Vercel Sandbox
Kubernetes
Terraform
LlamaIndex
CrewAI
FlowiseAI
Langflow
Dify
Agno
MCP
Cursor
Claude Desktop
Amazon S3
Google Cloud Storage
Google Sheets

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 (averaged across 2 sources)

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

No verifiable community signal. We scanned public discussion on Oct 14, 2026 and found posts matching the name “Spider Cloud”, but could not establish that they are about this product rather than something else sharing its name. Rather than publish a score built on the wrong subject, we publish none.

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