Buy Semantic LLM Cache.
Drop LLM cost and latency on repeat prompts without rewriting application code. Sub-15ms p95 latency.
Drop-In Integration Stage
OpenAPI 3.1 & Gateway Compatiblefrom openai import OpenAI
client = OpenAI(
base_url="https://api.youstostore.com/v1",
api_key="sk_live_your_key"
)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "Summarize user feedback for Q3 SaaS churn"}],
extra_body={"cache": {"mode": "semantic", "threshold": 0.96}}
)
print(response.choices[0].message.content)Calculate your monthly token savings.
Estimate how much you save on repetitive and semantic prompts with our 1-line proxy.
Test semantic similarity matching in real time.
Try exact seed prompts vs paraphrased queries to observe instantaneous 14ms memory cache hits.
Developer / Hobby
Growth / Startup
RECOMMENDEDScale / Enterprise
Stop paying an LLM to answer the same question twice
Semantic LLM Cache sits in front of deterministic chat-completion workloads. It returns a tenant-local cached response when a request is an exact or sufficiently similar match, and sends a miss to your configured OpenAI-compatible provider. If the primary provider fails, the service can try one configured fallback.
Engineering Pain & Fragility
LLM applications often repeat predictable work. A support assistant answers several phrasings of the same returns question. An internal tool summarizes an unchanged policy. A product generates the same explanation for many users. Each request can trigger another upstream model call even when an acceptable answer already exists.
A basic key-value cache only catches byte-for-byte duplicates. Building a safer semantic cache is more involved: prompts must be scoped to the right customer, match quality needs a controllable boundary, stale entries must expire, and non-deterministic requests should not quietly reuse old output. Provider outages add another set of routing and error-handling concerns.
Semantic LLM Cache handles that narrow layer. It is designed for repeatable, read-only completions where reuse is intentional. It is not a general LLM observability platform or an unrestricted proxy for every model feature.
What the API Solves
Send an OpenAI-style request to `POST /v1/chat/completions` with a model, role/content messages, temperature, and cache policy. The service builds a tenant-scoped representation of the conversation and checks eligible entries that have not passed their TTL.
In `exact` mode, only the same canonical conversation is reused. In `semantic` mode, a similar conversation may be reused when its score meets your threshold. The allowed threshold range is `0.90` to `1.00`, with `0.96` as the default. TTL can be set from 60 seconds to 2,592,000 seconds (30 days). Set the mode to `disabled` when a request must always go upstream.
Caching is limited to deterministic requests. If the temperature is non-zero and cache mode is `exact` or `semantic`, the API rejects the request with `422 unsafe_cache_request`. A non-zero-temperature request is accepted only when caching is disabled.
How it works. Step by step.
Send the completion request.
Point the eligible part of your application at the cache endpoint and pass `X-API-Key` with the request.
Choose the cache policy.
Select `exact`, `semantic`, or `disabled`; set a semantic threshold when needed; and provide a TTL.
Check within the tenant boundary.
The service considers entries only from the authenticated tenant. It never searches another tenant's cache.
Return a hit or call upstream.
A qualifying entry is returned directly. On a miss, the service calls the configured primary OpenAI-compatible route and stores the result for the requested TTL when caching is enabled.
Use the fallback when needed.
If the primary route fails, the service tries one configured compatible fallback before returning a provider-unavailable error.
Measure reuse.
Read the savings endpoint to inspect request count, hit count, avoided upstream tokens, and an estimated dollar value.
Built for enterprise production standards.
OpenAI-compatible `POST /v1/chat/completions` interface for role/content chat messages
Per-request exact, semantic, or disabled cache mode
Configurable semantic threshold from `0.90` through `1.00`
Configurable TTL from one minute through 30 days
Tenant-local entries and savings counters
Deterministic-cache guardrail that rejects non-zero temperature
Primary route plus one compatible fallback route
Cache status, similarity, and avoided-token metadata in each response
Aggregate savings reporting
API-key authentication, request IDs, structured JSON errors, rate limiting, usage metering, health/readiness probes, and Prometheus metrics
Ideal use cases & engineering workflows.
Semantic LLM Cache is built for engineering and platform teams running metered LLM features with meaningful repetition. It fits AI SaaS products, support systems, internal knowledge assistants, and workflow products that already use OpenAI-style chat completions. The strongest fit is a team that can identify deterministic traffic and define when two prompts are safe to treat as equivalent.
It is a poor fit when every output must be novel, the application relies on non-zero temperature, requests perform side effects, or tool calls must be cached automatically.
Repeated support answers
Reuse approved answers to closely related questions about returns, account settings, product policies, or other stable material. Keep the TTL aligned with how often the source content changes.
Read-only product explanations
Cache deterministic explanations, summaries, and classification rationales where the underlying inputs repeat and serving an equivalent prior answer is acceptable.
Internal knowledge assistants
Reduce duplicate upstream calls for recurring employee questions. Tenant scoping keeps one customer's cache from becoming another customer's answer source.
Provider continuity
Configure a primary and one compatible fallback endpoint for eligible requests. The fallback is a bounded continuity measure, not a multi-provider routing marketplace.
Cost review
Use the savings ledger to compare requests, hits, and avoided upstream tokens over time. Validate the estimated dollar value against your actual model mix and provider pricing.
Why buy instead of building internally?
The cache lookup is only one part of the work. A production-facing implementation also needs tenant boundaries, TTL enforcement, provider timeout handling, fallback order, authentication, rate limits, error envelopes, usage accounting, and operational probes. Those pieces are easy to postpone and expensive to debug after traffic grows.
Buying this API makes sense when you want a narrow integration rather than another internal service to operate. Your team still decides which calls are deterministic, what threshold is acceptable, and how long an answer stays valid. Build when semantic reuse is core intellectual property, you need custom matching or invalidation, or broad routing and observability belong in the same gateway.
Security, privacy and operational integrity.
Each protected request uses `X-API-Key`. API keys are stored as SHA-256 digests by the provided service adapters, and revoked keys no longer authenticate. Cache records and counters are addressed inside tenant-specific namespaces. Cached entries expire according to the TTL on the request.
The request middleware assigns or preserves an `X-Request-ID`, returns it in the response, and includes it in structured errors. Application request logs record method, route, status, and request context rather than prompt bodies. Prometheus metrics, liveness, and readiness endpoints support deployment monitoring.
Production configuration requires HTTPS URLs for both the primary and fallback providers. Keep provider credentials in your deployment secret manager and apply your own access, retention, and data-processing policies to prompt and response content.
Architectural Scope & Production Boundaries
Questions & answers for engineering leads.
The main route follows the OpenAI chat-completions response shape and accepts model plus role/content messages. Check your SDK's ability to set a custom base URL and pass the required `cache` object. Features outside the documented schema, such as automatic tool-call caching, are not supported.
Deploy Semantic LLM Cache in minutes.
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