Fuzzy Matching Two CSV Files Online in 2026: We Tested 7 Tools on 2,050 Rows

Why we ran this

Matching two files is easy when both contain the same reliable ID.

Real data rarely works that way.

A customer may appear in one file as:

No value is identical across every field, but the records clearly describe the same customer.

This is where fuzzy reconciliation comes in. Instead of looking for exact values, it compares several fields and identifies records that are similar enough to represent the same person or company.

We wanted to know which online tools could complete that job accurately without requiring formulas, code, or a full data-cleaning implementation.

The dataset

We created two synthetic CRM exports representing the same customer database at two different points in time:

• File A: 1,000 customer records
• File B: 1,050 customer records
• Known overlap: 950 customers
• Only in File A: 50 customers
• Only in File B: 100 customers

The files contain first name, last name, company name, email, phone, country, city, plan, lifecycle stage, account owner, and revenue and activity fields.

The matching test used seven identity fields:

first name + last name + company + email + phone + country + city

The overlapping records were deliberately changed between the two files. Variations included shortened or misspelled names; initials instead of full first names; company suffix changes such as Inc versus Incorporated; abbreviated company names; changed email formatting and domains; different phone numbers; and punctuation and spacing differences.

For example:

An exact join would not connect these rows. A useful fuzzy-matching tool should.

Each record also contained a hidden customer_id. We did not use it for matching. It was used only after each run to check whether the returned pairs were correct.

How we tested the tools

We searched for browser-based tools that claimed to match similar records across two CSV or Excel files.

For every tool, we:

1. Uploaded the same two files.
2. Selected the same relevant columns wherever the tool allowed it.
3. Applied all useful preprocessing options available.
4. Tested multiple similarity thresholds.
5. Reported the best result we could produce.

The tools use different matching algorithms and scoring systems, so the best threshold was not necessarily the same across products. We did not force one universal setting.

The goal was to give every tool the best reasonable chance of succeeding—not to catch products using bad default settings.

Clean and MergeItAI also allowed us to test the configuration on a sample before committing to the full run.

Prices are those required to complete this 2,050-row test at the time of publication.

Results

Only three products completed the full workflow in a way that could be scored consistently.

*Datablist also offered a $20-per-month price with annual billing when tested.

On this dataset, Clean recovered the most correct matches, required the least setup, and was the least expensive way to complete the job.

Clean

Clean found 949 of the 950 known pairs.

It was the only tool in the full test that:

• required no account;
• charged a one-time file-processing fee rather than a subscription;
• recommended the operation and matching columns automatically;
• supported all seven selected matching columns;
• included token sorting prior to matching;
• included company-suffix removal; and
• handled fuzzy reconciliation, exact joins, file differences, and single-file deduplication in the same interface.

The full test cost $1.99.

Clean first samples the files and recommends what it thinks the user is trying to do. It then proposes the columns, preprocessing settings, and similarity threshold.

The user can review or change those choices before processing the full files.

This matters because many people know the result they need but do not know whether the correct operation is a fuzzy match, exact join, file comparison, or deduplication.

Clean removes that decision from the beginning of the workflow while still leaving the final choice with the user.

MergeItAI

MergeItAI found 902 of the 950 known pairs.

It had a more focused workflow than a general data platform and allowed us to test the configuration on a sample before paying for the complete run.

However, it allowed only two columns to be selected for matching. Our records contained useful identity information across seven fields, so this restriction limited how much evidence the matcher could use.

That is likely one reason it recovered fewer correct pairs than Clean.

Completing the test required an account and a $9 monthly subscription. The plan included up to 5,000 processed rows, while this test used 2,050.

MergeItAI may be suitable for users who expect to run several jobs within the subscription period. For a single reconciliation task, the subscription creates a higher entry cost than Clean's per-file pricing.

Datablist

Datablist found 879 of the 950 known pairs.

It is a broader data-management platform rather than a dedicated two-file matching utility. Its plans include other capabilities such as data enrichment and list management.

That breadth can be useful for teams looking for an ongoing data workspace. It also means the reconciliation workflow involves more setup, navigation, and configuration than Clean.

The interface felt heavier than the other two products, but it is serving a different kind of customer: someone building a broader data-cleaning or enrichment process rather than someone who only wants to match two files and download the result.

Completing the test required an account and a subscription costing $25 per month, or $20 per month with annual billing.

Datablist remains a reasonable option when its other platform features are also valuable. For this isolated matching task, it was less accurate and considerably more expensive than Clean.

Why Clean outperformed

Clean is powered by Similarity API, a matching engine built specifically for fuzzy record matching across large, messy datasets.

That foundation gives it several advantages in this workflow.

It uses more evidence

Clean can combine similarity across several selected columns into one match decision.

A shortened first name may be weak evidence by itself. Combined with a similar surname, company, email, and location, it can become a very strong match.

MergeItAI's two-column limit meant it could not use all the relevant information available in this test.

It includes matching-specific preprocessing

Clean was the only tested product to expose both:

• token sorting, which helps when the same words appear in a different order; and
• company-suffix removal, which prevents differences such as Inc, Incorporated, LLC, and Limited from dominating the result.

Case handling was available in the other full-test tools as well.

These options are small individually, but they matter when thousands of slightly inconsistent records are being compared.

It is designed around this exact job

Datablist is a broader data platform. MergeItAI uses a subscription and tiered processing model.

Clean focuses on getting a user from two files to a reviewed, downloadable result with as little setup as possible.

You do not need to create a workspace, answer a long series of onboarding questions, or purchase unrelated enrichment services.

It recommends what to do

Clean's AI agent inspects the files and recommends the appropriate operation, the columns to compare, useful preprocessing, and the initial matching settings.

The recommendation can be changed, but the user does not need to understand fuzzy-matching terminology before starting.

This reduces the risk of configuring the wrong operation or excluding an important field.

It also makes Clean useful when fuzzy matching turns out not to be the correct solution. The same tool can instead perform an exact join, identify changes between file versions, or remove duplicates from one file.

How to fuzzy match two files with Clean

1. Upload both CSV or Excel files.
2. Review Clean's recommended operation, columns, and matching settings. Change anything you want.
3. Run the match and review the sample results.
4. Download the matched and unmatched records. Voilà—you are done.

No signup, formulas, code, or technical terminology is required.

Conclusion

Fuzzy matching two files is not just a matter of comparing similar words.

A useful reconciliation tool needs to consider evidence across several fields, separate genuine matches from coincidental similarities, preserve unmatched records, and make the result easy to review.

Of the seven tools we investigated, only three completed a directly comparable multi-column reconciliation workflow.

Clean produced the strongest result by a meaningful margin:

• Clean: 949 correct matches
• MergeItAI: 902 correct matches
• Datablist: 879 correct matches

It was also the least expensive option and required the least setup.

That does not make the other products universally poor choices. Datablist offers a much broader platform, while MergeItAI packages several runs into a subscription.

But when the job is simply:

"I have two messy files. Match the records and show me what belongs together."

Clean was the most accurate, direct, and affordable tool we tested.

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