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Use Varnish for shared, cacheable HTTP responses; use Redis for application data such as query results, objects, sessions, counters, and queues. They solve different problems. Varnish can return a complete public response without contacting the origin, while Redis stores values that application code retrieves and invalidates. A site that needs both whole-page delivery and fast application data commonly runs them together, with Varnish at the HTTP layer and Redis inside the application layer.
Choose by workload, not by popularity
The right choice follows the thing you want to cache and the component that understands when it is stale. Start with this decision table before changing your VPS architecture.
| Workload on the VPS | Best starting point | Why | Important qualification |
|---|---|---|---|
| Mostly public, read-heavy HTML pages | Varnish | It can serve a complete HTTP response directly to many visitors. | Define bypass rules for cookies, authorization, previews, and write requests. |
| Public GET responses from an HTTP API | Varnish | Responses can be shared when their HTTP freshness and variation rules are clear. | Do not share responses that contain user-specific data. |
| Database or API query results | Redis | Application code can address, combine, and invalidate individual values. | Choose a key scheme, TTL policy, and memory ceiling deliberately. |
| Computed objects, fragments, counters, queues, or sessions | Redis | The application needs a key-addressed store rather than a copy of an entire response. | Decide whether the data may be lost on restart or needs persistence. |
| Highly personalized traffic | Usually Redis, or Varnish with explicit variation rules | Per-user content is unsafe to share by default at an HTTP cache. | Cookies, authorization headers, and cache keys must be reviewed before enabling Varnish. |
| Public pages plus expensive application work | Both | Varnish absorbs repeated HTTP requests while Redis reduces repeated database and computation work. | Assign ownership of keys, TTLs, purge paths, and failure behavior to each layer. |
There is no universal hit-rate or speed number that makes one product superior for every VPS. Results depend on the working set, response size, concurrency, origin cost, invalidation frequency, and available memory. Treat the cache as part of the request design, not as a generic acceleration switch.
How the two caches fit into a request
Redis and Varnish sit at different points in the path from a visitor to your application.
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Client → Varnish (optional HTTP response cache) → web server/application → Redis (optional data cache) → database or other origin
Varnish is outside the application
Varnish is a reverse caching proxy between clients and origin servers. Its documentation describes it as “a reverse caching proxy: a proxy server that speaks HTTP that you put in front of your web servers.” On a cache hit, Varnish can return the stored response without asking the origin to render the page again. The cached unit is an HTTP object: status, headers, and body.
Because it speaks HTTP, Varnish can use response directives such as s-maxage, max-age, private, no-cache, no-store, and stale-while-revalidate when deciding whether an object is shareable and how long it is fresh. See the Varnish HTTP caching documentation for the semantics of these controls.
Redis is inside the application design
Redis is a key-addressed data store. The application asks for a particular key, such as a query result, rendered fragment, session record, counter, or computed object, and decides how to use or invalidate that value. Redis does not need to know how the final HTML response is assembled. Several different pages can read the same cached object, and one page can read many Redis keys.
This distinction explains why asking whether Redis “caches full web pages” is misleading. Application code can put a complete page in a Redis value, but Redis does not automatically understand HTTP freshness, cookies, authorization, or response headers. You would be implementing that policy yourself.
What Varnish does well—and what it requires
HTTP-aware sharing
Varnish is a strong fit when many clients can receive the same response. Typical examples include anonymous article pages, product or documentation pages whose public representation is shared, and GET API responses with a stable public representation. The origin can spend its time rendering misses and handling writes instead of repeating identical work for every visitor.
Do not assume that a response is safe to share merely because it is a GET. A response that varies by account, authorization header, cookie, language, device, or experiment needs an explicit variation strategy. Requests that mutate data should normally bypass or invalidate cached objects rather than being served from them.
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VCL provides policy control
Varnish Configuration Language (VCL) is a domain-specific language for request handling and document caching. It can select a backend, alter incoming requests, change response headers, route traffic, and define cache behavior. Varnish translates VCL to C and loads the resulting shared object. The reference is available in the Varnish Configuration Language documentation.
This flexibility is useful when the default rules do not match your application, but it makes configuration a software-maintenance task. Review VCL changes, keep the rules in version control, and make cache keys and bypass conditions observable. A small mistake in cookie or authorization handling can expose one user’s response to another; a mistake in purge logic can leave old content visible longer than intended.
Storage is based on complete objects
Varnish storage backends have different performance characteristics, but the built-in backends cache full objects. The official storage-backends guide therefore warns that concurrent large objects require proportionally more cache storage. Size the cache for the working set you actually want to retain and the number of large responses that may be resident at once.
Monitor object sizes, hit rate, evictions, storage utilization, and origin load. A cache that is too small can evict useful objects before they are reused; a cache that is oversized can consume resources that the origin or operating system needs. The right size is a capacity decision, not a fixed percentage of disk or RAM.
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Freshness and invalidation are part of the design
HTTP freshness gives Varnish a standard language for expiration, but publishing a change still requires a plan. You can use short freshness lifetimes, explicit purge or ban operations, or a combination. A page that must change immediately after an edit should not rely on a long TTL without an invalidation path.
Test the cases that are easy to miss: a logged-in request after an anonymous request, a response with a session cookie, an authorization header, a failed origin response, and a page after an update. Confirm which request reaches the origin and which headers the client receives.
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What Redis does well—and what it requires
Key-addressed application data
Redis is appropriate when the application can name the thing it wants to reuse. Useful keys might represent a database query result, a calculated permission set, a rendered component, a session, a rate counter, or a queue entry. The application can invalidate one key, a family of keys, or all keys associated with an event without purging an entire HTTP response cache.
Key design should encode the dimensions that change the value. If a result differs by tenant, locale, currency, or user, those dimensions belong in the key or in a carefully controlled lookup scheme. Include a version component when a schema or serialization change would make old values unsafe to read.
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Set a deliberate memory ceiling
Redis uses the maxmemory limit to trigger eviction. If you leave it unset, Redis can continue allocating memory until it competes with the rest of the VPS or the host runs out of memory. Set a ceiling that leaves operating-system and application headroom, then monitor usage and evictions.
Redis recommends leaving room for replication or AOF buffers because those buffers are not included in the eviction comparison. A configured limit is not permission to fill every byte of RAM with cached values. Account for the Redis process, data representation, allocator overhead, replication, persistence buffers, and the other services on the machine.
Match the eviction policy to the data
The available policies documented in Redis key eviction determine what happens when the configured limit is reached.
| Policy | What it does when Redis needs space | Use it when |
|---|---|---|
noeviction |
Retains existing keys and rejects writes that would exceed the limit. | Writes must not silently remove data and the application can handle write failures. |
allkeys-lru |
Evicts least-recently-used keys from the full keyspace. | All keys are disposable cache entries and recency predicts reuse. |
allkeys-lfu |
Evicts least-frequently-used keys from the full keyspace. | Long-term popularity is a better signal than recent access. |
volatile-lru |
Evicts least-recently-used keys among keys with an expiration. | Persistent keys must be protected and disposable entries have TTLs. |
volatile-lfu |
Evicts least-frequently-used keys among keys with an expiration. | Only expiring keys may be removed and frequency is the useful signal. |
volatile-random |
Chooses randomly among keys with an expiration. | A simple policy is acceptable and the expiring subset is disposable. |
volatile-ttl |
Prefers keys with the shortest remaining TTL among expiring keys. | Expiration order reflects the value of retaining an entry. |
Do not choose a volatile policy while storing cache entries without expiration and then expect those entries to be evicted. If no eligible keys exist, writes can still fail. Conversely, an all-keys policy can remove keys that your application treats as more important unless the keyspace is exclusively disposable cache data.
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Persistence changes the failure model
Redis offers RDB snapshots and AOF logging. Persistence can help sessions or valuable derived state survive a restart, but it introduces disk I/O, fork, rewrite, buffering, and recovery considerations. Read the Redis persistence documentation before treating a cache as durable storage.
For a disposable cache, disabling persistence can simplify operations: after a restart, the application repopulates missing values. For sessions or state that would cause user-visible damage when lost, evaluate durability separately from cache speed. A persistent Redis instance still needs backups, disk capacity, and a recovery plan; persistence is not the same as a complete disaster-recovery strategy.
Client-side caching can add another layer
Redis client-side caching keeps popular replies in application memory, reducing round trips and database work. It also creates another copy that can become stale. The client-side caching reference describes invalidation messages; bounded TTLs are another way to limit staleness. Use one of these controls rather than allowing an unbounded local copy to outlive the Redis value.
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| Axis | Redis | Varnish |
|---|---|---|
| Cache key and data shape | Application-defined keys containing objects, query results, sessions, counters, or other values. | HTTP request and response representation, including the response body and headers. |
| Placement | Usually called by the application after a request reaches the origin stack. | Placed in front of the web server or application as a reverse proxy. |
| Invalidation | Application deletes, overwrites, versions, or expires individual keys and key families. | HTTP freshness, purge or ban rules, and VCL determine when complete responses leave storage. |
| Workload scope | Fine-grained data shared by application code, often across several pages or services. | Shared responses for clients whose requests are safe to serve from the same object. |
| Memory and storage behavior | Data representation, allocator overhead, configured maxmemory, and optional buffers determine usage. | Complete object size and concurrency determine how much cache storage is needed. |
| Persistence | Optional RDB snapshots or AOF logging; persistence affects I/O, restart, and recovery behavior. | Primarily an HTTP response cache; origin data remains the source of truth. |
| Configuration burden | Key naming, TTLs, maxmemory, eviction, persistence, and client behavior. | HTTP headers, VCL, backends, cookies, authorization, variation, and purge behavior. |
| Failure impact | Misses or rejected writes can increase database work or break features that assume a value was stored. | Misses increase origin traffic; an incorrect rule can serve stale or private content. |
How to run Redis and Varnish together
Using both is rational when the site has two different bottlenecks. A typical flow is:
- A client requests a public URL.
- Varnish checks its HTTP cache key and returns a fresh response on a hit.
- On a miss, the request reaches the web server and application.
- The application reads expensive objects or query results from Redis, or rebuilds them from the database and stores them.
- The application response returns through Varnish, which may store the complete public response according to its HTTP policy.
The two layers should not compete over ownership. Varnish owns complete responses; Redis owns the application values used to create responses. Keep separate TTL and purge mechanisms so that invalidating a Redis object does not falsely imply that an already-stored Varnish response has changed.
| Concern | Varnish responsibility | Redis responsibility |
|---|---|---|
| Freshness | HTTP max-age, s-maxage, stale behavior, and response variation. | Key TTLs and application rules for rebuilding values. |
| Change publication | Purge or ban affected URLs and variants. | Delete, overwrite, or version affected keys. |
| Privacy | Bypass or vary on cookies, authorization, and other user-specific inputs. | Scope keys to the correct user, tenant, or permission context. |
| Failure response | Choose whether misses, stale objects, or origin errors are acceptable. | Define behavior when a key is absent, Redis is unavailable, or a write is rejected. |
| Observability | Track hit rate, misses, evictions, object sizes, and origin requests. | Track memory, evictions, rejected writes, hit/miss behavior, persistence events, and latency. |
Keep purge paths explicit
When an editor changes an article, the application may need to invalidate a Redis query result, a rendered fragment, and one or more Varnish URLs. Build that sequence into the publishing workflow instead of assuming that one cache will notice the other. Versioned keys can protect Redis from old schemas, while URL purges or short HTTP lifetimes prevent a previously rendered page from outliving its source data.
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Isolate failures where possible
A Varnish restart should create HTTP misses, not erase application sessions. A Redis restart should cause application cache misses, not expose a private response through Varnish. Design the application so that a missing Redis value can be rebuilt and so that a Varnish miss simply reaches the origin. If either layer is a hard dependency for correctness rather than acceleration, document and test that dependency explicitly.
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WordPress and other content sites
For a site with mostly anonymous, public pages, Varnish can absorb repeated page requests before PHP or the application stack runs. Logged-in dashboards, preview links, account pages, carts, and pages carrying user-specific cookies need bypass or variation rules. A public page that embeds a personalized fragment should not be cached as one shared object unless that fragment is handled separately.
Redis is useful inside the same site when database queries, computed objects, sessions, or rendered fragments are expensive to recreate. It can reduce work even when a page is not eligible for whole-response caching. The two choices are therefore not a WordPress-versus-Redis decision: one concerns delivery of a shared HTTP response, the other concerns data used while assembling a response.
API-specific decisions
Public GET endpoints can fit Varnish when clients may receive the same representation and the API emits accurate cache headers. Endpoints that depend on an authorization token, account, tenant, or rapidly changing state need explicit variation or an application-level cache instead. Redis lets the application cache a database result or computed object while still applying authorization logic on every request.
Never cache mutation requests as if they were ordinary reads. After a successful write, invalidate the Redis keys and HTTP objects that represent the changed data. If an endpoint can return different fields based on headers or query parameters, include those dimensions in the Varnish policy or keep the response out of the shared cache.
How much RAM does Redis need on a VPS?
There is no responsible universal RAM figure. Measure the data you intend to keep and add headroom for the Redis process, allocator overhead, the operating system, your web and database services, and any replication or AOF buffers. Then set maxmemory below the amount that would put the host under pressure.
- Estimate the number of live keys and the typical value size, including serialized metadata.
- Measure a representative dataset in Redis rather than multiplying an idealized payload size; key names and internal overhead count.
- Add room for peak working-set growth, temporary rewrites, replication, and AOF buffering when those features are enabled.
- Reserve memory for the rest of the VPS. A cache that forces the kernel to reclaim or kill another service is oversized even if Redis itself reports free space.
- Choose an eviction policy that matches whether every key is disposable, and alert before the ceiling is reached.
For a disposable cache, frequent evictions may be acceptable if the origin can rebuild values cheaply. For sessions or expensive derived state, an eviction event may be a user-visible failure, so increase headroom or change the data and persistence design instead of simply raising the limit.
Operational checklist before going live
- Classify the workload. List public responses, personalized responses, database results, sessions, counters, and other state separately.
- Define ownership. Decide which layer owns each TTL, key, URL, and invalidation action.
- Set Redis boundaries. Configure a deliberate
maxmemoryvalue, select an eviction policy, and account for replication or AOF buffers. - Choose persistence intentionally. Disable it only when losing the cache is acceptable; otherwise plan disk capacity, rewrite impact, backups, and recovery.
- Write Varnish rules. Document backends, cache keys, cookie and authorization bypasses, freshness headers, and purge behavior in VCL.
- Test privacy paths. Request the same URL as an anonymous visitor, a logged-in user, two different tenants, and a user with different authorization. Verify that no private response is shared.
- Test change publication. Update a record, invalidate Redis values, purge affected URLs, and confirm that the next request shows the new data.
- Observe misses and evictions. A high hit rate is not the only goal; check origin load, object size, Redis memory, rejected writes, and latency.
- Rehearse restarts. Confirm that Varnish misses recover normally and that Redis sessions or other required state behave according to your persistence decision.
Troubleshooting common symptoms
| Symptom | Likely cause | What to check |
|---|---|---|
| Origin CPU remains high despite Varnish | Low reuse, an undersized cache, short freshness, or broad bypass rules. | Inspect cache status, bypass conditions, object sizes, request variation, and origin requests. |
| Visitors see an old page after an edit | The Varnish object or a Redis value was not invalidated. | Trace the publication event through both purge paths and inspect TTLs. |
| Redis reports rejected writes or rising evictions | The configured ceiling is being reached or the policy does not cover the keys you expect. | Review maxmemory, policy, key TTLs, value growth, and non-cache data mixed into the keyspace. |
| Data disappears after a Redis restart | The data was disposable or persistence was disabled or insufficient. | Decide whether the application can rebuild it; if not, design persistence and recovery deliberately. |
| Varnish storage fills unexpectedly | Large complete objects, high concurrency, or a working set larger than the allocated storage. | Measure object sizes, resident objects, evictions, and the storage backend configuration. |
| A private response appears to another visitor | Cookies, authorization, or another variation dimension was omitted from bypass or cache-key rules. | Purge affected objects immediately, correct VCL, and test authenticated and anonymous paths separately. |
Bottom line
Run Varnish when your primary goal is to deliver the same HTTP response to many clients without repeatedly contacting the origin. Run Redis when your application needs fast, addressable values and control over key-level expiration or invalidation. On a VPS with both public delivery and expensive application work, use both: Varnish at the front, Redis behind the application, with explicit limits, privacy rules, purge paths, and recovery behavior.
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