Best Supermetrics Alternatives in 2026

There's a reason "Supermetrics alternatives" is one of the more searched phrases in marketing ops circles. It's not because Supermetrics is a bad product. It's because teams hit specific walls, and when they do, they start looking around.

If you're here, you've probably hit one of those walls. (If you're also evaluating ad-ops tooling more broadly, we cover a full breakdown of the best bulk Meta ad launch tools separately. It's a different category but equally relevant for performance teams.)

Why Teams Switch from Supermetrics (6 Common Reasons)

People don't wake up craving "a different connector." Something specific broke, got expensive, or stopped scaling. Usually it's one of these six things.

Reporting keeps breaking. One connector outage or a platform schema change and every dashboard goes red. You're spending Tuesday mornings firefighting instead of analyzing.

The cost stopped making sense. Supermetrics pricing is structured around sources, accounts per source, refresh frequency, and users. Once you scale to many client accounts or need faster refresh cadences, the bill grows fast.

You outgrew spreadsheets. Google Sheets is a great spreadsheet tool. It's a shaky database once you have dozens of accounts, daily granularity across long history, and ad-level breakdowns. You've probably already noticed this.

You want a warehouse-first setup. BigQuery, Snowflake, Redshift. Real governance, dbt models, reliable historical data you can actually trust. Sheets can't get you there.

You run an agency, and client reporting is the product. You don't need raw tables. You need white-label dashboards, scheduled report delivery, and client portals. That's a different category of tool entirely.

You suspect the data is "wrong." Attribution looks off, UTMs are inconsistent, numbers don't match the platform UI. You hope that switching tools will fix it. (If inconsistent UTMs are your main culprit, our guide on UTM parameters for Facebook Ads explains where the inconsistencies typically originate.)

That last one deserves a harder look.

This guide is built to solve the real job-to-be-done: get trustworthy marketing data into the place you analyze it, at the refresh speed you need, at a cost you can predict.

What Does a Successful Supermetrics Replacement Look Like?

Before evaluating any tool, set the bar. A Supermetrics replacement is genuinely working if, after switching:

• Your numbers match the source UI for agreed definitions (or you can explain why they don't).
• Dashboards don't break when a platform changes a field.
• You can backfill and retain history without manual exports.
• You can scale accounts and granularity without Sheets becoming a performance bottleneck.
• You can predict cost before you add new accounts, clients, or destinations.

If a tool can't do those things, it's not an alternative. It's just a different way to suffer.

What Is Supermetrics and What Can't It Do?

Being precise about this matters, because a lot of the confusion around "alternatives" comes from not knowing what category Supermetrics sits in.

What it is: Supermetrics is a data extraction and delivery tool. It pulls data from marketing platforms (Meta Ads, Google Ads, GA4, and dozens more) and pushes it into destinations like Google Sheets, Looker Studio, Excel, BigQuery, and Snowflake depending on your plan.

Think of it like this: ad platforms are water reservoirs. APIs are the pipe interfaces. A connector is the pump, filtration, and scheduling system. Destinations (Sheets, a warehouse, a BI tool) are the tanks you actually drink from.

What it isn't:

• An attribution system
• A BI tool (even if it feeds one)
• A single source of truth by itself
• A fix for messy tracking or naming chaos

That last point matters enormously. Many "alternatives" aren't connectors at all. They're reporting suites, ELT platforms, or marketing intelligence hubs. Comparing them like-for-like is how teams end up buying the wrong thing, building on the wrong foundation, and wondering why nothing improved. For context on what proper Facebook Ads reporting infrastructure looks like end-to-end, our dedicated guide covers the full stack.

Supermetrics Pricing in 2026: Plans and Cost Breakdown

Before evaluating anything else, it helps to be clear on what Supermetrics actually costs at your current level. Supermetrics structures plans around core destinations, number of data sources, accounts per source, users, and refresh frequency.

Here's a snapshot (as of February 2026):

Why does this matter when evaluating alternatives? Because most alternatives price differently. An alternative that advertises 20/month might cost 300/month once you translate your actual usage (accounts, refresh frequency, destinations) into their billing units. You need to do that translation before you buy. For paid social teams, understanding the Facebook Ads budget implications of adding accounts at scale is equally important. Connector costs and ad spend scale together.

How to Choose Where Your Marketing Data Should Land

The single fastest way to narrow down your options is to answer one question: where does your data need to land?

Path A: Spreadsheet-First (Google Sheets / Excel)

Choose this if:

• You need simple, accessible reporting
• Stakeholders live in spreadsheets and won't change
• You don't need massive historical tables at daily granularity

Watch out for:

• Row limits and performance degradation at scale
• Fragile manual blends that break silently
• Growing pains when you add more accounts

Path B: Looker Studio-First

Choose this if:

• You want shareable, visual dashboards
• You can work within BI-style constraints
• Templates and quick turnaround matter more than raw flexibility

Watch out for:

• Blending complexity and connector quotas
• Refresh reliability varying across connectors
• "Metric drift" where the same metric shows different values from different sources

Path C: Warehouse-First (BigQuery / Snowflake / Redshift)

Choose this if:

• You want real governance and a genuine source of truth
• You need substantial history and ad-level granularity
• You need transformations you can trust (dbt, SQL models)

Watch out for:

• Setup complexity on day one
• You must define your data models, QA processes, and consistency checks. The tool won't do it for you.
• Cost shifts from "tool subscription" to "tool + warehouse + transformations"

If you're considering warehouse-first, also think hard about multi-touch attribution vs marketing mix modeling. These are the analytical frameworks your warehouse data will ultimately need to support.

How Supermetrics Alternatives Actually Bill You

This is the section most alternatives articles skip, and it's the one that causes the most buyer's remorse.

Different vendors charge for fundamentally different "units." Translating your real-world usage into any vendor's model before you commit is non-negotiable.

If you don't map your setup into a vendor's billing unit before buying, you're essentially guessing your future bill. Don't guess. Teams that have already been running Facebook Ads at scale often have the most complex billing footprints (many accounts, high granularity, multiple destinations), so the translation exercise matters most for them.

Best Supermetrics Alternatives for Google Sheets (2026)

These are tools built for teams where spreadsheets are the primary reporting environment. They're not trying to be data warehouses or full analytics platforms, and that's a feature.

If you're pulling Meta Ads data into Sheets, one often-overlooked issue is that messy naming conventions and inconsistent UTMs create cleanup work regardless of the connector you use. Check out how to connect Google Sheets to Meta Ads Manager and how to automate Google Sheets to Facebook Ads to understand the full picture before you commit to a spreadsheet-first approach.

Coupler.io

Coupler.io is a scheduled data importer. It pulls from various sources into Google Sheets and data warehouses, with explicit row-per-run limits and configurable refresh frequencies.

Why teams pick it over Supermetrics: It's a straightforward "scheduled imports" workflow. You care about rows per run and refresh frequency, not marketing templates.

Pricing snapshot (February 2026):

One thing to calculate first: Row limits and refresh frequency are explicit, which is honest. But it also means you need to estimate your data volume carefully. Daily, campaign-level pulls across many accounts will hit row ceilings faster than you'd expect. If your primary sources are Meta and Google, knowing the Facebook Ads bulk upload workflow helps you estimate the volume you're dealing with before committing to row-limited plans.

DataSlayer.ai

DataSlayer.ai is a reporting connector commonly used for Google Sheets and Looker Studio workflows.

Pricing snapshot (February 2026, in EUR):

Note: Business tier includes destinations like Sheets, Looker Studio, BigQuery, and Excel.

Before you commit: Always verify what counts as a "connector" and what counts as a "destination" in your specific configuration. Test schema stability on your most-relied-upon sources before committing. This matters especially for Meta data. Facebook Ads creative fatigue signals are some of the most schema-sensitive data points and are frequently affected by connector instability.

Two Minute Reports

Two Minute Reports is a set of connectors and reporting workflows oriented around Google Sheets and Looker Studio.

Pricing snapshot (February 2026):

Plans include limits on connections, accounts per connector, users, and refresh frequency (up to hourly on higher tiers).

The connection-count trap: Understand what "connection" means in their model. It's often closer to "how many distinct client setups or reports you have" than "how many platforms you've connected." If you have 20 clients, each needing their own Sheets, you'll hit your connection limit faster than you think. This is a good moment to think about how to scale Facebook Ads from an operations standpoint. As you scale ad accounts, your connector usage scales with them.

Windsor.ai

Windsor.ai is a connector platform that emphasizes a large source library and BI syncs, with optional warehouse and database destinations. It uses MAR (monthly active rows) as a usage metric for warehouse volume.

Pricing snapshot (February 2026):

Windsor states 325+ data sources, and all plans include access to all data sources and destinations.

The real cost driver here: If you're sending data into warehouses, MAR overages become the real cost driver, not the plan price. Map your expected granularity and historical depth into MAR estimates before you commit. High-volume ad teams using tools that launch thousands of Facebook ads will see their MAR usage scale rapidly. Factor this in before signing a warehouse-tier plan.

Power My Analytics

Power My Analytics positions itself as a "marketing data hub" with connectors, Looker Studio reporting, and warehousing included on paid plans.

Pricing snapshot (February 2026):

The Custom tier includes BigQuery, SQL, and FTP exports, API access, and a "sovereign data warehouse" option.

The per-account math: Pay close attention to "accounts per source." If you have 30 client Meta ad accounts, that's one source but 30 accounts. The add-on math matters a lot for agencies. For an overview of how Facebook Ads for agencies actually work at scale (including the multi-account management overhead), that context shapes how you evaluate any connector's per-account pricing model.

Best Supermetrics Alternatives for Agency Client Reporting

This is where the biggest category mistake happens. If your actual need is beautiful client reports, scheduled delivery, and white labeling, you're not really replacing a connector. You're replacing Supermetrics plus all the templates and reporting workflows you've built on top of it.

That changes the comparison entirely. The tools below are reporting platforms first. They include connectivity, but their core value is the client-facing deliverable.

Agencies running paid social at scale face a double challenge: not just reporting on the data, but ensuring the underlying data is clean and structured in the first place. If you're running Facebook Ads for clients or managing multiple Facebook ad accounts, clean upstream data is what separates agencies that scale from agencies that spend every Monday in spreadsheet reconciliation.

Whatagraph

Whatagraph is a marketing reporting platform that connects data sources and produces dashboards and shareable reports.

Pricing snapshot (February 2026, in EUR):

Plans include unlimited users. "Source credits" are the primary scaling driver.

The gotcha: "Source credits" typically map to how many client ad accounts you connect. For agencies with many accounts across many clients, this can scale faster than the headline price suggests. Model it explicitly before committing.

AgencyAnalytics

AgencyAnalytics is built specifically for agencies, with client management features and 80+ data integrations.

Pricing snapshot (February 2026, billed annually):

The pricing model is binary: Per-client pricing is either perfect or brutal depending on your client mix. If you have many small, low-retainer clients, it adds up. If you have fewer large accounts with predictable billing, it works cleanly. There's no middle ground on this model.

DashThis

DashThis takes a different approach: it prices by number of dashboards, not by data source.

Pricing snapshot (February 2026, paid annually):

Unlimited integrations and data sources across all plans.

Dashboard count math: If each client needs multiple dashboards (executive summary, channel performance, creative analysis, landing pages), dashboard-based pricing climbs faster than you'd expect. Count your dashboards honestly before you assume this is cheaper.

Best Supermetrics Alternatives for Warehouse-First Setups

If your real pain is data correctness at scale, long history you can trust, joins across many sources, or scaling to dozens of accounts, you're usually heading toward an ELT approach. These tools load raw data into your warehouse; you handle the modeling.

Before getting into the tools: a warehouse-first setup is only as reliable as the data flowing into it. If you're running Facebook Ads at scale or launching bulk ad variations, the quality of your naming conventions and UTM structures at launch time directly determines how usable your warehouse data becomes.

Fivetran

Fivetran is a managed ELT platform that loads data into warehouses. It's primarily usage-based, with MAR as the core metric.

Pricing reality (from Fivetran's pricing page):

Fivetran's pricing page provides example costs to give you a sense of scale:

→ Facebook Ads: median usage 34,479 MAR / median monthly list price $17.23

→ Google Ads: median usage 88,240 MAR / median monthly list price $44.12

→ An example combination shown on the page totals approximately $549/month based on their example mix and plan assumptions.

What Fivetran won't do for you: Warehouse-first is powerful, but it forces you to own your definitions. Your ROAS table is only "true" if you've explicitly defined cost inputs, conversion windows, currency conversions, and join logic. Fivetran won't do that for you. It just gets the data there reliably. For paid social teams, understanding Facebook CBO vs ABO is one concrete example of a definition you'll need to encode explicitly in your warehouse models. Connectors don't interpret these for you.

Airbyte

Airbyte is an ELT platform with open-source roots and cloud offerings. It uses "Data Workers" as a capacity-based billing unit on Pro and Plus plans.

Per Airbyte's pricing FAQ, each Data Worker can handle multiple concurrent syncs (typically around 3), and plans are priced by capacity rather than data volume.

The trade-off with this flexibility: Airbyte's power is flexibility and control. The trade-off is that reliability becomes your responsibility. SLAs, retry logic, schema drift handling, and pipeline monitoring are all things you need to actively manage unless you're on a managed plan with strong support. Treat data pipelines like production software.

On pricing specifics: Airbyte's own pricing page focuses on the capacity model and plan comparisons. If you need budget certainty, confirm current pricing directly on Airbyte's website.

What Are Marketing Data Hubs and When Do You Need One?

This category often makes sense when you have many sources, want some data normalization and modeling built in, need exports to multiple destinations, but don't want the full DIY complexity of a warehouse-first build.

Funnel.io

Funnel.io is a marketing data hub and measurement platform, commonly used by agencies and larger marketing teams. It emphasizes data normalization across sources and multi-destination exports.

Pricing context (early 2026):

Funnel uses a "flexpoints" usage model, which makes costs scale with usage drivers. As of early 2026, Funnel closed its free plan to new users, and from February 2026, the Starter plan begins at $400/month for new upgrades. The platform reports 500+ connectors.

Flexpoints warning: If predictable pricing was your main reason for leaving Supermetrics, point-based systems can become a new version of the same surprise, just with different units. Treat this as a "model your bill first, then commit" product. The AdManage pricing model deliberately avoids this by being flat-rate. Worth benchmarking against when evaluating upstream tools alongside your connector choice.

Which Supermetrics Alternative Should You Use?

Rather than a ranked list (which would be meaningless without knowing your setup), here's a use-case-first decision guide.

If you just need Google Sheets and a handful of sources:

→ Two Minute Reports Lite or Basic: best when the "connections per client" model maps cleanly to your setup.

→ Coupler.io Starter or Business: best when you want transparent row-per-run limits and a simple scheduled import model.

If you're an agency where client reporting is the deliverable:

→ AgencyAnalytics: best when "client management + reporting" is your world and per-client pricing fits your business model.

→ DashThis: best when you want unlimited data sources and prefer pricing by number of dashboards.

→ Whatagraph: best when you want a reporting-first platform with source-credit style scaling.

If you want BI plus warehouse reliability:

→ Fivetran: best when you want managed reliability and are comfortable with MAR-style usage pricing.

→ Airbyte: best when you want flexibility and are willing to treat data pipelines as production software (monitoring, QA, ownership).

→ Windsor.ai: best when you want many connectors and BI syncs plus optional warehouse output and you understand the MAR overage model.

How to Evaluate Any Supermetrics Alternative Before You Buy

Use this when evaluating any Supermetrics alternative. If a vendor can't answer these questions clearly, that itself is meaningful signal.

1. Data Correctness and Definitions

Can you control attribution windows and conversion definitions, or does the tool just mirror whatever the API returns?

Do they normalize metrics across platforms, or will you be comparing apples to asteroids? Understanding multi-touch attribution and last-click attribution models before you switch connectors helps you ask the right questions here.

Can you control timezone and currency handling explicitly?

2. Freshness and Reliability

What's the actual refresh frequency, and is it per connector, per destination, or global? (Plans often say "hourly" but mean something narrower.)

Do they have retry logic and backoff for platform outages? Do they send pipeline health alerts? For Meta-specific pipeline issues, check whether the vendor handles the Meta Ads API rate limits gracefully. It's one of the more common sources of silent data gaps.

How do they handle API throttling from the source platforms?

3. History and Backfills

How far back can you pull, and what does a backfill cost in both money and time?

Do schema changes force full re-syncs?

4. Governance

Roles and permissions (critical for agencies). Audit logs. SSO, SCIM, and access controls if you're at the enterprise level.

5. Cost Predictability

The most important question is this: "If we add 20 more client accounts next quarter, what happens to the bill?"

To answer that, you need to map:

• Number of platforms (sources)
• Number of client accounts per platform
• Granularity you need (daily vs hourly vs ad-level)
• Refresh frequency required
• Destinations (Sheets + Looker Studio + a warehouse can count as multiple billing events)

If you can't answer this question for a vendor, you don't know your real cost. This is especially pressing for agencies. See how the Facebook Ads agency model maps onto connector billing to understand the cost unpredictability risk.

Why Switching Connectors Won't Fix Messy Reporting Data

Most teams treat reporting problems as downstream. They're often upstream.

Here's what we see constantly: teams spend weeks evaluating and migrating to a new connector, get everything set up cleanly, then discover the same numbers look wrong. Attribution is still off. Campaign groupings still don't make sense in Looker Studio. The warehouse tables are consistent, but the data inside them is a mess. If you've been experiencing Facebook Ads not delivering or unexplained performance drops, the culprit is often this same upstream inconsistency rather than the connector itself.

The culprit isn't the connector. It's what was flowing through the old one.

If you're bulk-launching ads across platforms and geos, the biggest reporting killers are:

• Inconsistent naming conventions across campaigns and ad sets
• Inconsistent UTMs that make GA4 and any attribution model unreliable
• Inconsistent creative identifiers that make it impossible to track performance at the asset level
• "One-off" exceptions that slowly become the real system

A connector can only import what exists. If your campaign naming is messy, your reporting layer becomes a permanent cleanup operation. If your UTMs are inconsistent, every warehouse model becomes a reconciliation nightmare.

Getting this right starts with understanding ad creative naming conventions and UTM parameters for Facebook Ads. Those are the two pillars of clean upstream data for any reporting stack.

For campaigns specifically, a solid Facebook ad naming convention framework makes the difference between dashboards that answer questions and dashboards that generate more questions.

This is exactly where AdManage fits into the picture.

AdManage is a bulk ad-ops tool built for teams launching hundreds or thousands of ad variations on Meta, TikTok, Google Ads, Pinterest, Snapchat, and AppLovin. One of its core functions is enforcing naming conventions and UTM structures at the moment of launch, so what flows into your data pipeline is clean from the start.

When you launch a batch of 500 ad variations through AdManage, every ad gets a consistent name, a correctly structured UTM, and a properly formatted identifier. No "someone forgot to add the UTM" issues. No "this campaign was named differently last month" surprises. The data that lands in your connector, warehouse, or reporting platform is already structured the way you need it.

That means whether you switch to Fivetran, Coupler.io, AgencyAnalytics, or anything else: the cleanup work you'd normally do in your reporting layer has already been done upstream.

AdManage is used by performance teams at Veed, Photoroom, Speechify, Calm, and others. AdManage pricing is flat-rate: £499/month for in-house teams (3 ad accounts) and £999/month for agencies (unlimited ad accounts), with no per-spend tax.

If your reporting has naming or UTM inconsistencies, fix that upstream first. AdManage is worth evaluating before you finalize any data pipeline decision.

See AdManage pricing →

How to Switch from Supermetrics: 6-Step Migration Plan

This is the part most alternatives articles skip. It's also the part that costs you the most if you get it wrong.

Step 1: Inventory Everything Supermetrics Is Currently Powering

Before you touch anything, build a clear list:

• Which destination does each connection feed? (Sheet, Looker Studio, warehouse)
• Which data sources does each connection use?
• How many accounts per source?
• What's the refresh schedule?
• Which dashboards depend on each dataset?
• Are there custom blends, calculated fields, or cross-source joins?

If you skip this, you'll discover "silent dependencies" mid-migration, after things have already broken. Knowing how many Facebook Ads you should run at once is a useful baseline for this inventory phase. It frames how complex your current setup actually is.

Step 2: Decide Whether You're Actually Staying Spreadsheet-First

Be honest with yourself here. If your data involves many accounts, daily granularity across long history, or ad-level breakdowns, you're already pushing spreadsheets past what they're built for.

A warehouse-first approach is often less work long-term even if it feels like more work on day one. Don't rebuild a fragile spreadsheet setup just because it's familiar. If you're managing dozens of ad accounts and wondering whether Sheets can actually keep up, see our guide on how to manage multiple Facebook ad accounts. The operational complexity covered there maps directly onto the data complexity you'll face in your reporting stack.

Step 3: Rebuild One Pipeline End-to-End (The Pilot)

Pick one dashboard that genuinely matters and rebuild it completely in the new tool:

• Pull the same sources
• Use the same time range
• Apply the same breakdowns
• Calculate the same metrics

Then compare against your existing data:

• Totals by day
• Totals by campaign
• Totals by platform

If numbers differ, don't guess. Trace it: timezone differences, currency conversion settings, attribution windows, or filters (brand vs non-brand, placements, device types). Understand every discrepancy before you move forward.

This is also the moment to audit your upstream data quality. Are your campaigns following a Facebook ad naming convention? Are UTMs applied consistently? If you're organizing Facebook Ads in a structured way, reconciliation becomes dramatically easier during migration.

Step 4: Build QA Checks So Failures Don't Go Unnoticed

The minimum viable QA layer:

• Freshness check: The latest date present in your data is within the expected range.
• Completeness check: Row count doesn't drop to near zero unexpectedly.
• Consistency check: Spend totals are within an acceptable tolerance when compared against the platform UI.

If you're warehouse-first, run these as daily scheduled jobs. If you're spreadsheet-first, dedicate a "QA" tab that stakeholders can glance at. This is especially critical when you're running Facebook Ads for clients. They will notice data discrepancies faster than you will.

Step 5: Run Both Pipelines in Parallel for Two Full Refresh Cycles

Don't cut Supermetrics off until:

• Your new pipeline has survived at least two consecutive refresh cycles without issues
• You've validated the numbers after each refresh
• The stakeholders who rely on this data have confirmed "looks right"

Two cycles isn't a lot. But it's enough to catch one-off failures vs. systemic problems. Keep an eye on ad-level Facebook Ads learning phase behavior during this window. Platform algorithm signals can look like pipeline data errors if you're not careful.

Step 6: Cut Over and Decommission Cleanly

Once you cut over:

• Archive the old Supermetrics datasets (don't delete immediately)
• Document the new pipeline: what pulls from where, at what frequency, to what destination
• Set clear owners and escalation paths for when things break

The documentation step isn't optional. Pipelines that aren't documented will break silently and stay broken. If you're structuring a media buying team that owns this pipeline, how to structure a media buying team covers how to assign ownership so someone is always accountable when data breaks.

Common Questions About Supermetrics Alternatives

What's the cheapest Supermetrics alternative in 2026?

It depends entirely on how many accounts you have per platform and how often you need to refresh. Two Minute Reports shows a $9/month tier in its plan comparison table, but you need to verify that your required connectors, account counts, and refresh cadence actually fit that tier before assuming it's your price. If budget is the core concern, also think about upstream savings: using AdManage to standardize your ad data at launch reduces the time spent cleaning data downstream, regardless of which connector you use.

What's the best alternative if I'm moving to BigQuery?

For managed reliability and usage-based pricing that scales predictably, Fivetran is the go-to choice for warehouse-first setups. If you want more flexibility and are comfortable treating data pipelines like production software, Airbyte is a serious option. If you want many connectors plus optional warehouse output, Windsor.ai is another path, but be sure to model MAR overages before committing.

What if I just need client-facing reports, not raw data tables?

Then you're not replacing Supermetrics. You're replacing Supermetrics plus all the templates and reporting workflow you've built on top of it. Tools like AgencyAnalytics, DashThis, and Whatagraph are purpose-built for that, and they'll get you much further than a connector alone.

Is Supermetrics still worth it in 2026?

If you want a mature, marketing-focused connector with clear plan limits around sources, accounts per source, users, refresh cadence, and core destinations, Supermetrics is still a straightforward option for many teams. The key is making sure your actual usage fits the plan limits before you scale. The jump from one tier to the next can be significant.

Do I need to fix my naming conventions before switching connector tools?

Yes, and this is probably the most underappreciated step in any reporting migration. If your UTMs are inconsistent or your campaign naming doesn't follow a reliable structure, every tool you switch to will import those inconsistencies faithfully. Fixing it upstream (at the point of ad launch) is far more efficient than trying to clean it up in your reporting layer. AdManage is specifically built to enforce this standardization at launch time, which makes any connector you pick downstream work better. See our guide on UTM parameters for Facebook Ads to understand the full scope of what needs to be standardized.

How long does a Supermetrics migration usually take?

Realistically, a single pipeline pilot (one dashboard, end-to-end) takes one to two weeks if you're being thorough. Full migration across all dashboards can take four to eight weeks for teams with many dependencies. The most common mistake is rushing the parallel-running phase (Step 5 in our migration plan). Those two refresh cycles catch problems you'd otherwise discover in production. Teams using Facebook Ads automation tools alongside their data pipeline find the process faster because their upstream data is already structured consistently.

What's the difference between a connector tool and a reporting platform?

A connector tool (like Fivetran, Coupler.io, or Supermetrics itself) pulls data from source platforms and puts it somewhere else. A reporting platform (like AgencyAnalytics, DashThis, or Whatagraph) includes connectivity but is primarily designed to create finished client-facing reports. The distinction matters because if you buy a connector when you need a reporting platform, you'll end up building templates yourself on top of raw data. If you need a deeper look at reporting options specifically for Meta, see our breakdown of the best Facebook Ads reporting tools and how a Facebook Ads dashboard should be structured.

About the Pricing Data in This Guide

All pricing and plan-limit details in this article are based on vendor pricing pages as of February 26, 2026. Pricing models and limits change frequently, especially for usage-based products, so treat these as a current snapshot and verify directly before purchasing.

Ready to launch cleaner campaigns that flow into cleaner data from day one? See AdManage pricing →