MCP vs Apify vs Custom Agents: When to Use Each
By Marek Cziba — Open-source MCP Server Developer
Every team building with LLMs eventually hits the same wall: the model can reason, but it cannot do. Three solutions compete for that gap — MCP servers, Apify actors, and custom agents. They are not interchangeable. Choosing wrong costs weeks of rework; choosing right ships a working product in days.
The three options, honestly defined
MCP (Model Context Protocol) is a protocol, not a product. An MCP server exposes your tools — functions, APIs, data sources — to any MCP-compatible client (Claude Desktop, Claude Code, Cursor, and growing list of others) in a standard shape: tools, resources, prompts. You write the server once, and every client that speaks MCP can use it. The value is interoperability: one integration, many consumers.
Apify is a hosted platform of pre-built automation units called actors — mostly web scraping, data extraction, and browser automation — run and monitored in the cloud, priced per compute unit. The value is infrastructure you don't operate: proxies, browsers, scheduling, storage, and thousands of actors someone else maintains.
Custom agents are your own code: a loop that calls an LLM, executes code or API calls, checks results, and iterates. Frameworks vary, but the defining trait is that you own the control flow. The value is arbitrary complexity — multi-step workflows, business rules, state, and rollback that neither MCP nor Apify provides out of the box.
Where each one wins
MCP: you have tools, and clients are the bottleneck
MCP is the right answer when you've already built (or can build) a capability as a simple function call, and the problem is getting it into the hands of AI users across different clients.
Signals you're in this situation:
- Your team uses several MCP clients and refuses to maintain one integration per client.
- The capability is deterministic: convert a URL to Markdown, compute a hash, read an RSS feed, query a database. No orchestration needed — one call, one result.
- You want distribution. MCP registries and directories are where AI users look for tools; a well-listed server gets installed by strangers.
Cost profile: low at runtime (your code runs wherever it runs), moderate up front (one server per capability surface). Risk: you own correctness, rate limits, and auth of whatever the server wraps.
Our 16-server monorepo is exactly this shape — each server does one thing (base64, UUID v7, regex testing, website-to-Markdown conversion) with zero infrastructure. uvx hash-checksum-mcp and it's live in any client. Nothing to host, nothing to babysit.
Apify: the hard part is the web itself
Scraping looks easy until you hit rotating IPs, headless browser farms, CAPTCHAs, and HTML that changes weekly. Apify wins when your target is the open web at scale:
- You need thousands of pages, not ten.
- The site fights back (bot detection, geo-blocking, login walls).
- You want scheduled runs, dataset storage, and proxies without building any of it.
Cost profile: predictable per compute unit, but it scales with volume — a heavy crawl is real money. Risk: vendor dependency, and actor quality varies (community actors break when target sites change).
Custom code can scrape ten pages fine. It cannot cheaply replicate a proxy network and a browser farm.
Custom agents: when the workflow is the product
Build your own agent loop when the value is in the sequence, not the single call:
- Multi-step decisions with state: "gather data → validate → reconcile → draft → require human approval → publish."
- Business logic that doesn't fit anyone's API: internal rules, legacy system glue, transactional rollbacks.
- Latency, cost, or compliance constraints that forbid shuffling data through third parties.
The cost profile is inverted: highest up-front, lowest marginal cost — and full control. The risk is that you also own every failure mode: retries, evaluation, prompt drift, monitoring. An agent that "works in the demo" needs engineering to survive production.
A decision flow that actually resolves
Ask these five questions in order:
- Is the work a single, deterministic call? → MCP server. Stop here.
- Is it primarily web scraping or browser automation at scale? → Apify actor (or your own crawler only if you have unusual compliance needs).
- Does it require multi-step decisions, state, or human-in-the-loop? → custom agent.
- Do different AI clients need the same capability? → whatever the engine is, expose it via MCP.
- Are you about to write plumbing that Apify already sells? → buy it, wrap it.
Question 4 is the trap people miss: the engine and the interface are orthogonal. The most common production pattern we see is Apify for execution, MCP for delivery.
The hybrid: MCP server wrapping an Apify actor
This combination covers 80% of real-world deployments:
AI client (Claude, Cursor)
│ MCP protocol
▼
MCP server you control ← auth, validation, business rules
│ API call
▼
Apify actor ← browsers, proxies, storage, scheduling
The MCP server stays thin: it validates input (critical — the LLM will eventually pass garbage), maps results into clean tool responses, and enforces your API keys. Apify does the hostile-web heavy lifting. You get MCP's interoperability and Apify's infrastructure without owning either's downside.
We use the same shape inside the monorepo: servers wrap libraries (not platforms), but the boundary logic — validate in, normalize out — is identical.
What each one costs you
| Build speed | Runtime cost | You own | |
|---|---|---|---|
| MCP server | Hours–days | Your hosting only | Correctness, auth |
| Apify actor | Minutes (existing) | Per compute unit | Input mapping, budget |
| Custom agent | Days–weeks | LLM tokens + infra | Everything: retries, evals, drift |
| Apify + MCP hybrid | Hours | Compute + your thin server | Input validation, spend limits |
Bottom line
- MCP is how tools reach AI clients. If you have a capability, package it as MCP — that's distribution, not architecture.
- Apify is where hard web automation already lives. Buy the crawling, don't build it.
- Custom agents are for workflows where the sequence itself is your product — and you're willing to own the production engineering.
Most teams don't choose one forever. They start with the cheapest thing that works (often a hosted actor or an MCP server wrapping an existing API), and reach for a custom agent only when the workflow — not the tool — becomes the differentiator.
Source code: github.com/MarekCziba/mcp-servers — 16 MCP servers, one monorepo, full CI/CD to PyPI.
Install the flagship in one line: uvx website-to-markdown-mcp
Skills & Keywords
MCP ModelContextProtocol Apify AIAgents FastMCP Automation Scraping LLM OpenSource
Top comments (1)
Your interoperability point makes me wonder: when would you favor a managed actor exposed through MCP and invoked by a custom agent over choosing just one of the three?