Stateful vs stateless describes 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 only on that specific server.
Almost every useful system has state somewhere. The real 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 backed by intentionally stateful databases, queues, caches, and storage services. This makes application nodes easier to replace and scale while durable state remains in systems designed to preserve it.
Raff Technologies supports architectures that can start on one VM and later separate application, database, storage, and worker roles as scaling or recovery requirements grow.
Stateful vs stateless at a glance
| Decision factor | Stateful | Stateless |
|---|---|---|
| Meaning | Depends on retained context | Does not depend on context stored only on one node |
| Request handling | May require previous state | Any healthy node can usually handle the next request |
| State location | Local memory/disk or dedicated state service | Request itself or shared external services |
| Node affinity | Often possible or required | Ideally avoided |
| 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, file stores, persistent sessions | Web servers, APIs, reverse proxies, many workers |
| Main concern | Durability, consistency, recovery | Dependency availability and externalized state |
A useful rule is:
Keep durable state in the component responsible for preserving it. Keep application nodes replaceable when scaling, deployment, and recovery matter.
What is a stateful application?
A stateful application or component depends on information retained from earlier activity to process the next request, event, job, or connection.
Examples include:
- relational and document databases;
- durable queues and message brokers;
- file servers;
- applications that store sessions locally;
- applications that keep uploaded files on one server;
- long-running workflows with local checkpoints;
- multiplayer or real-time systems with connection state;
- caches that contain state the application cannot safely lose.
Statefulness is not a design flaw. Databases, queues, and storage systems are intentionally stateful because the application needs continuity.
The operational cost is that stateful components are harder to replace. Teams must plan for data consistency, backup and restore, storage capacity, network access, replication or failover where required, schema compatibility, and recovery behavior.
What is a stateless application?
A stateless application node can process a request without depending on context stored only on that machine.
Required context is usually:
- included in the request;
- represented by a validated token;
- retrieved from a shared database;
- retrieved from a shared cache;
- read from object storage;
- consumed from a shared queue.
Common stateless application examples 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.
A stateless application still uses state. It simply avoids making one application server the only owner of that state.
This is why stateless nodes are easier to scale horizontally, replace after failure, deploy gradually, rebuild from automation, and remove during maintenance.
Stateful vs stateless examples
The distinction becomes clearer when looking at the same workload in both forms.
| Workload | Stateful implementation | More stateless application design |
|---|---|---|
| User sessions | Session stored in one VM's memory | Session stored in shared cache/database or represented safely in a token |
| Uploads | File saved only to one app server | File stored in shared object storage |
| Background jobs | Worker owns job progress only in memory | Queue/database records job state and retry ownership |
| Web/API | Local user context required on one server | Any healthy node can serve the next request |
| Search | Index is the only copy of data | Index can be rebuilt from an authoritative database |
| Cache | Cache is the only source of a record | Cache can be rebuilt from durable data |
| Database | Durable records stored and retained | Database remains intentionally stateful; app nodes around it can be stateless |
The goal is not to eliminate state. The goal is to assign state to the right owner.
Application state needs an explicit owner
Application state is any information the system must remember after the current operation ends.
Examples include:
- customer and transaction records;
- user sessions and shopping carts;
- uploaded files;
- background-job progress;
- queue messages;
- workflow checkpoints;
- cache entries;
- search indexes;
- locks and leases;
- idempotency records;
- connection presence.
Classify each state item by importance and lifetime.
| State class | Meaning | Examples |
|---|---|---|
| Durable | Must survive process or node failure | Orders, accounts, documents |
| Recoverable | Can be recreated from another source | Search index, derived report |
| Temporary | May expire or be discarded | Cache entry, temporary processing file |
| Local | Exists only on one process or node | In-memory session, local upload |
| Shared | Accessible by multiple nodes | Database, object store, queue |
| Connection-bound | Exists for the life of a connection | WebSocket presence, stream position |
For each item, record:
- Owner: Which component is authoritative?
- Durability: Must it survive node failure?
- Consistency: Can stale or duplicate data be tolerated?
- Lifetime: Request, session, job, customer, or indefinite?
- Access: One node, many nodes, ordered, concurrent, or append-only?
- Recovery: Rebuild, restore, replay, or fail over?
- Security: Which services and users may access it?
This prevents one VM from becoming the undocumented owner of sessions, files, jobs, and business data.
Stateless architecture makes horizontal scaling easier
Stateless architecture allows traffic or work to move between healthy application nodes because no individual node owns irreplaceable local context.
Users ↓ Traffic distribution ↓ Stateless App VM 1 Stateless App VM 2 ↓ private network ↓ Database / Cache / Queue ↓ Object Storage
Before scaling out an application tier, verify that:
- sessions are shared or self-contained safely;
- uploads are stored outside individual app nodes;
- jobs can be retried or resumed;
- configuration and secrets are externalized;
- nodes are built repeatably;
- database connections are controlled;
- one node can be drained without destroying important context.
A simple operational test is:
Can one application node be drained and deleted while users remain signed in, files remain available, jobs continue, and durable data remains intact?
If not, identify which local state prevents replacement.
Use Horizontal vs Vertical Scaling for the scale-up versus scale-out decision and Cloud Autoscaling for VMs when nodes also need to be added and removed dynamically.
Stateful vs stateless load balancing
Load balancing behaves differently depending on whether request handling depends on state tied to a particular node.
A traffic distributor does not make an application stateless by itself. It only routes requests. The application state model determines whether any healthy node can safely handle the next request.
For a stateless application tier, traffic can usually be distributed across healthy nodes without requiring user affinity.
A stateful application tier may require:
- sticky sessions;
- connection-aware routing;
- primary/replica roles;
- state synchronization;
- safe connection draining;
- recovery behavior when the preferred node disappears.
Sticky sessions can reduce short-term migration work, but they preserve dependency on a particular application node. They do not solve local uploads, local job state, database limits, or node-failure recovery.
If the application needs multiple nodes, shared session state is usually more flexible than permanent server affinity.
Sessions should not accidentally tie users to one VM
A session stored only in application memory makes that app node stateful.
This creates several operational consequences:
- node failure can end active sessions;
- traffic may become uneven;
- deployments require more care;
- scale-in can remove active user context;
- failover is harder to reason about.
A replaceable design may use a shared database or cache for sessions, or use appropriately designed tokens where that model fits the product.
Tokens do not eliminate session architecture. Expiration, revocation, rotation, sensitive claims, and authorization changes still require deliberate design.
Uploaded files should not depend on one application server
Local uploads can work on an early single-server application, but they become a scaling and recovery boundary when:
- another node cannot access the files;
- the original VM fails;
- a deployment replaces the server;
- storage grows independently of compute;
- users are routed between nodes.
Shared Object Storage is usually a better fit for files that multiple application nodes need to access.
Temporary processing files can remain local when they are disposable and bounded. Commit the completed result to its authoritative store before the node is removed.
Background jobs need durable state and safe retries
A worker that stores job progress only in memory is difficult to recover safely. After failure, the team may not know whether the job completed, partially completed, or should be retried.
A safer queue or database-backed design records:
- job identity;
- ownership or lease;
- attempt count;
- progress or checkpoint;
- completion result;
- failure reason.
Workers should be idempotent where practical so repeating the same job does not create duplicate charges, messages, records, or destructive operations.
Stateless workers are strong horizontal-scaling candidates because another worker can take new work, while durable ownership remains in the queue or database.
For durable queues, retry safety, and worker scaling decisions, see Background Workers and Task Queues for SaaS Apps.
Databases are intentionally stateful
A database should not be made stateless. It should be given the correct durability, access, performance, and recovery boundaries.
A database plan should consider:
- data durability;
- storage latency and capacity;
- backup and recovery requirements;
- connection limits;
- private networking;
- replication or failover where required;
- schema migration compatibility;
- query and lock behavior.
Adding more stateless application nodes can increase database connections and query concurrency. Application scale-out therefore requires database headroom.
Raff provides Managed Databases for PostgreSQL, MySQL, Valkey, ClickHouse, and Kafka when teams want the data layer separated from application VMs.
Caches are stateful only when the application depends on their contents
A cache can contain temporary, reconstructable state. It becomes a critical stateful component when the application cannot recover or operate correctly without its contents.
Decide whether cached data is:
- disposable;
- reconstructable but expensive;
- session-critical;
- queue-like;
- the only remaining copy of business data.
A cache miss storm after restart can overload the database even when no durable data is lost. That failure mode should be planned explicitly.
On Raff, use Valkey terminology rather than Redis when referring to Raff's managed in-memory data service.
WebSocket and real-time systems remain connection-stateful
WebSocket services retain live connections, subscriptions, presence, and delivery position. They can still scale horizontally, but not in exactly the same way as ordinary request-response APIs.
A multi-node design may need:
- connection-aware routing;
- shared presence state;
- pub/sub between nodes;
- reconnect and resubscribe behavior;
- safe connection draining;
- message ordering and duplicate handling.
The server node can remain replaceable, but active connection state needs a defined recovery or migration behavior.
Deployment strategy depends on the state model
A common production pattern combines stateless web or API nodes with stateful backend services.
A common pattern is:
Stateless web/API nodes ↓ Stateful database, queue, cache, storage
This allows application nodes to use rolling or replaceable deployments while stateful services follow stricter migration and recovery procedures.
During a rolling deployment, old and new application versions may operate at the same time. Shared state must remain compatible across:
- database schemas;
- session formats;
- cache keys;
- queue messages;
- API contracts.
An expand-and-contract migration is often safer:
- Add the new state or schema without removing the old form.
- Deploy code that can understand both versions.
- Migrate data where required.
- Remove old application versions.
- Remove obsolete fields or formats later.
Use Blue-Green vs Rolling Deployments for the release-model decision.
State determines the recovery path
A failed stateless application node can often be rebuilt from code and configuration.
A failed stateful component may require:
- restoring a backup;
- promoting a replica;
- replaying queued work;
- reattaching persistent storage;
- reconciling incomplete writes;
- validating consistency.
| Component or state | Typical recovery approach |
|---|---|
| Stateless application node | Rebuild or replace |
| Customer database | Restore, replay, or fail over |
| Durable queue | Resume or replay safely |
| Object uploads | Restore retained objects or versions where available |
| Cache | Rebuild or restore only if intentionally durable |
| Search index | Recreate from authoritative data |
| Session state | Expire, restore, or preserve according to product requirements |
A snapshot or backup is only one part of a recovery design. Use Raff Data Protection for current snapshot and backup capabilities.
Keep a single-server architecture migration-friendly
A single VM can be the correct starting architecture for a small application. The goal is not to distribute every service immediately.
Keep the simple design ready for later change by:
- avoiding local-only sessions where practical;
- storing durable uploads outside the application directory;
- separating secrets and configuration from code;
- using repeatable deployment steps;
- protecting durable data;
- assigning clear ownership to local files and databases;
- monitoring storage and dependency growth.
Separate components when doing so solves a measured scaling, recovery, security, or operational problem.
Use Single Server vs Multi-Server Architecture to decide when adding another VM provides a real benefit.
How this applies on Raff
A growing Raff architecture can use:
- Raff Cloud Servers for application and worker VMs;
- VPC for private backend communication;
- Managed Databases for supported managed data engines;
- Object Storage for shared files and durable assets;
- Volumes for persistent block storage;
- Data Protection for backup and snapshot workflows.
Raff VM public traffic is unmetered with no normal VM egress fee, but port speed is class-specific: 1 Gbps on Cost-Optimized VMs and 3 Gbps on General Purpose, CPU-Optimized and Windows VMs. VPC traffic is private and unmetered.
A practical progression is:
Single application VM ↓ state ownership becomes explicit App + separate data/storage services ↓ application nodes become replaceable Multiple stateless app or worker VMs ↓ Traffic distribution + private networking + shared state
