Algolia vs Typesense vs Meilisearch vs Postgres Full Text

What you actually need to know

• Postgres full text is the cheapest option and works for most early stage products.
• Typesense and Meilisearch are the right middle ground for teams that want better search without paying the Algolia bill.
• Algolia is the right call when search is core to the product and the budget allows.
• All four now support some form of vector or hybrid search in 2026.
• In my experience, the search engine decision is reversible if the application layer is abstracted.

The core argument

Search is the feature founders most often underestimate at the start and most often regret underbuilding at scale. The lesson I have learned across client projects is that you do not need the best search engine on day one. You need a search engine that is good enough today and that you can swap out in three weeks if the workload demands it.

This makes the question less about which engine is best in the abstract and more about which engine fits your current stage. A consumer app with a thousand products can run Postgres full text and never think about search again. A B2B SaaS with millions of records and search as a key UI surface needs something purpose built. A marketplace with rich faceted search and a strong consumer experience may need Algolia for the polish alone.

The trap I see most often is teams that pick the most powerful option on day one because a conference talk made it sound necessary. They pay a thousand dollars a month for a search engine that is searching twelve thousand records, when Postgres would have done it for nothing. The opposite trap is teams that stay on Postgres too long and let the search experience degrade until customers complain.

The right move is to build a clean search abstraction at the application layer, ship Postgres or Typesense by default, and revisit the decision when the workload changes. The abstraction is what makes the decision reversible.

When each one wins

Postgres full text wins when. The catalog is under a million records. The search complexity is straightforward keyword and basic ranking. The team has limited capacity for search engineering. The infrastructure is already running Postgres.

Typesense wins when. The catalog is between one and ten million records. The team wants better search than Postgres provides without the Algolia bill. The team has the operational capacity to run a small additional service. The product needs faceted search and typo tolerance out of the box.

Meilisearch wins when. The team prefers a slightly different ergonomic and an opinionated default set of search behaviors. The differences from Typesense are real but small.

Algolia wins when. Search is a core competitive feature. The team wants zero search engineering. The budget supports the bill. The product is consumer facing with high search volume and high stakes on conversion.

What it actually costs

Numbers above are based on published 2026 pricing and what I see in client client engagements. The exact bill will move based on usage shape. The gap between the open source options and Algolia widens as scale grows.

Features to demand from your search layer

• A clean application abstraction. The product code should not know which engine is running.
• A reindex pipeline that can rebuild the search index from the source of truth on demand.
• Telemetry on query latency, zero result rate, and click through. Search without these is invisible.
• A way to handle typos and synonyms. Out of the box for the dedicated engines. Manual work for Postgres.
• A way to integrate vector search later. All four support it now, but the implementation varies.
• A clear answer to faceted filtering. Faceting in Postgres is doable but heavy.

Expert opinion

The search engine question is rarely the question founders think it is. The real question is how much engineering you want to own. Algolia removes the engineering. Postgres maximizes it. The middle two split the difference. Pick the one that fits the team you have, not the team you wish you had.

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Yashveer Singh, founder of Yashveer Labs

How this plays out in practice

On a client B2B SaaS with about three hundred thousand records, we shipped on Postgres full text. The product launched, the search worked, customers were happy. Eighteen months later, the catalog had grown to two million records and customers were complaining about latency. We migrated to Typesense in eleven working days because the application layer had been abstracted from day one. The search experience improved noticeably and the cost increase was less than a hundred dollars a month.

The opposite story is a project that started on Algolia at fifty thousand records. The monthly bill was four hundred dollars. The team complained about cost for a year before deciding to migrate. The migration to Typesense took six weeks because the application code was tightly coupled to the Algolia SDK. The migration cost more in engineering time than two years of Algolia bills.

For more on the search topic, see SaaS search at scale and the search problem why adding it late always hurts. For the vector search side, vector databases compared.

Common mistakes teams make

1. Starting with the most powerful engine when the workload does not need it.
2. Staying on Postgres until customers complain. The right time to migrate is just before the complaints.
3. Tight coupling between application code and the search SDK. Abstract the boundary.
4. No reindex pipeline. When the schema changes or the index gets corrupted, the team has no clean recovery.
5. No telemetry on search. The team has no idea which queries are slow or returning no results.
6. Ignoring vector search until customers ask for hybrid behavior. The infrastructure is there now, build for it.

Where to start, a 14 day plan

1. Day one and two. Define your search abstraction. The interface the product code talks to. Implement the Postgres backend first.
2. Day three to seven. Ship the Postgres implementation. Measure latency, zero result rate, and click through.
3. Day eight to ten. If the metrics are clean, stop. You do not need another engine.
4. Day eleven to fourteen. If the metrics are not clean, prototype Typesense or Meilisearch on the same abstraction. Run both side by side. Pick the winner.

For deeper reading, Postgres performance at scale covers what to do before moving off Postgres for any reason, and the boring API covers the philosophy of why simple, swappable infrastructure beats clever locked in choices.