Answers that name TikTok

If you mean paid social ad accounts (Meta, TikTok, Google, etc.), I’d look first at Smartly.

smartly.io

It’s particularly suited to agencies because you can:

• Manage multiple client ad accounts from one interface rather than switching between Business Managers. Smartly specifically supports multiple Meta ad accounts in a single view. Smartly Knowledge Base
• Automate campaign and budget workflows, reducing repetitive account-by-account optimization. Smartly Smartly
• Optimize bids and budgets using predictive algorithms and automate performance actions. Smartly
• Combine reporting across channels into centralized dashboards, so you aren't maintaining spreadsheet reporting manually. Smartly
• Connect Meta, TikTok, Google, Pinterest, Snapchat, Reddit, LinkedIn and other channels. Smartly Knowledge Base Smartly Knowledge Base

What I'd choose by agency size

• 5–20 client accounts: Smartly is worth considering if you're doing serious paid-media management and want automation.
• 20+ accounts / multiple platforms: Smartly becomes much more compelling because centralized campaign management, automation and cross-channel reporting address the account-juggling problem directly.
• If you mainly need reporting, not optimization: I'd choose a lighter-weight cross-account reporting platform instead; Smartly may be more platform than you need.
• If you also manage organic social/content: Look at an agency social-management platform such as Sprout Social, Agorapulse or Sendible rather than an advertising-focused platform.

Bottom line: If your pain is specifically “I manage lots of clients' paid ad accounts and I'm constantly logging in, checking performance, changing budgets, and updating spreadsheets,” Smartly is one of the strongest fits. Smartly Smartly Knowledge Base

If you tell me how many clients/accounts you manage and which platforms (Meta, Google, TikTok, LinkedIn, etc.), I can narrow it down to the 2–3 best tools and compare pricing/features.

If by “spying” you mean legally monitoring competitors’ publicly visible ads and reverse-engineering their creative strategy, there are some very good tools now.

My picks

🥇 If I were buying one: Foreplay

foreplay.co

I'd choose Foreplay if your main objective is “show me what competitors are making, let me organize the best examples, and help me figure out what creative concepts to test.”

It's particularly useful because the problem isn't merely finding competitor ads—it's building a swipe file of hooks, formats, offers, UGC styles, CTAs, landing pages, etc. Current 2026 comparisons also put Foreplay toward the top for creative research/swipe-file workflows. Exposure

🕵️ Best actual “ad spy”: PiPiADS

pipiads.com

If you're doing DTC/e-commerce, dropshipping, TikTok, or Meta creative research, I'd look hard at PiPiADS.

The attraction is volume: instead of manually searching individual competitors, you can search/filter large collections of ads and look for things such as:

• ads that have been running for a long time
• products/brands
• creatives
• hooks and copy
• engagement signals
• landing pages
• geographic targeting
• creative variations

That makes it more appropriate when your question is “what ads are succeeding in this market?” rather than simply “what is Brand X running?”

🔎 Best broad database: BigSpy

bigspy.com

BigSpy is worth considering if you want cross-platform breadth rather than a tool focused primarily on one network.

🏢 Enterprise: SocialPeta

socialpeta.com

If you're an agency, larger brand, game publisher, or growth team doing systematic competitive intelligence, SocialPeta is the more serious option. It's designed around large-scale ad intelligence rather than just maintaining a marketer's swipe file.

Don't overlook the free stuff

You can actually get surprisingly far without paying.

Meta Ad Library is the first place I'd look for Facebook/Instagram competitors. Meta's library was specifically created to provide transparency into ads running across its platforms. About Facebook

facebook.com

For TikTok, Creative Center provides a public collection of creative examples and Top Ads. TikTok's current documentation says Top Ads can be filtered by things such as region, industry, objective and format, with performance-oriented insights. TikTok Ads TikTok For Business

ads.tiktok.com

One important 2026 wrinkle: TikTok says Top Ads is being upgraded to TikTok One, and the old Top Ads area will no longer be updated. TikTok For Business

The stack I'd actually use

If you're trying to beat competitors rather than just collect ads, I'd do:

Free research
→ Meta Ad Library + TikTok Creative Center/TikTok One

Creative intelligence
→ Foreplay

Deep ad discovery
→ PiPiADS or BigSpy

Enterprise / large-scale competitive monitoring
→ SocialPeta

And I'd judge competitor ads primarily by longevity + number of creative iterations + repeated messaging, rather than assuming an ad with lots of likes is necessarily profitable. Public ad libraries don't expose the competitor's actual ROAS.

If you tell me what you're selling (e.g. SaaS, DTC, agency, info product, app/game) and whether you're mostly interested in Meta, TikTok, YouTube, or all of them, I can narrow this down to the best 2–3 tools and what each will actually let you see.

Yes. There are several platforms now aimed specifically at automating creative testing + audience/targeting experiments at scale. The important distinction is whether you want testing infrastructure or a system that actually generates, launches, evaluates, and iterates the tests for you.

Strong options

• smartly.io — probably the strongest enterprise option. It combines creative production, campaign deployment, audience personalization, testing, and optimization across Meta, TikTok, Pinterest, Snapchat, YouTube, etc. It can generate large numbers of creative variants and connect performance data back to creative elements. Smartly Smartly
• marpipe.com — particularly good if your core problem is systematic multivariate creative testing. It can test combinations of images, headlines, CTAs, etc., rather than simply asking "A or B?" and has automated statistical analysis. Marpipe Marpipe
• motionapp.com — stronger on creative analytics and identifying why an ad won than on being the entire experimentation engine. Good when you're already producing lots of ads and need automated insight from the results.
• superscale.ai — interesting if you specifically want an agent-like testing loop: generate variants → publish → read performance → iterate. A recent 2026 comparison describes it as covering the end-to-end loop across Meta, TikTok, Instagram and Google. Superscale
• Meta Ads Manager / TikTok Ads Manager themselves — don't overlook these. Both platforms increasingly have native experimentation and automated delivery. TikTok's current Smart+ tooling, for example, supports split tests across variables including creatives, budgets, catalogs and placements. TikTok For Business

The catch with "audience variations"

This is actually the harder part.

A platform can easily create:

10 hooks × 5 images × 3 CTAs = 150 creatives

But cleanly determining whether Creative A or Audience B caused the performance difference is much harder because Meta/TikTok's algorithms dynamically allocate impressions. Recent research specifically highlights this problem: conventional two-arm ad tests can confound the creative effect with the platform's algorithmic targeting response. arXiv

So if your desired workflow is something like:

Hypothesis → generate 50 creatives → test across 10 audiences → automatically allocate budget → statistically identify winners → kill losers → create next-generation variants

then you're looking for something closer to an automated experimentation platform/agent, rather than a conventional ad-management SaaS.

If I were evaluating this for a company

I'd divide the market into three categories:

If you're asking because you're considering building a platform like this, there's a pretty interesting gap here: most products automate creative production, media buying, or creative analytics, but fewer genuinely automate the experimental design → execution → statistical attribution → next-test generation loop across both creative and audience variables. Superscale NewForm

If that's what you're exploring, I can also map out what the current platforms do vs. what an ideal "autonomous ad experimentation" platform would need to do, including the API architecture and where the defensible moat could be.

Yes. The category you’re looking for is essentially “value-based ad optimization”: connect CRM/offline revenue + customer LTV back to Meta/Google so the ad algorithms optimize for valuable customers, not just leads or first purchases.

A few tools stand out:

The closest match to your description

Northbeam Apex is particularly interesting. It takes Northbeam's first-party, multi-touch attribution data and sends it into participating ad-platform algorithms. You can define a “North Star” such as first-time revenue, returning revenue, or blended revenue, along with your attribution model/window. www.northbeam.iodocs.northbeam.io

So the conceptual flow is:

CRM / orders → customer LTV → attribution → Northbeam → Meta/Google optimization

Rather than:

Ad → cheap lead/purchase → Meta optimizes for more cheap leads/purchases

Northbeam says Apex currently supports active optimization on Meta, with additional platforms at various stages of availability. www.northbeam.iodocs.northbeam.io

If you're ecommerce

Triple Whale may actually be the more natural choice. Its Sonar Optimize sends enriched first-party conversion data to ad platforms, including new-vs-returning customer information and custom conversion events. Its documentation specifically describes using this data for campaign targeting, bidding, and delivery optimization. kb.triplewhale.com

It also has LTV/cohort analysis and customer segmentation, including syncing valuable segments to ad platforms. www.triplewhale.com

If you're talking about a B2B / lead-gen CRM

Centerflow is worth looking at. It explicitly positions itself around sending actual CRM deal/revenue values back to Google, Meta, LinkedIn, Microsoft, and TikTok, rather than simply reporting conversions. It also claims to associate future purchases with the original marketing campaign to calculate customer LTV. getcenterflow.com

That is much closer to:

Facebook lead → CRM → qualified opportunity → closed revenue → LTV → Facebook optimization

which is what you'd want if a $20 lead can ultimately be worth $2,000 but another $20 lead is worth $50.

If you tell me whether you're doing B2B lead gen, SaaS, or ecommerce—and which CRM (Salesforce, HubSpot, etc.) + ad platforms you're using—I can narrow this down to the 2–3 best options and explain exactly how the data would flow.