Checkly vs AliveMCP

Quick verdict

• Choose Checkly if: your team owns monitoring as code in the repo, you already deploy checks through CI, you need Playwright browser checks against a customer-facing dashboard, you have engineering capacity to maintain MCP-protocol assertions in TypeScript, or your fleet is small enough that hand-authored coverage is honest coverage.
• Choose AliveMCP if: the MCP-protocol layer is the surface that matters, you've been bitten by a body-substring assertion that stayed green during a real regression, you depend on third-party MCPs you don't run, you want schema-drift alerts as a first-class signal, or you want a public per-server status page out of the box without composing it from individual checks.
• Run both if: you have an existing Checkly deployment with browser checks against the dashboard and heartbeat checks against the crons, and you want the MCP-protocol layer covered specifically. At $9–$49/mo on top of Checkly this is the cheapest way to close the protocol-specific gaps the API check leaves open without taking on TypeScript-assertion maintenance.

Side by side

Detailed differences

1. Monitoring-as-code is a workflow choice, not just a feature

The biggest decision in the Checkly comparison isn't a feature — it's whether your team's monitoring philosophy is "every check belongs in the repo" or "monitoring is a managed service the platform provides." Both are defensible. Monitoring-as-code gives you reviewable diffs, history in git, the same deploy pipeline as the application, and the ability to test checks the same way you test code. The cost is that someone has to write and maintain every check, every assertion, every alert-channel binding, and every protocol-shape change. AliveMCP picks the other side of the trade: protocol coverage is the product's job, not yours, and the cost is that you don't get a TypeScript file in your repo to review. For teams that have already invested in Checkly's workflow, the natural answer is to keep Checkly for the things it's best at and use AliveMCP for the MCP-protocol layer specifically — not to replace one with the other.

2. The substring trap, in TypeScript

The shortest path to MCP coverage in Checkly is an API check that POSTs {"jsonrpc":"2.0","id":1,"method":"tools/list"} at your endpoint and asserts on a body field with JSONPath. That works the day you write it. It stops working when the regression ships in a field your assertion didn't name — for example, the day a deploy ships {"tools": []}: any assertion that doesn't include response.body.tools.length > 0 stays green, and you find out from a customer that every agent calling your MCP has been seeing zero tools for two days. AliveMCP's tool-list hash detects this as a state-change event on the next probe — there is no assertion to forget, because the canonical hash of an empty list is structurally different from the hash of a populated list. The substring problem is no longer a class of bug. A separate write-up covers why the protocol-aware layer matters for this class of failure.

3. Per-check-runs pricing scales with cadence and check count

Checkly's pricing is per-check-run, denominated as runs-per-month and tiered by check type (API runs, browser runs, heartbeat counts). The Hobby tier covers 10,000 API check runs and 1,500 browser check runs per month for one user — fine for a personal project at a 5-minute cadence on a couple of MCPs, tight at 60-second cadence on more than one. The Team tier opens at $80/mo with bigger envelopes; runs accrue at every check × every cadence × every location, so multi-region coverage of a small fleet at minute cadence climbs the meter quickly. AliveMCP is flat tiers — adding another MCP doesn't move the bill, tightening cadence doesn't move the bill, adding regions doesn't move the bill, because cadence and coverage are the product. For teams that find themselves doing arithmetic on Checkly run quotas every quarter, the flat-tier shape is its own argument.

4. Browser checks vs MCP endpoints

Half of Checkly's value proposition is the Playwright-based browser checks product. It's strong, well-documented, and the right primitive for "does the customer-facing dashboard render and let me click through the happy path." It is not the right primitive for an MCP endpoint, which has no UI to render, no DOM to assert on, and no clickable surface to script. If your team uses Checkly for browser checks against a SaaS dashboard alongside MCPs you ship — a common shape — keep using Checkly for the dashboard and use AliveMCP for the MCP. They don't compete; they cover different surfaces. Schema-drift detection requires canonicalizing the tool list and hashing the canonical form, which a Playwright check has no path to compute and an API check can only approximate by re-implementing the canonicalization in TypeScript and maintaining it as the spec evolves.

5. Where Checkly wins

The honest list: monitoring-as-code as a first-class workflow with PR review, CI deploy, and version history; Playwright browser checks for any product surface that has a UI (AliveMCP doesn't try to compete on this); heartbeat checks for cron-shaped workloads (we recommend Checkly heartbeats for the cron side of an MCP deployment regardless of which protocol monitor you choose); the TypeScript SDK and CLI for teams that want to compose checks programmatically and maintain them like the rest of the codebase; private locations for monitoring inside a VPC where AliveMCP cannot reach; the existing alert channels and integrations already configured if your team uses Checkly for the rest of the stack. If those are real requirements — and for any team running a non-trivial product alongside an MCP, several of them are — Checkly is the right answer for the part of the stack it covers, and AliveMCP is the complement that covers the protocol layer Checkly doesn't reach.

Setup-time comparison, concrete

An indie MCP author getting Checkly coverage of one MCP server, working solo, typically spends:

• ~15–30 min on the initial repo setup — installing @checkly/cli, configuring checkly.config.ts, wiring the project to the CI pipeline, picking the regions, declaring the alert channels.
• ~15–30 min on the API check itself — request body, headers, JSONPath assertions for the fields you want to verify, latency thresholds, retry behaviour.
• ~10–20 min on alert-channel plumbing — Slack webhook, email, PagerDuty integration if used, alert-policy linkage.
• Ongoing time per spec change — every MCP protocol revision, every new method you want covered, every new field worth asserting on is another PR with a TypeScript edit.

That's an hour or so for one server, plus a maintenance commitment on every protocol revision. The cost amortises across a fleet — the second MCP is mostly copy-paste — but the assertion-maintenance cost grows linearly with surface coverage.

The same author getting AliveMCP coverage typically spends ~2 minutes — pasting the public endpoint URL into the dashboard and confirming the first probe succeeds, or doing nothing at all if the server is already listed in any of the public registries. Schema validation, tool-list hashing, latency-per-region tracking, state-change eventing, and registry auto-discovery are the default behaviour with no TypeScript to author.

Alert-routing recommendation

The setup we see working for teams that already run Checkly for the broader stack:

• Checkly browser-check alerts → application on-call. The dashboard down, the happy-path login broken, the screenshot diff failing — all the customer-facing-UI signal continues to flow through Checkly's existing alert pipeline into the team's existing on-call rotation. Checkly is the right primitive and there's no reason to move it.
• Checkly heartbeat-check alerts → cron-owning engineer. The nightly partition-rotation cron didn't ping in, the hourly registry-sync job is silent — these route to whoever owns the scheduled-job layer. Checkly is the right primitive here too.
• AliveMCP MCP-protocol alerts → MCP-owning engineer. JSON-RPC handshake failures, tool-list hash diffs, registry-listed-but-unreachable third-party dependencies, protocol-version transitions — these route to the engineer or team that owns the MCP. The on-call surface stays narrow because the question being asked is specifically "is the MCP protocol responding correctly to outside callers" and the answer is yes/no, not a TypeScript assertion result that needs interpretation at 3am.

This keeps the broader on-call surface narrow and high-signal: Checkly continues to do what its API and browser and heartbeat checks do well across the application, and AliveMCP covers the protocol-specific layer that a hand-authored TypeScript check structurally cannot maintain without ongoing engineering investment.

Try AliveMCP

Try AliveMCP

Further reading

• Checkly alternative for MCP servers — full page
• MCP monitoring tool — buyer's evaluation checklist
• MCP server uptime monitoring — what it actually means
• JSON-RPC health checks vs HTTP probes
• Schema drift in MCP tool definitions
• State of the MCP Registry, Q2 2026