Designing a Multi-Tenant KV-Cache Layer in ASP.NET Core for Scalable Inference Serving

Quick Answer

Multi-Tenant KV-Cache Layer in ASP.NET Core: Learn how to build a production‑grade Multi‑Tenant KV‑Cache Layer in ASP.NET Core, covering tenant isolation, Redis clustering, batching tricks, and cost‑optimization for inference workloads.

Tenant Isolation, Billing, and Fair Eviction

In a multi‑tenant SaaS that serves thousands of inference requests per second, the cache is the single point that can turn a 20 ms latency spike into a revenue‑losing event. The key challenges are:

• Tenant isolation – a miss in tenant A’s namespace must never return tenant B’s data.
• Cost allocation – memory usage must be billable per tenant so that a runaway tenant doesn’t eat the entire cluster.
• Eviction fairness – LRU should be scoped per tenant, not globally, otherwise a single hot tenant can starve others.
• Latency guarantees – the cache layer must not add more than 5 ms to an inference pipeline.

When these constraints are not satisfied, the cache can become a silent performance killer or even a security liability.

When this fails in production

• Cross‑tenant key collisions cause data leakage and incorrect responses.
• Unbounded memory growth from a single tenant’s write‑through cache kills the cluster.
• Improper TTLs lead to stale results or unnecessary compute.
• Batching logic that mixes tenants ends up hitting the wrong namespace and increases RTT.
• Missing tenant validation in middleware allows malicious actors to read or evict another tenant’s keys.

Real‑World Example – A News‑Aggregator SaaS

Our prototype, FeedSummarizer, receives RSS feeds from 1,200 tenants, runs a transformer model to produce 200‑word summaries, and exposes a REST API to fetch them. The SLA is 95 % of requests < 200 ms. The inference engine is an Azure-openai-integration-with-net-rag-debugging-429s-in-production-20260825" class="internal-link">Azure OpenAI endpoint costing $0.0002 per request. The cache must:

• Store up to 1.5 GB per tenant for hot articles.
• Support 10 k requests per second across all tenants.
• Provide per‑tenant eviction without impacting others.

Key Design Decisions

public class FeedSummarizer
{
    private readonly ITenantCache _cache;
    private readonly IInferenceEngine _engine;

    public FeedSummarizer(ITenantCache cache, IInferenceEngine engine)
    {
        _cache = cache;
        _engine = engine;
    }

    public async Task<string> SummarizeAsync(string articleId, string content)
    {
        var key = ComputeHash(content);
        var cached = await _cache.GetAsync(key);
        if (cached != null) return cached;

        var summary = await _engine.GenerateAsync(content);
        // Write‑through only for hot articles (90th percentile of traffic)
        await _cache.SetAsync(key, summary, TimeSpan.FromMinutes(10));
        return summary;
    }
}

Trade‑offs

Below is a pragmatic comparison of the main architectural levers. In a production environment the trade‑off matrix is always about performance vs. cost vs. isolation.

Cache Strategy: Cache‑Aside vs Write‑Through

• Cache‑Aside gives full control over eviction and batching. It’s the only option if you need per‑tenant LRU – Redis’ global LRU can’t be scoped.
• Write‑Through is tempting for hot data, but you must guard against a single tenant flooding the cache. In our case we apply write‑through only to the top 10 % of articles per tenant.

Provider Choice: Azure Cache for Redis vs Open‑Source Redis

In practice, the managed service pays for the reduced operational overhead and the VNet isolation gives us a hard security boundary that a self‑hosted cluster can’t match.

Key Namespace Design

• Prefix + hash is the safest pattern: tenantId:hash. Avoid concatenation without a delimiter – it’s a common source of collisions.
• Hash the payload to keep key length predictable and stay below Redis’ 512‑byte limit.
• Store a small metadata header (e.g., TTL, tenant quota) alongside the value if you need to enforce per‑tenant limits in Lua scripts.

Eviction Policy & Quota Enforcement

Redis’ allkeys-lru works globally. To enforce per‑tenant quotas we run a Lua script every minute that:

1. Counts keys per tenant using SCAN with the tenant prefix.
2. Calculates memory usage via MEMORY USAGE per key.
3. Deletes the oldest keys until the tenant is below its quota.

Running this script on a schedule keeps the cluster healthy without adding latency to the hot path.

TTL Strategy

• Short TTL (30 s–5 min) for volatile prompts that change frequently.
• Long TTL (12 h–24 h) for embeddings reused across sessions.
• Dynamic TTL: adjust based on usage patterns – if a key is hit >10 times in an hour, bump its TTL by 50 %.

Batching vs Single Lookups

• Batching reduces RTT dramatically. In our benchmark, MGET for 10 keys per tenant cut latency from 420 ms to 115 ms.
• However, batching introduces a small serialization overhead. Use StringGetAsync with an array of RedisKey to keep the call async.
• For cross‑tenant requests (e.g., admin dashboards), fire one MGET per tenant in parallel with Task.WhenAll to avoid blocking the thread pool.

Performance Considerations

• Connection pooling – a single ConnectionMultiplexer per app instance is a must; it internally reuses sockets and keeps the overhead < 1 ms.
• Pipeline mode – for high‑throughput micro‑services, enable pipelining to batch commands before sending over the wire.
• Use StringGetAsync instead of StringGet to avoid blocking the thread pool on I/O.
• Measure key size and memory usage; 2 KB per key is a good rule of thumb for inference results.

Scaling Notes

• When the cluster grows beyond 8 GB, move to a cluster of 2 GB shards. Use RedisCluster and keep key prefixes consistent across shards.
• Deploy the cache in a separate subnet with NSG rules that only allow traffic from the application tier.
• Enable cluster-enabled yes and set cluster-node-timeout 15000 to avoid split‑brain scenarios.
• Monitor Keyspace Hits / Misses per tenant to spot hot tenants early.

Tenant Allocation & Cache Strategy Checklist

Use the following checklist to decide whether a new tenant should get a dedicated namespace or share a cluster, and which cache strategy to apply.

Better Approach Based on Experience

In our production rollout we discovered that the naive approach of using a single allkeys-lru policy was a nightmare when a single tenant started a burst of 100 k requests per minute. The cluster evicted keys from other tenants, causing a 30 % spike in latency for everyone.

Switching to a per‑tenant Lua eviction script fixed the problem in minutes. Additionally, we introduced a hot‑key cache – a small in‑memory dictionary in each worker that stores the most frequently accessed keys for the current request cycle. This reduces the number of round‑trips to Redis by 15 % for the hottest 1 % of keys.

Common Mistakes Engineers Make

• Using KeyValuePair for keys without a delimiter – leads to collisions.
• Skipping tenant validation in middleware – allows cross‑tenant reads.
• Hard‑coding TTLs – a 30 s TTL for all keys caused stale summaries during traffic spikes.
• Over‑sharding – creating a shard per tenant for 1 k tenants caused high management overhead and network latency.

When this fails in production (again)

• Cache hit rate drops below 50 % after a model update – indicates the hash function changed or the payload format changed.
• Redis logs show OOM command not allowed – per‑tenant quota script is missing or mis‑configured.
• Observability dashboards show a spike in GET latency to 300 ms – the batch size is too large for the network bandwidth.

Balancing Isolation, Eviction, and Performance

Implementing a Multi‑Tenant KV‑Cache Layer in ASP.NET Core is not a matter of picking a library; it’s a set of disciplined trade‑offs around tenant isolation, eviction fairness, and performance. The right decisions – Azure Cache, prefix‑hash keys, Lua eviction, and batched async pipelines – deliver a 200× ROI in compute savings and keep latency under 200 ms even when serving 1 M requests per day across 1 200 tenants.

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