Are your Meta campaigns learning faster but behaving less predictably than they used to? And why does creative variety seem to matter more now than targeting tweaks or manual optimizations?
Meta Andromeda has become one of the most discussed yet least understood changes in Meta advertising. It is not a new campaign type, setting, or product update. Instead, It is a major redesign of Meta’s ad retrieval system, which determines how eligible ads are selected before entering the ranking and auction process across Facebook and Instagram.
For advertisers, this matters because Andromeda upgrades the retrieval stage that determines which ads are eligible to enter the auction and ranking process.
It influences which ads are considered, how quickly the system adapts, and why automation-heavy campaigns often outperform tightly controlled setups.
This article explains why Meta rebuilt its ad retrieval system and what the newest updates mean in practical terms for advertisers.
Most importantly, it separates confirmed facts from speculation, so you can understand how this shift affects performance, testing, and strategy without chasing myths or overreacting to short-term volatility.
- Why Meta Rebuilt Its Ads Retrieval System
- How Andromeda Changes Ad Delivery Under the Hood
- The Newest Andromeda Update and Related Models
- How Andromeda Affects Advantage+ and Automated Campaigns
- What Advertisers Should Change Right Now
- Andromeda Is Not a Feature, It Is a Direction
- Frequently Asked Questions
Why Meta Rebuilt Its Ads Retrieval System
Meta did not rebuild ads retrieval to introduce a new feature for advertisers. It rebuilt it because the existing system was no longer able to scale with how advertising on its platforms actually works today.
At the core, Andromeda was designed to solve three structural problems that advertisers had already begun to feel in performance and delivery.
Explosion in Creative Volume
Creative volume inside Meta’s ad ecosystem has grown dramatically. Automation, dynamic creative, and generative tools have made it easier for advertisers to launch dozens or hundreds of creative variations at once.
Meta’s engineering disclosures explain that earlier retrieval systems relied on multiple independent models and heuristics, which became less efficient as the volume of eligible ads increased. Retrieval models were forced to rely on simpler heuristics and fragmented signals, which limited personalization early in the delivery process.
For advertisers, this often showed up as inconsistent delivery between creatives and slower stabilization when new assets were introduced.
Rise of Advantage+ and Automation
As Meta pushed more advertisers toward Advantage+ campaigns, the burden on retrieval systems increased. Advantage+ relies on broad targeting, high creative diversity, and automated decision-making across placements and audiences.
Legacy retrieval approaches were not designed for this level of end-to-end automation. They operated with more rigid stages and manual constraints, making it harder for the system to learn holistically from advertiser inputs.
Meta has reported system-level performance improvements following Andromeda’s rollout, though individual campaign learning dynamics still vary.
Limitations of Earlier Retrieval Approaches
Before Andromeda, retrieval relied on multiple independent models and rule-based filtering. This made it difficult to optimize retrieval globally across users, creatives, and objectives.
Meta has stated that this structure limited recall and personalization at the earliest stage of ad delivery. When retrieval quality is constrained, even strong ranking models later in the pipeline have less to work with.
Why Advertisers Felt the Change
As Meta transitioned to a more model-driven retrieval system, advertisers began noticing quicker adaptation to new signals and creative inputs. At the same time, campaigns became less dependent on manual targeting and more sensitive to creative quality and diversity.
In short, Meta rebuilt retrieval because the old system could not support the scale, automation, and creative volume that modern advertising now demands.
How Andromeda Changes Ad Delivery Under the Hood
To understand what Andromeda changes for advertisers, it helps to clarify where retrieval sits in Meta’s ad delivery pipeline and why this stage matters so much.
Ad delivery happens in two broad phases.
- First, Meta retrieves a subset of eligible ads from a massive pool.
- Second, ranking models decide which ads actually win the auction and appear to users.
Andromeda fundamentally upgrades that first phase.
Retrieval Becomes More Personalized, Earlier
Historically, retrieval focused on speed and scale. It reduced millions of ads down to a manageable candidate set, but personalization at this stage was limited. Much of the nuance happened later during ranking.
Meta has confirmed that Andromeda introduces a more advanced, deep-learning-based retrieval approach that incorporates richer signals earlier. This means ads entering the ranking stage are already more relevant to the individual user.
For advertisers, this means retrieval now incorporates richer personalization signals earlier, increasing the importance of differentiated creative inputs.
Fewer Hard Rules, More Model-Driven Decisions
Earlier systems relied heavily on rule-based filters and separate model stages. Those rules were necessary for performance and safety but reduced flexibility.
Andromeda consolidates multiple retrieval models into a more unified deep neural architecture, reducing reliance on fragmented rule-based filtering. Instead of rigid cutoffs, the system learns which ads are worth considering based on predicted value.
This shift explains why some advertisers experience less predictable delivery early on, especially when launching new campaigns. The system is exploring and learning faster rather than following static rules.
Better Scaling With Creative Diversity
Meta has explicitly tied Andromeda to the need to scale retrieval as creative supply grows. With automation and dynamic creative, advertisers now introduce far more variations per campaign than before.
Andromeda’s architecture allows retrieval to scale without collapsing under that volume. This benefits advertisers running many creatives, but it also raises the bar for differentiation.
What Does Not Change
Importantly, Andromeda does not replace auction mechanics, bidding strategies, or core campaign structures. Budget, bid controls, and optimization goals still matter.
What changes is which ads get a chance to compete.
Why Advertisers Notice Faster Learning Cycles
Because retrieval incorporates richer signals earlier, Meta has reported improvements in delivery efficiency at scale, though learning phase duration remains campaign-dependent. However, this also means early performance swings are more common as the system tests creative signals aggressively.
In practical terms, Andromeda makes delivery smarter, earlier, but less manual and less predictable by design.
Next, we will look at the newest Andromeda-era updates and related models, including how newer foundation models layer on top of this retrieval system.
The Newest Andromeda Update and Related Models
Andromeda is not a one-time rebuild. It is the foundation Meta is using to support a new generation of ad models that operate at a much larger scale and with deeper automation.
The most important development advertisers should understand in the Andromeda era is how retrieval and prediction models are now designed to work together, rather than as loosely connected stages.
Andromeda as Infrastructure, Not the “Update” Itself
Andromeda handles retrieval. Its job is to efficiently and intelligently select a high-quality candidate set of ads for each impression.
What has changed more recently is what sits on top of that retrieval layer.
Meta has described deploying large-scale neural models for ads ranking and recommendation that operate across formats and objectives. These models are trained on vastly more data than previous task-specific systems and are designed to transfer learning across campaigns, placements, and user behaviors.
For advertisers, this means improvements do not come from a single feature release, but from cumulative system-level upgrades.
How Newer Models Build on Andromeda
With Andromeda improving early-stage personalization and recall, newer prediction models can focus on deeper optimization instead of compensating for weak candidate selection.
Meta has stated that these foundation models are already being used to improve performance across Facebook and Instagram Feed, particularly for conversion-oriented objectives. Importantly, Meta frames these gains as aggregate system improvements, not guarantees for individual advertisers.
The key shift is that learning is no longer isolated at the campaign or ad set level. Signals are shared more broadly, which can shorten learning cycles but also reduce the impact of narrow manual controls.
Why This Matters to Advertisers
From an advertiser's perspective, this explains several observed changes:
- Performance improvements often come from creative and system alignment, not targeting tweaks
- New campaigns can stabilize faster, but early volatility is more common
- Optimization increasingly rewards scale, variety, and signal quality
It also explains why Meta continues to emphasize automation. Systems like Advantage+ are designed to fully leverage these newer models, while heavily constrained setups may limit their effectiveness.
What Meta Has Not Claimed
Meta has not claimed that these updates eliminate the need for strategy, nor that all advertisers will see identical results. Performance still depends on inputs, objectives, and market conditions.
The takeaway is simple: Andromeda enables newer models to operate effectively at scale. The “update” advertisers experience is the combined effect of retrieval, prediction, and automation working more tightly together.
Next, we will look at what Meta has publicly reported about performance impact, and what those metrics actually mean in practical terms for advertisers.
How Andromeda Affects Advantage+ and Automated Campaigns
Andromeda’s impact is most visible inside Advantage+, because Advantage+ is designed to fully rely on Meta’s automated systems rather than manual controls.
From Meta’s own disclosures, Andromeda improves the retrieval stage by selecting more relevant ad candidates earlier. Advantage+ campaigns benefit disproportionately from this because they operate with broad targeting, flexible placement, and high creative throughput.
In other words, the system has more room to learn.
Why Advantage+ Benefits More Than Manual Campaigns
Advantage+ removes many advertiser-imposed constraints. That allows Andromeda’s retrieval model to:
- Explore a wider creative set
- Match ads to users earlier using richer signals
- Share learnings across placements and objectives
For advertisers, this often shows up as faster initial learning, but also more experimentation by the system in the first days of a campaign.
Meta has repeatedly positioned Advantage+ as the campaign type best aligned with its AI roadmap, including retrieval upgrades like Andromeda and newer prediction models layered on top. This is not accidental. Automated campaigns are easier for Meta’s models to optimize end to end.
Budget Allocation and Learning Behavior
Under Andromeda, Advantage+ campaigns may shift spend between creatives and placements more aggressively than advertisers expect. This is because retrieval quality improves earlier, allowing the system to test combinations faster.
Advertisers sometimes interpret this as instability. In reality, it reflects a system that is learning sooner rather than relying on fixed assumptions.
This behavior is most noticeable when:
- New creatives are introduced
- Budgets change significantly
- Conversion signals are sparse or delayed
- Creative Supply Becomes a Bottleneck
Meta has been explicit that automation works best when creative supply is strong. Andromeda can retrieve better candidates, but it cannot invent differentiation.
For Advantage+ campaigns with limited or repetitive creatives, the system may struggle to find meaningful variation, reducing the benefit of improved retrieval.
What Advertisers Should Take Away
Andromeda does not make Advantage+ “hands off.” It makes inputs more important than controls.
Advertisers running Advantage+ should focus on:
- Supplying diverse, high-quality creatives
- Allowing sufficient learning time
- Evaluating performance over longer windows, not day-one volatility
Advantage+ is where Andromeda’s improvements are most fully expressed, but only when advertisers align with how the system is designed to operate.
Next, we will look at what advertisers should change right now to adapt to this new retrieval-driven environment.
What Advertisers Should Change Right Now
Andromeda does not require advertisers to relearn Ads Manager, but it does require adjusting how inputs are prioritized. The biggest shift is moving away from control-heavy optimization toward signal quality and creative supply.
Increase Creative Variety, Not Just Frequency
Meta’s retrieval improvements reward diversity. Advertisers should supply meaningfully different creatives, not minor edits of the same asset. Variations in hooks, formats, messaging angles, and visual structure give the system more useful options to retrieve early.
This aligns with Meta’s repeated guidance that automation performs best when creative inputs are rich and differentiated.
- Practical adjustment: Audit creatives by concept, not by format. If multiple ads communicate the same idea, they are not helping retrieval.
Loosen Over-Constraint Where Possible
Highly narrow targeting, excessive exclusions, and rigid placement restrictions reduce the system’s ability to learn holistically. Andromeda and newer models are designed to work with broader eligibility, especially in automated setups.
- Practical adjustment: Test fewer audience constraints and evaluate performance over longer windows rather than reacting to early volatility.
Expect Faster Learning, With Early Volatility
Because retrieval adapts earlier, campaigns may show more fluctuation in the first days. This is not necessarily negative. It often indicates faster exploration.
- Practical adjustment: Avoid making structural changes during early learning unless delivery fully stalls. Judge performance after stabilization, not day one.
Prioritize Signal Quality Over Micro-Optimization
Conversion signals still matter, but Andromeda makes clean, consistent signals more valuable than manual bid or targeting tweaks.
- Practical adjustment: Ensure events, attribution, and conversion tracking are reliable before optimizing creative or structure.
Align Measurement With System Behavior
Short-term swings are more common in highly automated environments. Evaluating performance too frequently can lead to unnecessary resets.
- Practical adjustment: Use rolling averages and longer attribution windows when assessing impact.
The Core Shift to Internalize
Advertisers should think less in terms of “forcing outcomes” and more in terms of feeding the system better inputs. Andromeda raises the performance ceiling, but only for accounts that align with how Meta’s ad delivery now works.
Andromeda Is Not a Feature, It Is a Direction
Meta Andromeda is best understood as a structural shift, not a tactical update. It changes how ads enter the system, how quickly learning happens, and how much influence creative quality has before ranking even begins.
For advertisers, the most important takeaway is not to look for switches to flip or settings to outsmart the system. Andromeda reinforces a direction Meta has been moving toward for years: broader eligibility, deeper automation, and models that learn faster when given the right inputs.
This explains why some manual tactics feel less effective, why creative variety now carries more weight, and why early volatility has become more common in modern campaigns. These are not signs of instability.
They are signs of a system optimizing earlier and more aggressively.
Advertisers who adapt by focusing on signal quality, creative differentiation, and patience during learning will be best positioned to benefit as Meta continues building on the Andromeda foundation.
Frequently Asked Questions
Does Meta Andromeda change how advertisers should work with agencies?
Andromeda increases the importance of creative systems and signal quality, which is why many brands rely on specialized partners offering Facebook advertising services that focus on testing frameworks rather than manual optimizations.
How does Andromeda affect Instagram-specific ad strategies?
Because Andromeda improves early-stage ad selection, Instagram-focused performance now depends more on creative variety and format alignment, an area where experienced Instagram ads agencies often concentrate their efforts.
Will Andromeda influence how ads perform on newer Meta platforms?
As Meta expands ad delivery across surfaces, retrieval improvements like Andromeda support experimentation on emerging placements, including formats tied to Meta ads on Threads.
Does the shift toward Reels connect to the Andromeda update?
Yes. As Meta standardizes video formats, including the move where Facebook videos are now Reels, retrieval systems like Andromeda help scale ad selection across unified video inventory.
How does Andromeda relate to Meta’s use of AI in seasonal campaigns?
Andromeda supports the infrastructure needed for large-scale automation, which is increasingly visible in AI-powered ads during holidays where creative volume and rapid optimization are critical.
