Athos Commerce vs. Boost AI Search & Filter: Which Engine?
Boost AI Search & Filter wins this head-to-head for most mid-market Shopify stores: self-serve stepped tiers, an App Store install, and filter-tray depth once a catalog outgrows native's roughly 1,000-SKU comfort zone. Athos Commerce wins when a merchandising team will work a quote-led console daily, priced against its January 2025 Klevu-Searchspring merger. Either engine renders the metafield facet schema you should model first.
Your profile — see how the verdict shifts
- Confidence
- Medium — Both engines are credible and both tier sheets are unverified; Athos is still integrating the January 2025 Klevu-Searchspring merger, which caps confidence in any annual commitment
- Reference scenario
- $20M–$100M GMV · 10K–30K SKUs · search in the top-3 conversion paths · agency dev bench
- As of
- August 2026
Decision at a Glance
| Your profile | Verdict | Why |
|---|---|---|
| Under ~1,000 SKUs | WAIT | Native Search & Discovery renders metafield-driven filters free at this size (July 2026 research). Neither subscription earns itself; spend on facet data hygiene instead. |
| ~1,000–10,000 SKUs | CUSTOMIZE | Model the facet schema as metafields and let native render it; payback on a paid engine runs slower below ~10K SKUs (July 2026 research). Boost's lower tiers enter when analytics show filter abandonment. |
| ~10,000–50,000 SKUs | BUY | Third-party search pays back in 6–9 months here (July 2026 research). Boost is the self-serve default; shortlist Athos when a merchandising team will run campaigns in the console daily. |
| 50,000+ SKUs, or search-led UX | DEPENDS | Athos's quote-led enterprise depth competes with an Eluma-pattern owned stack at this scale: metered or quoted fees climb with the catalog while a self-hosted stack stays mostly flat. |
What Athos Commerce vs. Boost AI Search & Filter Actually Drives
| Outcome | Impact | How it works |
|---|---|---|
| Revenue — direct | High | Shoppers who search or filter convert at a multiple of browsers; instant, relevant results move that high-intent cohort straight to product page and cart. |
| Customer experience | High | Filter trays that match how shoppers think about the product (size, fit, compatibility) collapse a 20,000-SKU wall into three clicks. |
| Data & insight | Medium | Query, zero-result, and filter analytics reveal demand you don't stock yet; the vendor holds that corpus behind export terms while you rent. |
| Operational efficiency | Medium | Merchandising rules automate the collection curation a merch team used to do by hand, weekly, forever. |
Spend ceiling: Price either engine against the revenue running through search and filters, minus what free native rendering on a clean schema already delivers. The app-versus-app delta is smaller than either sales deck implies; the schema spend is the part that compounds.
What buying enables (top apps)
- + Instant filtering and search-as-you-type at catalog sizes native rendering can't hold (July 2026 research)
- + Merchandising rules, synonyms, and boosts a merch team runs without dev tickets
- + Search and filter analytics, including zero-result reporting, from day one
- + Vendor-maintained theme compatibility through Shopify's API version cycles
What building additionally unlocks
- + A facet schema that ports between native, Boost, Athos, or an owned stack with zero re-modeling
- + Free native rendering to roughly 1,000 SKUs (July 2026 research), so early spend goes to data instead of fees
- + Structured attributes that also feed product feeds, PDP spec tables, and AI shopping surfaces
- + An Eluma-class owned stack at extreme scale, with no per-query meter
Find Your Verdict in 3 Questions
Is your catalog under roughly 1,000 SKUs?
Yes: Your verdict: WAIT — native Search & Discovery covers this size free (July 2026 research); spend on facet schema hygiene instead.
No: Go to question 2.
Will a merchandising team work the search console daily — campaigns, boosts, curated zones?
Yes: Your verdict: BUY — shortlist Athos Commerce for merchandising depth, on a short first term with the January 2025 merger priced into vendor scoring.
No: Go to question 3.
Is the facet schema modeled — structured metafields for the attributes shoppers filter by?
Yes: Your verdict: BUY — Boost; self-serve tiers, install in days, and your schema indexes straight in.
No: Your verdict: CUSTOMIZE — model the schema first (an estimated $8,000–$25,000, Deploi estimate, illustrative), then pick the engine from strength.
The TCC Scorecard — 12 Dimensions
TCC — Total Cost of Capability: what it actually costs to have this capability over three years, whichever way you get it. Each dimension is scored 0–5 for both paths. How we score →
| Dimension | Buy | Build | Why |
|---|---|---|---|
| Cost | |||
| Acquisition & implementation | Boost installs from the App Store in days and indexes existing metafields; Athos onboards on a sales-led timeline; the schema-first lane spends an estimated 3–6 weeks modeling facets before anything renders (Deploi estimate, illustrative). | ||
| Recurring fees | Boost's stepped tiers climb with catalog size and features, and Athos quotes annual contracts (both verify); native rendering stays free once the schema exists. | ||
| Maintenance & upgrades | Either vendor maintains the engine and its theme integration; the owned lane's upkeep is schema governance, because new products must arrive with facet values filled. | ||
| Switching & exit | Synonyms, merchandising rules, and analytics history rebuild by hand on any engine swap; the metafield schema ports untouched, which is the point of owning it. | ||
| Risk | |||
| Vendor risk | The category consolidates: Klevu and Searchspring became Athos Commerce in January 2025, and Boost is one specialist in a crowded field (July 2026 research). The schema outlives any vendor. | ||
| Security & compliance surface | A search engine indexes catalog and behavioral data, not payment data; the native lane adds no third-party processor at all. | ||
| Platform-deprecation exposure | Both vendors absorb Shopify's roughly six-month API version cycle for you; metafields and native Search & Discovery are first-party surfaces Shopify keeps investing in. | ||
| Value | |||
| Fit to requirement | Instant filtering, merchandising rules, synonyms, and search analytics are the requirement past ~10K SKUs; native rendering is server-round-trip and rule-light at that size. | ||
| Time to market | Boost shows results in days; Athos adds a sales cycle; the schema build runs weeks before either lane shows value. | ||
| Performance & scale | Hosted engines return instant filter results on 50,000-SKU catalogs; native filtering strains well before that (July 2026 research). | ||
| Data ownership & AI-readiness | Query, zero-result, and merchandising analytics live vendor-side behind export terms; the structured attribute data AI shopping surfaces read stays yours only if you modeled it as metafields. | ||
| Focus & opportunity cost | Search rendering is a solved rental, and the schema work is unavoidable either way; spend dev hours on the data model and rent the engine. | ||
The App Landscape
| App | Status | Pricing | Best for |
|---|---|---|---|
| Athos Commerce | Live — Klevu and Searchspring merged into Athos Commerce (Jan 2025); evaluate the combined roadmap, not the legacy brands | Quote-based | Merchandising-heavy catalogs with a team to work the console, especially legacy Klevu or Searchspring customers |
| Boost AI Search & Filter | Live — The category's best-known filter-tray and search combo on Shopify | SKU/session-tiered | Self-serve install and predictable tiers from roughly 1,000 to 50,000 SKUs |
| Shopify Search & Discovery | Native — First-party, free. Shopify's free first-party app; renders metafield-based storefront filters | Free (included) | Server-rendered filters at zero recurring cost while the catalog is small |
| Metafield facet schema (+ Eluma-pattern stack at scale) | Build lane — The durable asset either engine renders: facet attributes modeled as metafields; the self-hosted Elasticsearch pattern behind Eluma extends it at extreme scale | $8,000–$25,000 schema modeling; $35,000–$90,000 for an owned stack (Deploi estimate, illustrative) | Every catalog — the schema ports between engines |
The Build Path
- Facet schema on metafields, native render: Model the attributes shoppers filter by (size, material, fit, compatibility) as structured metafields with controlled values, and let free native Search & Discovery render them. Owned data, no subscription.
- Same schema + Boost or Athos on top: Keep the metafield model as the source of truth and rent the render: merchandising rules, synonyms, and analytics ride on data that stays yours. Deploi has shipped metafield-source filters this way without a platform swap.
- Self-hosted Elasticsearch (the Eluma pattern): Real-time sync, typo tolerance, and faceted filters on infrastructure you run — Deploi's public Eluma build. It earns its keep at marketplace scale, not before.
- Effort band
- $8,000–$25,000 for schema modeling and theme wiring (Deploi estimate, illustrative), landing in the $10–25K contact-form band; an Eluma-class owned stack runs $35,000–$90,000 (Deploi estimate, illustrative), in the $25–75K band
- Typical timeline
- 3–6 weeks for schema and native wiring; 8–14 weeks for an owned Elasticsearch stack (Deploi estimate, illustrative)
- Maintenance, honestly
- Schema governance runs ~$3,000–$6,000/yr (Deploi estimate, illustrative); an owned stack carries ~15–20% of build cost per year (Deploi estimate) for index mappings, cluster patching, and sync fixes as API versions cycle.
- What you own — and what you take on
- You own: the facet schema, the controlled vocabularies, and attribute data that also feeds product feeds, PDP spec tables, and AI shopping surfaces. You take on: data discipline, because every new SKU must arrive with its facet values filled.
3-Year Total Cost of Capability
| Buy (app path) | Build (custom path) | |
|---|---|---|
| Year 0 (setup) | $1,000–$4,000 (install, facet mapping, merch rules) | $8,000–$25,000 (schema + theme wiring) |
| Years 1–3 (recurring) | $14,000–$43,000 (stepped tiers) | $9,000–$18,000 (schema governance) |
| 3-year total | ≈$15,000–$47,000 | ≈$17,000–$43,000 |
- † All figures illustrative samples for the reference scenario — not quotes, not verified pricing.
- † Boost column: mid-tier stepped pricing held flat (illustrative). Athos quotes annually and isn't charted; ask for the three-year number in writing.
- † Build column: schema modeling plus native render and governance; the Eluma-class stack isn't charted. Three-year horizon.
What the Sticker Price Hides
On the buy path
- — Stepped tiers ratchet with catalog size and sessions, so growth raises the Boost bill without anyone deciding anything
- — Merger integration risk sits on the Athos side: legacy Klevu and Searchspring stacks face one consolidated roadmap (January 2025, July 2026 research)
- — Synonyms, merchandising rules, and analytics history rebuild by hand at any engine swap
- — Quote-led annual contracts resist mid-term downgrades; size the Athos tier to today's catalog, not the forecast
On the build path
- — The schema project stalls without governance: new SKUs arriving with empty facet values quietly rot the filter UX
- — Native rendering is server-round-trip; filter-led browsing feels the lag as the catalog grows
- — An owned Eluma-class stack carries ~15–20% of build cost per year in upkeep (Deploi estimate)
What Merchants Say
Filter-app switchers describe the same surprise twice: the tier jump lands when the catalog grows, and the merchandising rules don't come along in the export.
Post-merger nerves run through the legacy base: Klevu and Searchspring customers ask which console survives and whether renewal pricing holds.
If You Change Your Mind Later
If you bought and outgrow it
Either engine unwinds the same way: the metafield schema ports untouched while synonyms, merchandising rules, and analytics history rebuild by hand on the destination. Time the exit to a contract boundary, because Athos's quote-led annuals resist mid-term exits harder than Boost's self-serve tiers. Keep facet definitions documented outside the vendor console from day one.
If you built and want out
The schema-first lane strands nothing: facet metafields render through native today and feed Boost, Athos, or an owned stack tomorrow. Graduating to an engine is an integration project measured in days to weeks, and the modeling spend transfers in full. That portability is why the schema comes first.
When This Answer Changes
We're watching for:
- ▸ Athos integration milestones: a unified platform, forced migrations off legacy Klevu or Searchspring stacks, or repricing would each move this verdict (verify at renewal)
- ▸ Shopify raising native Search & Discovery's roughly 1,000-SKU comfort ceiling or adding merchandising rules (re-verify quarterly)
- ▸ Boost pricing or packaging changes as the category consolidates
Verdict change log:
- 2025-01-01The merger didn't change what either product does today; it changed the vendor-risk column. One roadmap, one migration path, and renewal repricing are open questions worth a shorter contract term, not a veto.
Common Questions
Is Athos Commerce or Boost better for Shopify search and filtering?
Boost AI Search & Filter wins for most mid-market Shopify stores: self-serve App Store install, stepped tiers, and filter-tray depth without a sales cycle. Athos Commerce wins when a merchandising team works the console daily and a quote-led annual contract fits your buying process. Price the January 2025 Klevu-Searchspring merger into any Athos commitment, and expect third-party payback in 6–9 months past roughly 10,000 SKUs (July 2026 research).
What does the Athos Commerce merger mean for this comparison?
Klevu and Searchspring merged into Athos Commerce in January 2025, consolidating two mid-market search vendors onto one roadmap (July 2026 research). Merchants inherit real questions: which console survives, whether legacy stacks face forced migrations, and how renewal pricing moves. None of that vetoes Athos, because the merchandising depth is real. A shorter contract term and a written migration commitment price the risk sensibly.
Do I need a search app at all before 1,000 SKUs?
No. Native Search & Discovery renders metafield-driven filters free on every plan and holds up to roughly 1,000 SKUs (July 2026 research). Spend first on the facet schema: an estimated $8,000–$25,000 of metafield modeling (Deploi estimate, illustrative) that native renders today and any engine indexes tomorrow. Add Boost or Athos when zero-result queries and filter abandonment show up in analytics.
Your Next Steps
If you're going with BUY(matches your selected profile)
- Run both demos on your own catalog export, not the vendor's sample data
- Quote Boost at your real SKU and session numbers, plus the next tier up
- Ask Athos in writing which legacy console your contract lands on and what migrates at renewal
- Keep facet definitions in metafields, not vendor-only fields, so the schema ports
- Diary a pricing re-check at renewal; current tier sheets are unverified
If you're going with CUSTOMIZE
- Inventory the attributes shoppers filter by per category: size, material, fit, compatibility
- Model them as structured metafields with controlled values; backfill by bulk operation
- Wire native Search & Discovery filters to the schema and ship
- Set the re-decision triggers: zero-result rate and filter abandonment in analytics
- Shortlist Boost and Athos only when those numbers strain
Official Docs & Sources
- Storefront search (covers the Shopify Search & Discovery app) — Shopify Help Center
- Storefront API reference — shopify.dev
Official documentation linked for verification — our verdicts and estimates are our own.
Related Decisions
Should You Build or Buy Filters & Faceted Navigation on Shopify?
Filters and faceted navigation reward a CUSTOMIZE play: own the metafield schema, rent or use native rendering.
Should You Build or Buy Site Search on Shopify?
Site search on Shopify splits by catalog size: native to ~1,000 SKUs, buy in the middle, build at big-catalog, search-led scale.
Should You Build or Buy Merchandising Rules on Shopify?
Merchandising rules are a BUY once a merch team trades collections weekly.
Should You Build or Buy a Mega Menu on Shopify?
A theme-level mega menu wins for mid-market Shopify stores with any dev capacity.
Should You Build or Buy a PIM on Shopify?
PIM on Shopify is a scale decision: metafields cover most catalogs, PIM apps win at multi-channel breadth, custom pipelines at ERP-grade complexity.
Ready to pick your search engine from strength?
We'll model Boost's tiers and an Athos quote at your real catalog size, run the schema-first math honestly, and tell you if free native still covers you. The facet schema comes first either way, and we've built both it and the owned stack before.
Contact us todayVerdict scored for the reference scenario above. Estimates are not quotes; both apps' pricing is an illustrative band, re-verified quarterly. The parent filters-and-faceted-navigation page settles schema-versus-engine for the category; this page prices the named head-to-head plus the lane it hides. Full scoring anchors: see the TCC methodology.
Read how we score these decisions (the TCC Framework). No affiliate links, no paid placement — no app vendor pays to appear here.