Stateful and stateless describe where an application keeps the context it needs to work correctly. A stateful component depends on information retained between requests, jobs, or connections. A stateless application node can handle the next request without relying on memory or files stored on that specific server.
Almost every useful system has state somewhere. The architecture decision is whether that state is tied to one application node or placed in a shared, durable service designed to own it.
For most cloud applications, the practical target is hybrid: stateless web and API nodes supported by intentionally stateful databases, queues, caches, and storage services.
Stateful vs stateless applications at a glance
| Decision factor | Stateful component | Stateless application node |
|---|---|---|
| Request handling | May depend on previous activity | Each request can reach any healthy node |
| State location | Local memory, disk, or dedicated data service | Request or shared external service |
| Server affinity | May require a particular node | Should avoid node affinity |
| Horizontal scaling | Requires state coordination | Usually simpler |
| Failure recovery | Restore, fail over, or reconcile state | Replace the node and resume traffic |
| Common examples | Databases, queues, persistent caches, game sessions | Web servers, APIs, reverse proxies, many workers |
| Main concern | Durability, consistency, ordering, recovery | Dependency availability and externalized state |
A useful rule is:
Keep state in the component responsible for durability and continuity. Keep application nodes replaceable when scaling and recovery matter.
Application state is information the system must remember
Application state includes any information required after the current operation ends.
Examples include:
- user sessions and shopping carts
- customer and transaction records
- uploaded files
- background-job progress
- queue messages and delivery status
- WebSocket connections
- workflow checkpoints
- cache entries
- search indexes
- locks, leases, and idempotency records
The label “state” is too broad for one storage decision. Classify it by importance and lifetime.
| State class | Meaning | Examples |
|---|---|---|
| Durable | Must survive process and server failure | Orders, accounts, documents |
| Recoverable | Can be restored from another source | Search index, derived report |
| Temporary | May expire or be recreated | Cache entry, temporary file |
| Local | Exists only on one process or node | In-memory session, local upload |
| Shared | Available to multiple nodes | Database, object store, queue |
| Connection-bound | Exists for the life of a connection | WebSocket presence, stream position |
The most important design question is usually not “Does the application use state?” It is “Which component owns each state class, and what happens when that component fails?”
A stateful application depends on retained context
A stateful component uses information created by earlier activity to process the next request, event, or connection.
Common stateful workloads include:
- relational and document databases
- message brokers and durable queues
- persistent caches
- file servers
- search indexes
- multiplayer game servers
- real-time collaboration systems
- long-running workflows
- applications that store sessions or uploads locally
Statefulness is not a design flaw. Databases and storage systems exist because applications need reliable memory.
The operational cost is that stateful components are harder to replace and scale. Teams must plan for:
- data consistency
- backup and restoration
- replication or failover
- storage capacity and latency
- controlled network access
- upgrades and schema compatibility
- recovery point and recovery time objectives
- reconciliation after partial failure
A stateful service should not be treated like a disposable application VM. Its data path is part of the product.
A stateless application node does not own durable context
A stateless application node can process a request without relying on context stored only on that server.
Required context is usually:
- included in the request
- represented by a validated token
- retrieved from a shared database or cache
- read from object storage
- consumed from a shared queue
Common stateless workloads include:
- HTTP web servers
- REST and GraphQL APIs
- reverse proxies
- frontend-rendering nodes
- authentication gateways
- workers that checkpoint externally
- batch processors that use shared job state
AWS Well-Architected guidance recommends offloading state from individual compute nodes so instances can be replaced without availability impact. Microsoft similarly recommends externalizing server-side session state when an application needs to scale across multiple instances.
Stateless nodes are easier to:
- scale horizontally
- place behind a load balancer
- replace after failure
- deploy gradually
- rebuild from automation
- remove during maintenance
Statelessness does not remove state. It moves state into services that must now be designed and protected deliberately.
Most production systems are hybrid
A typical cloud application combines stateless and stateful components.
Users ↓ Load balancer ↓ Stateless web or API nodes ↓ private network Database + cache + queue ↓ Object storage and recovery systems
In this model:
- app nodes handle requests
- the database owns durable business records
- a shared cache or database owns session state
- a queue owns pending background work
- object storage owns uploaded files and generated assets
- recovery systems protect state according to its value
The application tier becomes replaceable while state remains in components designed to preserve it.
Use a state ownership decision framework
For every stateful item, record:
- Owner: Which component is authoritative?
- Durability: Must it survive node or region failure?
- Consistency: Can stale or duplicate data be tolerated?
- Lifetime: Request, session, job, customer, or indefinite?
- Access pattern: One node, many nodes, ordered, concurrent, or append-only?
- Recovery: Rebuild, restore, replay, or fail over?
- Security: Which services and users may access it?
- Migration: Can the state move without breaking old and new application versions?
This prevents one VM from becoming the undocumented owner of sessions, files, jobs, and secrets.
Sessions should not tie users to one application node
A session stored only in process memory makes the application tier stateful.
This often leads to sticky sessions, where a load balancer sends the user back to the same node. Sticky routing can work, but it creates limits:
- node failure can end active sessions
- traffic may become uneven
- deployments require more care
- scale-in can remove active user context
- failover becomes less predictable
A more replaceable design stores session state in a shared database or cache, or uses appropriately designed tokens.
Tokens do not eliminate all session concerns. Revocation, expiration, rotation, sensitive claims, and authorization changes still require deliberate design.
Uploaded files should not depend on one app VM
Local uploads may be reasonable for a first single-server deployment, but they create problems when:
- a second node cannot access the files
- the original VM fails
- deployment replaces the server
- storage grows independently of compute
- users are routed to different nodes
Shared Object Storage is usually a better fit for files that multiple nodes must access.
Temporary processing files can remain local when they are disposable and bounded. The completed output should be committed to its authoritative store before the node is removed.
Background jobs need durable ownership and safe retries
A worker that stores job progress only in memory is fragile. If it stops, the team may not know whether the job completed, partially completed, or should be retried.
A safer design uses a queue or database to record:
- job identity
- ownership or lease
- attempt count
- progress or checkpoint
- completion result
- failure reason
Workers should be idempotent where practical: repeating the same job should not create duplicate charges, emails, records, or destructive changes.
Scaling workers horizontally is useful only when the database, queue, and external APIs can support the added concurrency.
WebSocket and real-time services remain connection-stateful
WebSocket services retain live connections, subscriptions, presence, and delivery position. They can scale horizontally, but not in the same way as ordinary request-response APIs.
A multi-node design may require:
- connection-aware routing
- shared presence or subscription state
- pub/sub between nodes
- reconnect and resubscribe behavior
- safe connection draining
- message ordering and duplicate handling
The node can still be replaceable, but active connection state needs a defined migration or recovery behavior.
Databases are intentionally stateful
The objective is not to make a database stateless. The objective is to give it the correct durability, consistency, access, and recovery boundaries.
A database plan should consider:
- storage latency and capacity
- backup and point-in-time recovery needs
- connection limits
- private networking
- replication and failover
- schema migration compatibility
- monitoring and slow-query analysis
Adding stateless application nodes can increase database connections and query volume. Horizontal application scaling must include database headroom.
Caches should not accidentally become the only source of truth
A cache can be temporary, reconstructable state. It becomes a critical stateful system when the application cannot operate or recover without its contents.
Decide whether cached data is:
- safely disposable
- reconstructable but expensive
- session-critical
- queue-like
- the only copy of business data
Design cache loss explicitly. A cache miss storm after restart can overload the database even when no durable data is lost.
Statelessness improves horizontal scaling
Stateless app nodes let a load balancer route requests to any healthy instance.
Before scaling out, verify:
- sessions are shared or self-contained safely
- uploads are outside the app node
- jobs can be retried or resumed
- configuration and secrets are externalized
- application nodes are built repeatably
- database connections are controlled
- one node can be removed without losing critical context
Read Horizontal vs Vertical Scaling for the scale-up versus scale-out decision.
The operational test is simple:
Can one application node be drained and deleted while users remain signed in, files remain available, jobs continue, and no durable data disappears?
When the answer is no, identify which local state creates the dependency.
State affects load balancing and deployment
A load balancer does not make an application stateless. The state model must allow traffic to move between nodes.
Stateless app tiers support:
- round-robin or health-aware routing
- rolling deployments
- simpler node replacement
- more even utilization
Stateful backends may require:
- sticky sessions
- primary and replica roles
- connection-aware routing
- quorum and consistency rules
- safe draining
During rolling deployment, old and new versions may operate simultaneously. Shared state must remain compatible across:
- database schemas
- session formats
- cache keys
- queue messages
- API contracts
Use an expand-and-contract migration:
- Add new state or schema without removing the old form.
- Deploy code that understands both versions.
- Migrate data where required.
- Remove old application versions.
- Remove obsolete fields or formats later.
State determines the recovery path
A failed stateless node can often be rebuilt from code and configuration.
A failed stateful component may require:
- restoring a backup
- promoting a replica
- replaying a queue
- reattaching storage
- reconciling incomplete writes
- validating consistency
Separate state by recovery need.
| State | Typical recovery approach |
|---|---|
| Application node | Rebuild or replace |
| Customer database | Restore, replay, or fail over |
| Queue | Resume or replay safely |
| Object uploads | Restore retained objects or versions |
| Cache | Rebuild, warm gradually, or restore if intentionally durable |
| Search index | Recreate from authoritative data |
| Session state | Expire, restore, or preserve according to product needs |
Having a snapshot is not the same as having a tested recovery process. Use Data Protection as one part of a broader recovery design.
Keep the first architecture migration-friendly
A single VM can be reasonable for a small application. The goal is not to distribute every component immediately.
Keep the simple architecture ready for later change by:
- avoiding local-only sessions
- storing uploads outside the application directory
- separating configuration and secrets from code
- using repeatable deployment steps
- backing up durable data
- assigning clear owners to local files and databases
- monitoring storage growth and dependency usage
Introduce distributed components when they solve a measured scaling, recovery, security, or operational problem.
How this applies on Raff
A growing Raff architecture may use:
- Raff Cloud Servers for web, API, worker, database, and supporting workloads
- Load Balancers for health-aware routing across replaceable app nodes
- Private Cloud Networks for backend communication
- Object Storage for shared files and assets
- Volumes for persistent block storage where appropriate
- Data Protection for recovery planning
Users ↓ Raff Load Balancer ↓ Stateless application VMs ↓ private network Database / cache / queue ↓ Object Storage + recovery systems
Verify current product capabilities, storage behavior, and recovery workflows on live product pages before implementation.
Stateful vs stateless checklist
State ownership
- Every state item has an authoritative owner.
- Durable, temporary, local, and shared state are distinguished.
- Recovery and retention requirements are documented.
Stateless app tier
- Sessions do not depend on one node.
- Uploads and durable files are externalized.
- Jobs can be retried or resumed safely.
- Configuration and secrets are externalized.
- Nodes can be drained and replaced.
Stateful services
- Databases, queues, caches, and storage use controlled private access.
- Backups and restores are tested.
- Replication and failover behavior are understood.
- Schema and message changes support version coexistence.
Scaling and recovery
- Database and dependency headroom is measured.
- Load balancer health and draining are tested.
- Cache loss and connection loss are planned.
- Node removal does not destroy critical context.