Testing · head to head
QA Wolf vs Split
QA Wolf
Testing
Managed end to end test coverage where the vendor writes and maintains your Playwright suite
- From
- On request
- Rated
- -
Split
Testing
Feature flags tied to a metrics pipeline that attributes production impact to each release, now Harness FME
- From
- On request
- Rated
- -
The short version
- Each has a real cost: QA Wolf it costs materially more than any tool licence in this category because you are paying for engineering labour, so the comparison is against a salary rather than against Playwright being free.; Split split is now a module of the Harness platform and split.io redirects to harness.io, so buying it means entering a Harness commercial relationship rather than a standalone flag vendor contract.
- They diverge on capability: QA Wolf covers Tests written for you, Split covers Feature flags with targeting.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which QA Wolf and Split actually diverge.
Identical on both: starting price (On request), pricing model (quote), free tier (No), user rating (Not yet rated), category (Testing).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in QA Wolf
- Tests written for you
- Human failure triage
- Parallel hosted execution
- Coverage targets
- Portable Playwright output
- CI and issue integration
Only in Split
- Feature flags with targeting
- Metrics impact engine
- Guardrail metrics
- Statistical significance testing
- Event ingestion
- SDKs and streaming updates
- Audit and approvals
- Harness platform integration
What people use each for
The jobs each tool is most often brought in to do.
QA Wolf
- A startup with no QA function that needs regression coverage before a release cadence moves to dailynot Split
- Engineering teams where developers currently spend a day a week fixing broken end to end testsnot Split
- Companies that tried a record and replay tool and abandoned it when maintenance overtook the benefitnot Split
- Organisations that want automated coverage but cannot recruit automation engineers in their marketnot Split
Split
- A product organisation that wants every release measured against conversion and latency by default, not only deliberate experimentsnot QA Wolf
- Running A/B tests with real statistical significance rather than comparing two dashboard linesnot QA Wolf
- Catching a regression at five per cent rollout because a guardrail metric fires before the change reaches everyonenot QA Wolf
- A team already committed to Harness for CI and CD that wants flags in the same platform and contractnot QA Wolf
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
QA Wolf
- It costs materially more than any tool licence in this category because you are paying for engineering labour, so the comparison is against a salary rather than against Playwright being free.
- An external team writes tests against a product they do not use daily, so coverage follows documented flows and misses the edge cases your own staff would have thought to check.
- Every new feature needs to be communicated to the vendor before it can be covered, which adds a coordination step to your release process that an in house suite does not have.
- Test runs happen on QA Wolf infrastructure, so testing an environment behind a corporate VPN or with strict data residency rules requires negotiation and may not be possible.
- The service assumes a reasonably stable application; a product still changing its core flows every sprint will burn the maintenance allowance quickly and prompt a repricing.
Split
- Split is now a module of the Harness platform and split.io redirects to harness.io, so buying it means entering a Harness commercial relationship rather than a standalone flag vendor contract.
- Harness no longer publishes per-module prices, so an organisation that wants only feature management cannot get a list price and must negotiate a module out of a platform bundle.
- The measurement engine needs traffic volume to reach significance, so a product with modest usage gets flags with an experimentation layer it will rarely be able to conclude anything from.
- Metric attribution depends on correctly instrumented events flowing into Split, which is an integration project in its own right; without it you have paid for an experimentation platform and are using it as a flag switch.
- As part of a larger DevOps suite the roadmap now competes with CI, CD, security and cloud cost modules for attention, so feature management is no longer the company’s single focus the way it was as an independent product.
Pricing, plan by plan
QA Wolf
On request- QA Wolf$undefined/year
- Annual contract quoted on the number of tests maintained and the coverage target agreed
- Priced as a service, not a licence, so the figure is closer to headcount than to tool spend
Split
On request- Harness FreeFree
- Harness Open Source and community support
- Intended for individuals and small teams
- Feature management capability limited at this tier
- Harness Essentials$undefined/year
- Bundled DevOps modules including CI, CD, IaC and security testing
- Standard support
- Pricing requires contacting sales
- Harness Enterprise$undefined/year
- Mix and match from 15 or more modules including Feature Management and Experimentation
- Premier support with a dedicated account manager
- Higher pipeline concurrency and organisation limits
Which should you pick?
Choose QA Wolf if
- You need tests written for you.
- You work on Web, iOS, Android.
- You also want human failure triage.
Choose Split if
- You need feature flags with targeting.
- You work on Web, API, iOS, Android, Java, Node.js, Python, Go, .NET.
- You also want metrics impact engine.
Questions people ask
- Is QA Wolf or Split better?
- Neither clearly leads. QA Wolf starts at On request and Split at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, QA Wolf or Split?
- QA Wolf starts at On request and Split at On request.
- Does QA Wolf or Split run on more platforms?
- QA Wolf runs on Web, iOS, Android. Split runs on Web, API, iOS, Android, Java, Node.js, Python, Go, .NET.
- What is QA Wolf best used for?
- QA Wolf is most often used for a startup with no qa function that needs regression coverage before a release cadence moves to daily, engineering teams where developers currently spend a day a week fixing broken end to end tests, companies that tried a record and replay tool and abandoned it when maintenance overtook the benefit, organisations that want automated coverage but cannot recruit automation engineers in their market. Of those, a startup with no qa function that needs regression coverage before a release cadence moves to daily and engineering teams where developers currently spend a day a week fixing broken end to end tests are not what Split is typically brought in for.
- What can QA Wolf do that Split cannot?
- QA Wolf covers Tests written for you, Human failure triage, Parallel hosted execution, Coverage targets. Split covers Feature flags with targeting, Metrics impact engine, Guardrail metrics, Statistical significance testing.
Answered from the vendors’ own pages
QA Wolf: Do we own the tests?
Yes. They are standard Playwright and customers can take the repository, which is the main protection against lock in here.
Split: Is Split still called Split?
It is now sold as Harness Feature Management and Experimentation after the Harness acquisition, and split.io redirects to harness.io.
QA Wolf: Is eighty per cent coverage of the whole product?
It is of the flows agreed during onboarding, not of the code. Confirm what is in scope before signing.
Split: Can I buy just feature flags?
Not from a price list. Harness publishes bundle tiers and directs single-module buyers to sales.
QA Wolf: Does this replace our QA team?
It replaces automation maintenance. Exploratory testing, release judgement and edge case design still need someone who uses the product.
Split: What does it do that a plain flag service does not?
It attributes changes in your product metrics to specific flags with statistical significance testing and alerts on guardrail metrics.
Split: Do we need a lot of traffic?
Yes. Statistical tests need volume to conclude, so low-traffic products get limited value from the measurement layer.
Related pages
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- Split vs Testsigma
- Split vs Robot Framework
- Split vs Tricentis Tosca
- Split vs WebdriverIO
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- Split vs CircleCI
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