Stash vs Spider Cloud
Side-by-side comparison of features, pricing, and ratings
Stash or Spider Cloud: which should you choose?
| Dimension | Stash | Spider Cloud |
|---|---|---|
| Primary Use Case | Persistent memory & state for AI agents | Web data extraction for AI agents & RAG |
| Deployment | Self-hosted only (single binary, requires PostgreSQL) | Cloud API + optional self-hosted open source core |
| Storage Backend | PostgreSQL (SQL queryable memory) | Connectors: S3, GCS, Sheets, Azure Blob, Supabase |
| AI Integration | MCP server, episodes/facts/context for agents | LangChain, LlamaIndex, CrewAI, AutoGen, Dify, etc. |
| Latest News Impact | No recent news; assume classic feature set | Browser AI commands, scraper catalog with 1k+ examples |
Choose Spider Cloud if you need to feed web data into AI agents—its Rust engine, 99.9% success, and new Browser AI commands make it cost-effective for RAG pipelines. Choose Stash if you need a self-hosted memory layer for agents that persist conversations, facts, and state in Postgres—best for privacy-first or offline autonomous systems. They solve different problems: one fetches data, the other remembers it.
Stash is a shared memory your connected AI tools can recall in the next conversation.
Visit Website
Spider Cloud is a scraping, crawling, and web search API that returns clean markdown or JSON for AI agents and RAG pipelines.
Visit WebsiteWhat do real users say about Stash 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.
Stash
108 mentions across 7 sources · 58% positive — mixed (weighted across 7 sources)
Hacker News, YouTube, Product Hunt, App Store, Stack Overflow, GitHub, Lemmy
What users praise
- • PostgreSQL backend means memory is durable, queryable, and inspectable with plain SQL
- • Self-hosted single binary with no cloud lock-in satisfies privacy-first and compliance-minded teams
- • MCP server integration slots into modern agent frameworks without bespoke glue
- • Episode logging plus fact extraction covers both raw history and distilled long-term memory
What frustrates them
- • No verified community reviews for this specific tool in the collected data
- • Requires Postgres operational knowledge that many app developers lack
- • Advanced skill level and no managed option raise the barrier for small teams
- • MCP dependency ties adoption to a fast-moving, still-immature protocol
Researched Sep 14, 2026
Spider Cloud
No verifiable community signal. We scanned public discussion on Oct 22, 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 Stash, and who should pick Spider Cloud?
- Solo founder building an AI agent for researchPick: Spider Cloud
Spider Cloud's crawler can feed live web data into your agent's RAG pipeline at a low per-page cost, and the latest Browser AI commands let you automate interactions like clicking 'load more' buttons.
- Privacy-focused developer building autonomous agentsPick: Stash
Stash's self-hosted, offline-capable persistent memory in Postgres ensures no cloud dependency, perfect for sensitive environments where agent state should never leave your infrastructure.
- Team needing high-volume scraping for LLM training dataPick: Spider Cloud
With a 99.9% success rate and only $0.03 per 1k pages, Spider Cloud is cost-effective at scale, plus the 1,000+ scraper catalog speeds up implementation.
- Developer wanting SQL-queryable agent memoryPick: Stash
Stash stores episodes, facts, and context directly in Postgres, allowing you to run SQL queries on agent memory—ideal for debugging or analytics.
- Enterprise integrating web data with existing data connectorsPick: Spider Cloud
Spider Cloud's direct connectors to S3, GCS, Sheets, Azure Blob, and Supabase make it easy to pipe scraped data into your existing data lake.
Frequently Asked Questions
Stash vs Spider Cloud: which should you choose?
Choose Spider Cloud if you need to feed web data into AI agents—its Rust engine, 99.9% success, and new Browser AI commands make it cost-effective for RAG pipelines. Choose Stash if you need a self-hosted memory layer for agents that persist conversations, facts, and state in Postgres—best for privacy-first or offline autonomous systems. They solve different problems: one fetches data, the other remembers it.
Can Spider Cloud extract data from JavaScript-heavy pages?
Yes—Spider Cloud's Browser Cloud uses stealth anti-detection and its Rust engine handles dynamic content. The new Browser AI commands (Act, Extract, Observe) via WebSocket allow interaction with SPAs.
Does Stash work without an internet connection?
Yes. Stash is self-hosted, offline capable, and requires only PostgreSQL. No cloud dependency means it can run on air-gapped networks.
How does Spider Cloud price compare to other scraping APIs?
At $0.03 per 1k pages (only successful requests billed), Spider Cloud is among the cheapest. The 200 free monthly credits allow low-volume testing.
Can I query Stash's memory with SQL?
Yes—Stash stores episodes, facts, and context directly in PostgreSQL, so you can use standard SQL to analyze or filter agent memories.
Which tool integrates with LangChain?
Spider Cloud directly integrates with LangChain, LlamaIndex, CrewAI, AutoGen, FlowiseAI, Agno, Dify, and more. Stash does not list specific integrations but provides an MCP server.
Do either tools support real-time streaming?
Spider Cloud offers WebSocket-based Browser AI commands for real-time interaction (Act, Extract, Observe). Stash is not real-time; it stores state for later retrieval.
Is open source available for both?
Spider Cloud has an open-source core on GitHub but recommends the cloud API for reliability. Stash is fully open source (license not specified but free to self-host).
Can I use both tools together?
Absolutely—Spider Cloud can scrape web data and feed it into an AI agent, while Stash provides persistent memory so the agent remembers past interactions and facts.
What else are Stash and Spider Cloud compared with?
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Where can you read more about Stash and Spider Cloud?
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Analysis generated by our model pipeline from live data: