Zenml vs Spider Cloud

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

Analysis reviewed Live tool data as of 2026-10-08
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At a glance

DimensionZenmlSpider Cloud
Core FunctionML pipeline orchestration & agent runtimeWeb crawling & scraping API for AI agents
Primary UsersML engineers and data scientistsAI agent developers and RAG pipeline builders
Key StrengthReproducible pipelines with durable execution (Kitaru)High-speed scraping with 99.9% uptime at low cost
Notable IntegrationsKubernetes, Vertex AI, LangChain, LangGraphLangChain, LlamaIndex, CrewAI, AutoGen
Latest NewsKitaru open source durable execution for agents (2026-04)Browser AI commands via WebSocket (2026-03)

ZenML and Spider Cloud address different layers of the AI stack: ZenML is for orchestrating ML pipelines and making AI agents durable (via Kitaru), while Spider Cloud is for fetching web data at scale for RAG and AI agents. If you need to build reliable, reproducible ML workflows or add crash recovery to your agents, choose ZenML. If you need a fast, cheap, and reliable web scraping API to feed data to your agents, choose Spider Cloud. They can also complement each other in a broader system.

Try ZenmlTry Spider Cloud
Zenml
Zenml

Open-source ML orchestration plus a durable agent runtime that replays real production sessions before a change ships.

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Spider Cloud
Spider Cloud

Spider Cloud is a web scraping and crawling API that turns live pages into markdown or JSON for agents and RAG pipelines.

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Pricing
Freemium
Freemium
Plans
$0/mo
$39/mo
$999/mo
Custom
$1/GB + $0.0001/CPU-min
From $6/mo
From $40/mo
Custom
Popularity
18 views
7.5k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
WebCLIAPI
WebAPIPluginCLIDesktop
Categories
🕸️ Agent Frameworks & Orchestration📊 Data & Analytics⚙️ Developer Infrastructure
🌐 Web Scraping & Search APIs🖱️ Browser & Computer-Use Agents
Features
Declarative pipeline DAGs via Python decorators
Pluggable stack architecture across clouds and orchestrators
Automatic artifact versioning and lineage tracking
Built-in basic model registry with versioning
Smart caching to skip unchanged pipeline steps
Distributed execution on Kubernetes, Vertex AI, SageMaker, AzureML, Kubeflow, Airflow
Kitaru durable execution with checkpoints and durable waits
Replay boundaries and inspectable execution history for agents
Replay-based evals in Python and TypeScript
Convert production traces into replayable test scenarios
Cohorts: immutable sets of sessions for evaluation
Evaluators and side-by-side run-vs-run comparison
Experiment configuration: swap model, prompt, or tool policy
Session and trace import from Langfuse, LangSmith, Braintrust, Logfire and Arize Phoenix
Self-hosted deployment in Docker or your own VPC
Scrape a single page into markdown, JSON, HTML, raw text, or plain text
Crawl entire sites with each page streamed as one JSONL line in order the moment it finishes
Web search endpoint returns SERP results plus the scraped pages behind them in one call
Custom browser renders like a user: scripts run, lazy images load, infinite scroll completes
Unblocker loads protected pages through a real browser engine with geo checks and a 200
Browser Cloud runs full sessions 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 the 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
Requests stream back as they land, in order, without waiting for the last URL
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
Integrations
Apache Airflow
Kubeflow
Google Cloud Vertex AI
Amazon SageMaker
AzureML
Kubernetes
MLflow
Weights & Biases
LangChain
LangGraph
CrewAI
AutoGen
OpenAI Agents SDK
Claude Agent SDK
Docker
LlamaIndex
FlowiseAI
Langflow
Dify
Agno
MCP
Claude Code
Codex
Cursor
Claude Desktop
Amazon S3
Google Cloud Storage
Google Sheets

Who should pick which

  • ML engineer building training pipelines
    Pick: Zenml

    ZenML's declarative pipeline DAG, artifact versioning, and cloud backend switching allow reproducible and scalable training pipelines.

  • RAG pipeline developer needing fresh web data
    Pick: Spider Cloud

    Spider Cloud's fast scraping API, structured output, and data connectors (S3, Supabase) directly feed RAG systems.

  • Agent developer wanting crash recovery
    Pick: Zenml

    ZenML's Kitaru durable execution provides checkpoint replay and crash recovery for agents built with LangGraph, OpenAI Agents SDK, etc.

  • Team needing massive web scraping at low cost
    Pick: Spider Cloud

    Spider Cloud's Rust engine and $0.03/1k pages pricing make it cost-effective for high-volume scraping with 99.9% success.

Frequently Asked Questions

Zenml vs Spider Cloud: which should you choose?

ZenML and Spider Cloud address different layers of the AI stack: ZenML is for orchestrating ML pipelines and making AI agents durable (via Kitaru), while Spider Cloud is for fetching web data at scale for RAG and AI agents. If you need to build reliable, reproducible ML workflows or add crash recovery to your agents, choose ZenML. If you need a fast, cheap, and reliable web scraping API to feed data to your agents, choose Spider Cloud. They can also complement each other in a broader system.

Can I use both ZenML and Spider Cloud together?

Yes. Spider Cloud can fetch web data as part of a ZenML pipeline step (e.g., for RAG), and ZenML orchestrates the overall workflow.

Does Spider Cloud offer a self-hosted option?

Yes, the core is open-source on GitHub, but the cloud version provides managed scaling and anti-bot features.

Does ZenML require coding?

Yes, pipelines are defined via Python decorators; it's not a no-code platform.

Which tool is better for LangGraph agents?

ZenML's Kitaru now provides durable execution for LangGraph agents (checkpointing and replay). Spider Cloud can be used as a data source within those agents.

Does Spider Cloud handle CAPTCHAs?

Yes, its Silk model helps solve CAPTCHAs, and the unblocker uses rotating proxies and retries.

What is ZenML's Kitaru?

Kitaru is an open-source durable execution runtime for Python agents, providing crash recovery, human-in-the-loop, and replay from checkpoints (launched April 2026).

Are failed requests billed on Spider Cloud?

No, failed requests are not billed.

Does ZenML have a free tier?

Yes, ZenML is open-source with free unlimited pipeline runs; Pro adds extra features.

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Explore each tool further

Zenml
View Zenml reviewZenml alternatives
Spider Cloud
View Spider Cloud reviewSpider Cloud alternatives

Browse these categories

Best AI Agent Frameworks & Orchestration toolsBest AI Web Scraping & Search APIs toolsBest AI Data & Analytics toolsBest AI Browser & Computer-Use Agents tools
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Last reviewed: July 3, 2026