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Stateful systems remember context between interactions; stateless systems treat every request as independent. A stateful service can use information held in memory, a session store, a database, or a persistent connection to understand what happened earlier. A stateless service must receive everything it needs—or retrieve it from shared storage—on each request, so any healthy instance can usually handle the work.
The distinction is an architectural property, not a claim that one design is always better. Stateful designs simplify continuity and long-lived conversations but complicate scaling and failover. Stateless designs make routing, horizontal scaling, and replacement easier, while shifting context management to clients or shared services.
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What “state” means in a service
State is information that changes during an interaction and can affect a later operation. Examples include an authenticated user’s session, items in a shopping cart, the current step in a workflow, an open subscription, or messages associated with a live connection.
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A component is stateful when later work depends on context retained from earlier work. That context may be kept in process memory, on local disk, in a database or cache, or inside a connection-oriented component. A component is stateless when each request can be understood and completed without relying on state stored on the particular server that receives it.
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Stateless does not mean “stores no data.” A stateless API can write orders to a database, read profiles from a cache, or save files in object storage. The important boundary is whether request handling depends on one instance’s local memory or disk. AWS guidance recommends removing that dependency or offloading the state to a shared store.
Stateful vs. stateless at a glance
| Aspect | Stateful design | Stateless design |
|---|---|---|
| Request context | Later requests can rely on context retained by the service or connection. | Each request is independently understandable and fulfillable. |
| Session continuity | Often implicit in a server-side session or persistent connection. | Context travels with the request or is read from shared storage. |
| Load balancing | May need session affinity (“sticky” routing) or coordinated state. | Requests can usually go to any healthy instance. |
| Horizontal scaling | Adding nodes requires state replication, affinity, or a shared store. | Adding or removing interchangeable instances is simpler. |
| Failure recovery | Replacing a node can lose locally held context unless it was replicated. | A replacement can resume work when required state is external and available. |
| Latency | Can avoid repeated context lookups, but coordination or replication adds cost. | May perform a database/cache lookup or carry more request data. |
| Implementation | Conversation and connection workflows are straightforward; distributed coordination is harder. | Routing and deployment are straightforward; explicit context handling is required. |
Is HTTP stateful or stateless?
HTTP is stateless by design. MDN describes it this way: “HTTP is stateless: there is no link between two requests being successively carried out on the same connection.” The protocol does not require a server to remember an earlier request when processing a later one.
Applications can add state on top of HTTP. During login, a server may create a session record and send the browser a cookie containing a session identifier. The browser returns that cookie, allowing the application to associate separate HTTP requests with the same user. HTTP remains stateless at the protocol level; the application is using a stateful session mechanism.
Cookie-based session flow
- The client submits credentials to a login endpoint.
- The server validates them and creates a session record, commonly in a database, cache, or session store.
- The response sets a cookie containing an identifier or other session context.
- Later requests include the cookie.
- The application uses the identifier to retrieve the session and authorize the operation.
If the session exists only in one process’s memory, a load balancer may need sticky routing and a restart may log users out. Storing the session in a shared service removes that single-instance dependency while preserving application-level continuity.
What stateless REST means
REST statelessness is a constraint on communication: the server completes each client request independently of previous requests. A request must contain the authentication, resource information, parameters, and representation details needed to process it, or identify state that the server can retrieve from a shared system.
Example of an independent request
A client calls GET /orders/123 with an authorization token. Any healthy API instance can validate the token, fetch order 123, and return a response. The request does not depend on a conversation having been established with a particular instance.
Can a REST API keep session state?
Yes, but the placement matters. A REST API can use a cookie and a server-side session, or a token that identifies a user. Strictly stateless handling means the server does not rely on per-client data held locally between requests. A shared database or cache can hold that data; the API instances remain replaceable. If a request only works after an earlier request initialized memory on the same node, the service is stateful from an operational perspective, even if its URLs look REST-like.
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Long-lived conversations
Chat, multiplayer sessions, collaborative editing, and workflow engines often benefit from retaining a current conversation or state machine. The service can apply the next message or event without making the client resend the entire history.
Persistent connections
A WebSocket service maintains an open connection and its interaction context. AWS API Gateway documentation distinguishes stateful WebSocket APIs from stateless HTTP and REST APIs. Connection identity, subscriptions, and in-flight events naturally belong to the connection’s state.
Transactions and ordered workflows
Some operations have an explicit sequence: reserve inventory, authorize payment, then confirm shipment. A stateful coordinator can make the current stage visible and enforce valid transitions. The durable state should still be stored where it survives process failure.
Where stateless systems are useful
Public APIs and read-heavy services
When each call contains its authorization and resource details, instances can be added behind a load balancer without moving sessions. This is a common fit for REST endpoints, webhooks, and idempotent reads.
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Stateless instances can be created, drained, upgraded, or replaced without copying private in-memory sessions. AWS Well-Architected guidance connects this model with horizontal scaling, node replacement, and failure tolerance.
Workers and event consumers
A worker can receive a job, load required data from shared storage, process it, and record the result. Another worker can retry the job if the first one fails, provided the operation is designed for safe retries.
How load balancing changes the design
With a stateful service, a load balancer may pin a client to the instance that holds its session. Sticky sessions are easy to understand but reduce routing flexibility: an overloaded or failed node can affect all clients attached to it. Replicating state between nodes avoids strict affinity but introduces consistency, conflict, and network-failure concerns.
With a stateless service, the balancer can choose any healthy instance for every request. Shared databases, caches, or external files provide continuity. The shared layer becomes critical infrastructure, so it needs its own availability, capacity, backup, and consistency plan.
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Stateful failure mode
If state exists only in a process, a crash can discard it. Users may lose a cart, a connection, or a workflow position. Replication and durable checkpoints reduce that risk, but the design must define which copy is authoritative and how conflicting updates are resolved.
Stateless failure mode
A stateless instance can usually be replaced quickly, but requests still fail if the shared database, cache, identity provider, or object store is unavailable. Statelessness removes one class of dependency; it does not eliminate dependencies.
Idempotency and retries
Independent requests are easier to retry, but a retry can duplicate a side effect such as a charge or order. Use an idempotency key, durable operation record, or equivalent business rule for non-repeatable actions. This is a correctness control, not merely a scaling feature.
Performance and cost trade-offs
- Stateful designs: keeping hot context in memory can reduce lookup latency, while replication, affinity, and connection management add operational overhead.
- Stateless designs: requests may be larger or require a shared-store lookup, but instances are easier to distribute and replace.
- Shared stores: moving state out of instances centralizes durability and coordination. Capacity, latency, availability, and data-transfer costs must be budgeted.
- Network locality: a remote cache lookup can cost more than local memory, but local memory can become a recovery and scaling liability.
There is no universal benchmark that ranks one model faster. Results depend on payload size, storage latency, connection duration, replication strategy, traffic shape, and consistency requirements.
A practical decision framework
- Identify the context. List what must survive from one operation to the next: identity, workflow stage, connection subscriptions, or temporary computation.
- Set the failure requirement. Decide what may be lost on a process crash and what must survive node, zone, or region failure.
- Choose the owner of state. Put durable context in a database or other shared store; reserve process memory for disposable caches and acceleration.
- Test routing assumptions. Send consecutive requests through different instances. If the second request fails without sticky routing, the service has an unaddressed state dependency.
- Design retries. Define idempotency and ordering for every operation that changes data.
- Measure the real bottleneck. Compare lookup latency, replication traffic, connection counts, recovery time, and storage cost under representative load.
Hybrid architectures are normal
Most production systems combine both models. Stateless API instances can authenticate requests and serve resources, while a database stores profiles, a cache stores short-lived sessions, and a WebSocket gateway maintains live connections. This arrangement follows AWS guidance to offload state while allowing request-serving nodes to remain interchangeable.
A useful boundary is to make the API tier stateless even when the product has state. Keep the state explicit, durable, and observable in shared systems; keep instance-local memory optional. This lets teams scale the request tier independently from the systems that own data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Stateful and stateless screenshot API calls
A screenshot API call is naturally a stateless HTTP request: the URL and capture options travel with the request, and the caller receives an image or PDF response. If your application needs continuity—such as reusing authentication cookies, applying a chosen cache TTL, or tracking an asynchronous job—store that context in your own shared system rather than assuming one API client process will retain it.
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curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpScreenshotNeo accepts the cookie or consent banner before capture and removes more than 60 known consent platforms, newsletter popups, and chat widgets; each cleanup step can be disabled. Bot checks, CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers identify the page verdict and billing result. Its MCP server exposes take_screenshot, get_page_info, and capture_pdf for Claude, Cursor, and other MCP clients. The Free plan includes 1,000 screenshots per month with no card; paid plans start at $5 for 3,000 screenshots. Sign up for the free plan.
Common mistakes and troubleshooting
“Stateless” means no database
Cause: confusing request independence with data absence.
Fix: keep durable data in a shared database, cache, or object store; remove only the dependency on one instance’s local state.
Users are logged out after scaling
Cause: sessions live in process memory and requests reach different nodes.
Fix: use a shared session store, or deliberately configure affinity while you plan a durable migration.
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WebSocket clients cannot reconnect cleanly
Cause: connection state exists only on the old process.
Fix: persist subscriptions and workflow checkpoints, and define a reconnect handshake that rebuilds transient state.
Retries create duplicate orders
Cause: an independent request was retried without idempotency protection.
Fix: require a client-supplied idempotency key and record the completed result before acknowledging success.
Shared storage becomes the bottleneck
Cause: moving state out of instances concentrated reads, writes, or locks in one service.
Fix: measure access patterns, add suitable caching and indexes, partition hot data, and define behavior when the store is unavailable.
Bottom line
Choose stateful behavior when continuity, ordered interaction, or a persistent connection is central to the feature. Prefer stateless request handling when you need simple routing, horizontal scaling, and rapid instance replacement. In many robust systems, the best answer is hybrid: stateless service instances with explicit state held in durable, shared infrastructure.
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Frequently Asked Questions
Is a JWT automatically proof that an API is stateless?
No. A JWT can carry identity information between requests, but the service may still depend on local session data, connection state, or other instance-specific context.
Are cookies incompatible with REST?
No. Cookies are a transport mechanism. A REST API can use a cookie if each request remains understandable and the server does not depend on private state held by one instance.
Should temporary caches be considered state?
They are state in the broad sense, but disposable caches do not necessarily make a service operationally stateful. The key question is whether correctness depends on that cache existing on a particular instance.
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