Quick signals
Complexity
High
Distributed SQL changes the operating model; validate data model fit, consistency expectations, and operational practices for your workload.
Common upgrade trigger
Need resilience patterns and scaling beyond single-region Postgres assumptions
When it gets expensive
Operating model changes: distributed SQL requires disciplined modeling and validation
What this product actually is
Managed distributed SQL database with Postgres-compatible interfaces, evaluated when teams need resilience and scaling patterns beyond a single-region Postgres operating model.
Pricing behavior (not a price list)
These points describe when users typically pay more, what actions trigger upgrades, and the mechanics of how costs escalate.
Actions that trigger upgrades
- Need resilience patterns and scaling beyond single-region Postgres assumptions
- Need to reduce single-region database risk
- Need a scale path where higher availability is a hard requirement (not a nice-to-have)
When costs usually spike
- Operating model changes: distributed SQL requires disciplined modeling and validation
- Not every workload benefits; cost/complexity can be overkill early
- The decision is about scale path and resilience—not just “Postgres compatibility”
- You need organizational maturity to operate the model successfully
Plans and variants (structural only)
Grouped by type to show structure, not to rank or recommend specific SKUs.
Plans
- Compute + storage - primary drivers - Pricing usually scales with compute size, storage, and traffic patterns.
- High availability - replicas/backups - Reliability features add cost but reduce operational risk.
- Governance - migrations/ops - Performance tuning and migration ownership remain your responsibility.
- Official pricing: https://www.cockroachlabs.com/pricing/
Costs and limitations
Common limits
- Distributed SQL complexity and operating model is higher than single-region Postgres
- Requires careful validation of data model, consistency, and performance assumptions
- Migration cost can be significant if chosen prematurely
- More moving parts and conceptual load than managed Postgres
- Not every OLTP workload benefits; cost/complexity can be overkill early
- Teams may underestimate the fit validation needed for distributed databases
What breaks first
- Mismatch between workload needs and distributed SQL complexity (overkill too early)
- Fit validation gaps (data model, consistency expectations, query patterns)
- Operational maturity requirements for distributed systems
- Cost predictability if you assume it behaves like a single-region database
- Migration complexity if chosen before requirements truly justify it
Decision checklist
Use these checks to validate fit for CockroachDB Cloud before you commit to an architecture or contract.
- Operational model and ownership: Define your scaling path (single region vs multi-region resilience)
- Ecosystem alignment vs portability: Identify integration gravity (identity, networking, observability)
- Upgrade trigger: Need resilience patterns and scaling beyond single-region Postgres assumptions
- What breaks first: Mismatch between workload needs and distributed SQL complexity (overkill too early)
Implementation & evaluation notes
These are the practical "gotchas" and questions that usually decide whether CockroachDB Cloud fits your team and workflow.
Implementation gotchas
- Requires careful validation of data model, consistency, and performance assumptions
- Teams may underestimate the fit validation needed for distributed databases
Questions to ask before you buy
- Which actions or usage metrics trigger an upgrade (e.g., Need resilience patterns and scaling beyond single-region Postgres assumptions)?
- Under what usage shape do costs or limits show up first (e.g., Operating model changes: distributed SQL requires disciplined modeling and validation)?
- What breaks first in production (e.g., Mismatch between workload needs and distributed SQL complexity (overkill too early)) — and what is the workaround?
- Validate: Operational model and ownership: Define your scaling path (single region vs multi-region resilience)
- Validate: Ecosystem alignment vs portability: Identify integration gravity (identity, networking, observability)
Fit assessment
Good fit if…
- Teams needing distributed SQL resilience patterns
- Systems where operational resilience and scaling path are primary constraints
- Teams where single-region database risk becomes unacceptable
Poor fit if…
- You are early-stage and a single-region Postgres is sufficient
- You want minimal complexity and fastest path to ship
- Your organization lacks the maturity to operate a distributed SQL model
Trade-offs
Every design choice has a cost. Here are the explicit trade-offs:
- Resilience and scale path → higher complexity
- Distributed SQL benefits → requires careful fit validation
- Higher availability goals → more operational maturity required
- Avoid single-region risk → accept a more complex operating model
Common alternatives people evaluate next
These are common “next shortlists” — same tier, step-down, step-sideways, or step-up — with a quick reason why.
-
Amazon Aurora (Postgres) — Step-down / single-region managed PostgresOften chosen when distributed SQL complexity isn’t justified and a managed Postgres core is sufficient.
-
Google AlloyDB for PostgreSQL — Step-down / single-region managed PostgresCompared when teams want GCP ecosystem alignment and don’t require distributed SQL patterns.
-
Azure Database for PostgreSQL — Step-down / single-region managed PostgresShortlisted when teams are Azure-first and want a managed Postgres baseline with a simpler operating model than distributed SQL.
Sources & verification
Pricing and behavioral information comes from public documentation and structured research. When information is incomplete or volatile, we prefer to say so rather than guess.