#1UUID vs ULID vs NanoID vs Snowflake: picking an ID strategy that fits the system

What we tested: We evaluated the tools and techniques described in this article using real developer workflows. All browser-based tools on this site run locally, your data never leaves your machine.

ID schemes mostly differ in four ways: randomness, sortability, size, and coordination cost.

The choice depends on where the ID is used. Database primary keys want good insertion behavior. Public IDs want unpredictability. Distributed systems sometimes want a smaller sortable integer.

#2The Four Contenders at a Glance

Every one of these solves the same core problem, generate a unique identifier without a round-trip to a central sequence, but they optimize for different constraints. There is no universally best choice; there is a best choice for a given column.

#2The Four Axes That Actually Matter

#31. Sortability and index locality

This is the axis most teams underestimate, and it's a real performance issue, not a theoretical one. A B-tree index (Postgres, MyS/InnoDB) stays efficient when new keys are inserted at the end. Random keys like UUIDv4 scatter inserts across the whole tree, causing page splits, index fragmentation, and cache thrash. On a hot table this measurably slows writes and bloats the index.

Time-ordered IDs, UUIDv7, ULID, Snowflake, prefix the value with a millisecond timestamp, so new rows insert sequentially, the way an auto-increment integer would. This is the single strongest reason to prefer UUIDv7 or ULID over UUIDv4 for a primary key.

The flip side: time-ordered IDs leak creation time and are roughly enumerable in order. For a public identifier where you don't want people inferring "how many signups do you have," that ordering is a liability, reach for random UUIDv4 or NanoID there instead.

#32. Randomness and unguessability

If an attacker can guess the next ID, exposing it in a URL is an IDOR waiting to happen. UUIDv4 (122 random bits) and NanoID (default ~126 bits) are effectively unguessable. UUIDv7 and ULID keep 74–80 random bits after the timestamp, still unguessable in practice, but their ordering makes them enumerable in sequence, which is a different property from being individually guessable. Snowflake IDs are largely predictable and should never be your only access-control barrier.

#33. Size, bytes and characters

Size shows up in two places: storage (index size, row width, replication volume) and ergonomics (URL length, log readability).

• Snowflake wins on storage: a 64-bit integer is 8 bytes, half of a 128-bit UUID's 16, and integers make small, fast indexes.
• ULID and NanoID win on display: ULID's Crockford Base32 is 26 characters with no hyphens; NanoID defaults to 21 URL-safe characters and is tunable shorter.
• UUID is the bulkiest to display at 36 characters with hyphens, though on disk it's still 16 bytes if you store it as uuid/binary(16) rather than as text. Storing a UUID as a varchar(36), a shockingly common mistake, nearly triples its footprint and wrecks index performance.

#34. Coordination cost

• UUIDv4/v7, ULID, NanoID need zero coordination, any process on any machine generates a globally-unique value with negligible collision odds. This is why they dominate distributed systems.
• Snowflake needs each generator to hold a unique worker/machine ID. That means a provisioning mechanism (a config service, a ZooKeeper-style registry, or careful static assignment). Get two workers sharing an ID and you get silent collisions. The payoff for that operational cost is the compact, sortable 64-bit integer.

#3See them side by side

Here's what each scheme actually produces in Node.js, so the trade-offs are concrete:

import { randomUUID } from 'node:crypto';   // UUIDv4
import { ulid } from 'ulid';                // ULID
import { nanoid } from 'nanoid';            // NanoID (21 chars default)
// UUIDv7: npm install uuidv7
import { v7 as uuidv7 } from 'uuidv7';

console.log('UUIDv4 :', randomUUID());       // 550e8400-e29b-41d4-a716-446655440000
console.log('UUIDv7 :', uuidv7());           // 01942c44-8a2b-7f3e-9c12-5e3a8b7c6d5f
console.log('ULID   :', ulid());             // 01J7KX8V9W0000Z3MQ7GHPJ4K2
console.log('NanoID :', nanoid());            // V1StGXR8_Z5jdHi6B-myT

Notice the timestamp prefix on UUIDv7 (01942c44-8a2b-7f3e) and ULID (01J7KX8V9W), sort these lexicographically and they stay in creation order. UUIDv4 and NanoID have no such ordering. That single difference is the primary reason to choose one scheme over another for database keys.

#2A Decision Guide

Primary key for a new table (2026 default): UUIDv7 or ULID. You get UUIDv4's decentralization and unguessability without the index fragmentation, because the timestamp prefix keeps inserts sequential. Postgres 18 and most modern libraries generate UUIDv7 natively; ULID is a great equivalent if you want the shorter 26-char text form.

Public-facing ID in URLs where enumeration must be impossible: UUIDv4 or NanoID. No time prefix means no creation-order leak and no enumeration. NanoID if you want it short and pretty; UUIDv4 if you want the standard format and native DB type.

Distributed system that needs small, sortable integer keys and can afford worker-ID provisioning: Snowflake. High-write systems where an 8-byte sortable key materially beats a 16-byte one, and you already run coordination infrastructure.

Don't over-think low-stakes IDs. For an internal job ID or a cache key, UUIDv4 from the UUID generator is completely fine, the analysis above only pays off on hot tables and public surfaces.

#2A Note on Storing UUIDs

Whichever 128-bit scheme you pick, store it in a native binary/uuid column, not as text. A uuid type (Postgres) or binary(16) (MySQL) is 16 bytes; the same value as varchar(36) is 36+ bytes and produces a larger, slower index. For MySQL specifically, if you must store UUIDv4 as binary, functions that byte-swap the time fields help locality, but adopting UUIDv7/ULID sidesteps the whole problem because they're already time-ordered. You can inspect the exact byte layout and version bits of any UUID with the UUID generator.

#2Frequently Asked Questions

#3Should I use UUIDv4 or UUIDv7 for a database primary key?

For a new primary key in 2026, prefer UUIDv7. It embeds a millisecond timestamp in its high bits, so newly generated IDs sort in creation order and insert at the end of a B-tree index, avoiding the page splits and index fragmentation that random UUIDv4 causes on high-write tables. UUIDv7 keeps enough randomness (74 bits) to remain unguessable and requires no central coordination, so you keep every advantage of UUIDv4 while fixing its main performance drawback. The one case to still choose UUIDv4 is a public-facing identifier where you specifically do not want IDs to reveal or order by creation time.

#3What is the difference between ULID and UUIDv7?

They are conceptually almost identical: both are 128-bit identifiers with a 48-bit millisecond timestamp prefix followed by random bits, both are time-sortable, and both need no coordination. The differences are in encoding and tooling support. ULID uses Crockford Base32 and renders as a 26-character, hyphen-free, case-insensitive string that is slightly more compact and URL-friendly. UUIDv7 renders in the standard 36-character hyphenated UUID format and, importantly, is a formal IETF standard with native support in databases (Postgres, SQL Server) and standard libraries. If you want standards compliance and native DB types, choose UUIDv7; if you want the shorter text form and don't mind a third-party library, ULID is an excellent equivalent.

#3Is NanoID more secure than UUID?

Not meaningfully, both are cryptographically strong when generated from a secure random source. NanoID's default 21-character alphabet provides about 126 bits of randomness, comparable to UUIDv4's 122 random bits, and both are effectively unguessable. NanoID's advantages are ergonomic: it is shorter, uses a URL-safe alphabet by default, and lets you tune the length and character set to trade collision resistance against compactness. The important caveat for both is that the randomness must come from a CSPRNG; a NanoID or UUID built from Math.random() is predictable and unsafe for security-sensitive IDs. Generate strong random values with the token generator.

#3Why do random UUIDs hurt database performance?

Because they defeat the sequential-insert pattern that B-tree indexes are optimized for. When primary keys are random (UUIDv4), each insert lands at an unpredictable position in the index, forcing the database to split pages, rewrite index nodes, and evict useful pages from cache. On a busy table this shows up as slower writes, a larger index on disk, and more I/O. Time-ordered IDs, UUIDv7, ULID, or Snowflake, insert at the end of the index like an auto-increment integer, keeping it compact and write-efficient. A second, independent performance mistake is storing a UUID as a 36-character string instead of a 16-byte native type, which inflates and slows the index regardless of version.

#3When is Snowflake the right choice over a UUID?

Choose Snowflake when you need a small, sortable, integer key and you already run, or can afford to run, the coordination needed to assign each generator a unique worker ID. Its 64-bit integer is half the size of a UUID and produces the smallest, fastest indexes, and its timestamp prefix makes it sortable by creation time. The cost is operational: two workers accidentally sharing an ID produce silent collisions, so you need reliable worker-ID provisioning. Snowflake shines in large distributed systems (its origin is Twitter) where the storage and sort benefits of a compact integer key justify that coordination overhead; for most applications, a coordination-free UUIDv7 or ULID is the simpler win.

There is no single winner here, there's a right tool per column. Default new primary keys to UUIDv7 or ULID for sortable, coordination-free inserts; reach for NanoID or UUIDv4 on public URLs where enumeration must be impossible; and pick Snowflake only when a compact sortable integer is worth the worker-ID plumbing.

Generate, inspect, and compare real identifiers with the UUID generator, see the version bits and byte layout, and mint secure random tokens and share codes with the token generator.

Written by Rahul Jalavadiya, founder of AllDevToolsHub. All tools run locally in your browser.

#2Sources / Further reading

• IETF - RFC 9562: UUIDs (Universally Unique Identifiers)
• ULID - Specification
• nanoid - Documentation
• Twitter - Snowflake ID design