Is Postgres really enough in 2026?

Before you add Redis, Kafka, Elasticsearch, and a second datastore, ask: what problem do we have today, and what’s the simplest thing that solves it?

Early-stage teams often reach for Redis, MongoDB, Kafka, and Elasticsearch before they have anything to cache, any documents to flexibly store, or anything to search. Each service adds another deployment, another backup story, and another thing that can page you at 2 AM.

What many engineers learn (sometimes painfully) is that Postgres already covers most “day one” needs: ACID transactions, foreign keys, JSON document storage, full-text search, geospatial queries, lightweight pub/sub, and a big extension ecosystem.

Postgres also has plenty of proof it can scale (Instagram, Spotify and Reddit all run at massive scale on Postgres) but the more relevant point for most teams is simpler: you can delay adding specialized systems until you have real usage patterns to measure.

The trap of premature optimization

There’s a logic to adding specialist tools up front. We’ve all been burned by scaling surprises, and it can feel prudent to “get ahead of the curve” by adding Redis for caching, MongoDB for flexibility, and Elasticsearch for search.

In practice, many teams don’t need those systems for a while. And if you do end up needing them, it’s usually after you’ve seen real traffic, real query shapes, and real bottlenecks.

Every extra service taxes the one thing early-stage teams can’t afford to lose: velocity. More services mean more integration code, more deployment complexity, more dashboards, and more failure modes to debug.

A useful default question is:

What’s the least amount of infrastructure that solves our current problems?

In 2026, that answer is still often Postgres.

What Postgres can do (that you’re not using)

Postgres covers a surprising amount of surface area now: relational data plus jsonb, full-text search, geospatial (PostGIS), time-series extensions, vector search (pgvector), and more.

That doesn’t make it the best tool for every job. It does make it a solid default until the workload proves otherwise.

The point isn’t that Postgres replaces every other system forever. It’s that Postgres can often replace the extra services you don’t need yet, until you have evidence that you do.

Skip Elasticsearch: full‑text search and trigrams

Elasticsearch excels at large‑scale search with custom analyzers, synonyms and complex ranking. But running and syncing another cluster is a big operational cost. Postgres offers two complementary search tools:

1. Full‑text search with tsvector and tsquery: you can generate a tsvector from your text and index it with a GIN index. Queries support stemming, boolean operators and ranking via ts_rank. For example:

Ranking large result sets can be expensive, but for blogs, docs and modest datasets this works well.

2. Trigram search via the pg_trgm extension: trigrams break text into three‑character sequences and support fast substring (ILIKE) and fuzzy matching. Create a trigram index and then use LIKE or the similarity operator:

In practice, adding trigram or tsvector indexing often reduces searches from minutes to milliseconds. Use Elasticsearch or another Lucene‑based engine only when you need advanced relevance ranking, facet aggregations or multi‑node search clusters.

Skip Redis early on: unlogged tables and materialized views

Standard Postgres tables write all changes to the Write‑Ahead Log (WAL) so they can be recovered after a crash.

Unlogged tables skip WAL entirely, making write‑heavy workloads faster, but with a trade‑off: unlogged tables are truncated after an unclean shutdown and do not replicate to physical standbys. That makes them perfect for caches or scratch data you can rebuild.

Consider a simple cache table:

Benchmarks vary by hardware and dataset, but skipping WAL often makes bulk loads and updates roughly twice as fast compared with logged tables while producing far less WAL traffic.

If losing cached data on crash is acceptable, unlogged tables let Postgres stand in for Redis until you truly need Redis‑level single‑digit millisecond latency or in‑memory data structures.

Additionally, built‑in materialized views let you precompute aggregations or rollups; these act like caches inside your database and can be refreshed on demand.

Skip MongoDB: JSONB + GIN indexes done right

MongoDB’s appeal is a schemaless document model. Postgres’s jsonb provides document storage with ACID transactions and SQL semantics. Always use jsonb instead of json because jsonb stores a decomposed binary representation that can be indexed and queried efficiently.

To make jsonb queries fast you need GIN indexes. Postgres ships two operator classes:

• jsonb_ops (default) – indexes keys and values; it supports containment (@>), jsonpath (@?, @@) and key‑existence operators (?, ?|, ?&).
• jsonb_path_ops – supports a narrower set of operators (containment and jsonpath) but produces smaller, faster indexes for those operations. It does not support key‑existence operators.

Use jsonb_path_ops if your queries are mostly containment checks; stick with jsonb_ops if you frequently test for the presence of a key. You can also create targeted expression indexes on specific paths.

Example document table:

Then query by containment or key:

GIN indexes accelerate reads at the cost of slower writes. Measure your workload before sprinkling GIN on every column.

Postgres + JSONB handles “mostly relational with some flexible fields” workloads elegantly; you might choose MongoDB only when ingest rates are extremely high, you need auto‑sharding day one or your data model is truly document‑first.

Beyond the basics: hidden capabilities

Materialized views for pre‑computed aggregations

Materialized views physically store a query result. They’re useful for dashboards, leaderboards and expensive rollups that don’t need to be perfectly up‑to‑date. Materialized views don’t refresh automatically; schedule REFRESH MATERIALIZED VIEW (optionally CONCURRENTLY if you create a unique index) so readers aren’t blocked. This avoids building separate caching layers for reporting queries.

Partitioning for time‑series and large tables

Partitioning divides a logical table into child tables based on a key (commonly by date). The planner can prune irrelevant partitions, yielding faster scans and easier retention management. Postgres 14 and later support declarative partitioning and ATTACH/DETACH operations. Use partitioning for append‑only logs, event streams and high‑cardinality tables.

LISTEN/NOTIFY for lightweight pub/sub

Postgres’s built‑in LISTEN/NOTIFY is a transient signaling mechanism that lets clients subscribe to channels and receive notifications. It’s perfect for cache invalidation, nudging workers or simple internal events. Messages are not persisted and there are payload size limits, so if you need guaranteed delivery, retries or cross‑service fan‑out, use a dedicated broker like Kafka or RabbitMQ.

Connection pooling for scale

Postgres uses a process‑per‑connection model. Many languages spawn hundreds of database connections, quickly exhausting memory. A connection pooler such as PgBouncer multiplexes many logical connections over a smaller pool of physical connections, stabilizing throughput and avoiding the “out of connections” crisis long before you need a bigger database instance.

A decision framework for 2026

1. Load real data and measure. Use EXPLAIN (ANALYZE) to find slow queries, pg_stat_statements to identify hotspots and your cloud provider’s metrics to understand CPU, I/O and memory. Guessing is a recipe for premature optimization.
2. Optimize Postgres first. Before adding new services, add appropriate B‑tree or GIN indexes, rewrite N+1 queries, use materialized views, apply partitioning and set up a connection pooler. Many performance problems disappear at this stage.
3. Add specialized tools only when you’ve proven a bottleneck. Reach for Redis when you need millisecond‑level latency or specialized data structures; choose MongoDB when document writes and sharding requirements dominate; deploy Elasticsearch when search relevance and multi‑tenant clusters matter. Until then, letting Postgres shoulder the load keeps your stack simple.

When Postgres isn’t enough

There are good reasons to add other tools:

• Redis – for extremely low latency, in‑memory data structures, pub/sub semantics independent of your database tier.
• MongoDB or FerretDB – when your application is dominated by high‑volume document writes, you need auto‑sharding from day one or your data model is deeply document‑centric.
• Elasticsearch or a Lucene derivative – when advanced relevance scoring, synonyms, faceted search or multi‑node search clusters are critical.
• Dedicated message brokers – for reliable delivery, ordering guarantees or long‑lived queues across multiple services.

The point isn’t “Postgres forever.” It’s “Postgres until you can prove you need something else.”

Development velocity Is your competitive advantage

Every additional service increases operational complexity and cognitive overhead: another query model, another backup strategy, another set of alerts, another cluster to keep healthy.

If the thing slowing you down is database workflow (not query performance), this is where Xata can fit nicely.

Xata runs on vanilla Postgres and adds workflows teams often bolt on later:

• Database branching for isolated feature work and preview environments
• Scale to zero for idle branches/environments
• Built-in anonymization for safer production-shaped data in dev/test

If you’re already Postgres and your pain is “environment sprawl + safe data access,” it’s worth a look. And if you’re happy with your current Postgres workflow, this post still stands.

Postgres in 2026 – simple by default

In 2026, Postgres isn’t just a relational database. It’s a robust platform with JSONB, full-text and trigram search, geospatial queries, time-series extensions, vector similarity search, and a deep extension ecosystem.

Start with Postgres.

Measure your workload.

Optimize within Postgres.

Only when you have a concrete, measurable bottleneck should you reach for a specialist tool.

Until then: keep the stack simple and keep shipping.