Launch Week wrap-up: everything that shipped in five days, April 2026

Meilisearch Launch Week April 2026: that's a wrap.

---

Another Launch Week done. Five days, multiple releases across scaling, enterprise security, AI, observability, and search relevance. We shipped for platform teams scaling past a single node, for enterprise buyers with an IT checklist, for developers building AI-powered search, for anyone who has stared at a slow query wondering which step was costing them latency, and for marketing teams running seasonal campaigns.

Taken together, this is the shape of a search engine built for production at scale: scalable, secure, AI-ready, observable, and flexible enough to let you shape results on your terms.

Here's what was covered last week.

Day 1, Monday: Sharding & Replication, self-serve

Sharding and replication are now available on Meilisearch Cloud, composable, and built for production at scale.

Three use cases, three reasons to care:

1. Horizontal scaling. Your dataset no longer fits on a single machine. Shard it across nodes.
2. High availability. You can't afford the blip when an instance restarts. Replicate, and fail over.
3. Geographic replication. Place instances closer to your global users to reduce response time. Available today with replication alone; combining geo-replication with sharding is possible, but optimal distribution across regions is still being worked on.

Sharding and replication compose. You can have multiple replicated shards, a replicated single-node setup, or whatever shape your workload needs. Scalability is the direction we're taking Meilisearch as a whole., as customers have been running production workloads on sharded Meilisearch for months. This release brings that capability to every team on Cloud.

→ Reach out to get set up for your workload

→ Read the full Sharding & Replication announcement

Day 2, Tuesday: Enterprise SSO/SAML, plus MFA for everyone

Enterprise-grade authentication is here, with a security upgrade for everyone else on the platform.

Enterprise Single Sign-On with SCIM-based user lifecycle management is now live. Your identity provider (Okta, Azure AD, Google Workspace, Auth0, anything SAML 2.0 or OIDC compliant) becomes the source of truth for user identity. Meilisearch controls what those users can access inside the product.

What this gets you:

• Auto-provisioning. first-time SSO users are created automatically and added to a default team. No manual invites.
• SCIM-driven lifecycle. when someone is removed in your IdP, their access is revoked in Meilisearch immediately. Tokens revoked, org memberships cleared. No stale access.
• Mixed membership. a user can belong to an SSO-enforced org and a non-SSO org at the same time. Login routing handles both gracefully.

And alongside SSO, MFA shipped for every account, free, on every plan. If your security team cares about the difference between "SSO is enforced" and "most people have MFA turned on," you now have both levers.

This is the feature your security team will want first. Now it's there before they have to ask.

→ Read the docs and reach out to configure SSO.

Day 3, Wednesday: Chat UI in your Cloud dashboard

We launched the /chat endpoint in October: a single API call that handles the full RAG workflow on top of your Meilisearch data. On Wednesday, we made it point-and-click. Open your Cloud dashboard, select an index, and the setup is done for you. The system prompt is generated from your actual documents. Guardrails are set. There's an inspector tab so you can see every tool call and raw LLM message, and an integrate page with ready-to-use code snippets for wiring it into your app.

Who this is for: anyone building chat experiences where users should get a direct answer, not a list of links. Support portals, internal knowledge bases, documentation sites, product catalogues.

→ Read the full Chat UI announcement

Day 4, Thursday: Search Performance Inspector

See exactly where your search time goes.

The Search Performance Inspector gives you a full breakdown of every query: keyword search, semantic search, filtering, facet distribution, formatting, the embedder. It's the same view our engineering team uses internally to debug customer performance issues, now in your Search Preview tab.

The headline use case: you notice half your query time is going to facet distribution, realize you don't actually need facet counts on that endpoint, and cut your latency in half. Or you catch formatting doing more work than it should and adjust your highlight and crop parameters accordingly.

Developer-facing by design. Instead of opening a support ticket with "it feels slow," you come in with the actual trace.

→ Free for all Cloud users. Try it now

Day 5, Friday: Document Joins and Dynamic Search Rules

Two beta releases, one day, both about relevance.

Experimental feature: Document Joins

You have movies. You have actors. You put them in separate indexes and link them, because you don't want to duplicate every actor's biography inside every movie document.

What you couldn't do until now: filter movies by a property of their actors. "Find all the movies where an actor is under 40 and French" meant either denormalizing your data (painful) or doing a two-step search in your application code (also painful, and fragile).

The new _foreign filter keyword changes that. You can write a filter on the movies index that references the actors index directly. Meilisearch runs the filter on the foreign index, gets the matching documents, and uses those to filter your current index, all in one query.

It also solves a subtler problem: filtering "employees named Maya with surname Shin" inside a single document that contains multiple employees can't guarantee you're matching on the same employee. The foreign filter can, because it filters on the actual subdocuments in the linked index first.

Document hydration, inlining the full actor data into your movie results, has been there. Document Joins is the filtering layer that makes the relationship fully usable in search.

Experimental feature: Dynamic Search Rules (pinning)

Relevance is great. Relevance is also not always what you want.

Sometimes you want the fridge pinned at position 2 for all of summer, because it's on promotion. Sometimes you want a specific laptop to show up when someone types "computer," because you know your customers. Sometimes you want a support article pinned at the top of results during a known product incident.

Dynamic search rules let you do that. You define activation conditions (what triggers the rule, for example "query contains 'computer'"), and Meilisearch inserts the pinned document at the exact position you specify, after the engine has done its normal ranking work. If the pinned document fails the active filters (out of stock, wrong region), it won't be inserted. If the conditions don't match, the rule does nothing, and you get organic results.

A few things worth knowing:

• Pinning works across regular, federated, and hybrid search.
• It overrides the ranking engine for that one slot. This is intentional. Guaranteeing an exact position is something pure ranking cannot do.
• It targets the whole user base, not individual users. For user-level personalization, we have a separate feature (still in beta).

This is the first step in a full merchandising toolkit. Promoting and demoting (boosting and burying) ship next.

→ Try both today - free

What's coming next

A preview of what's already in motion:

• Promoting and demoting for dynamic search rules, completing the merchandising toolkit.
• Better dashboard visibility for sharded projects: aggregated task views across nodes, unified monitoring.
• A full overhaul of the chat route: unified agent configuration, parallel multi-search, deeper guardrails, and observability.
• Richer inspector tooling in Chat UI, with more granular guardrail configuration.

→ Keep an eye on our public roadmap.

---

Browse every release

Everything from this week, in one place:

→ Meilisearch Launch Week April 2026

Thanks for following along this week. Docs and guides for every release are linked from the launch week page. Want to talk through what's relevant for your stack? Our team is here.

Happy searching.

---

Questions? Drop by our Discord or check out the documentation.