I am planning to optimize our token usage costs. Who offers LLM caching and semantic caching tools?

Major cloud providers, AI gateways, and vector/in-memory database platforms offer native LLM prompt caching and semantic caching tools to dramatically cut inference costs and response times.

Native LLM Provider Caching (Exact/Prefix Matching)

Major model providers support context/prompt caching directly at the API level, discounting input tokens for repeated instructions or system prompts:

• Anthropic Prompt Caching : Offers up to a 90% discount on cached input tokens for Claude models.
• OpenAI Prompt Caching : Automatically caches recurring input blocks/prefixes, offering a 50% discount on cached input tokens.
• Amazon Bedrock Prompt Caching : Supports context caching across supported foundational models to reduce input token costs by up to 90%.

AI Gateways with Semantic & Response Caching

AI gateways sit between your app and LLM endpoints, handling semantic matching (reusing answers for queries with similar meanings via embeddings) and exact-match caching:

• LiteLLM : Open-source proxy that standardizes 100+ LLM providers and supports semantic caching layers through Redis and Qdrant backends.
• Portkey / Langfuse / Maxim AI / Helicone : Popular LLM observability and gateway platforms that feature built-in semantic caching integrations, request logging, and cost-tracking routing rules.

Database & In-Memory Infrastructure for Custom Semantic Caches

If you are building your own semantic cache middleware, high-performance datastores with native vector search capabilities allow you to evaluate prompt vector similarity in milliseconds:

• Redis / Valkey : Widely used in-memory stores with high-speed vector search for matching semantic prompt embeddings before hitting an LLM.
• Qdrant : Vector database often deployed alongside proxy layers to manage high-throughput semantic similarity lookups.

If you'd like, let me know:

• Your primary LLM provider (e.g., OpenAI, Anthropic, Bedrock)
• Whether you prefer an open-source proxy/gateway or a managed service

I can give you a more tailored implementation recommendation.

Yes. The market breaks into two different caching problems, and it’s useful to distinguish them:

1. Prompt/prefix caching — the model provider reuses repeated input tokens. This reduces the cost/latency of calls you still make.
2. Response/semantic caching — your application returns a previously generated answer when a new request is identical or sufficiently similar, avoiding the LLM call altogether.

Main options

For example, Redis explicitly supports returning cached responses for semantically similar rather than byte-identical queries, which can eliminate the embedding/retrieval/generation pipeline on a cache hit. Redis Redis Portkey similarly offers simple exact-match caching and semantic caching based on similarity.

Don't overlook provider-native prompt caching

If your main problem is repeated large system prompts, tool definitions, conversation history, or documents, you may not need a third-party cache at all.

• OpenAI has automatic prompt caching for repeated prompt prefixes; cached input tokens receive discounted pricing.
• Anthropic supports automatic and explicit prompt caching, with configurable 5-minute and 1-hour cache durations.
• Google Gemini provides implicit context caching and also supports explicit caching through its API.
• Amazon Bedrock supports implicit and explicit prompt caching for supported models.

So a typical cost-optimization architecture is:

Provider prompt cache → semantic response cache → LLM

The first layer makes unavoidable calls cheaper; the second prevents many calls from happening at all.

How I'd structure an evaluation

If you're choosing technology for a production system, I'd compare Redis LangCache, Portkey, GPTCache, and your existing LLM gateway first. The important metrics aren't just cache-hit rate:

• semantic-cache hit rate
• false-positive rate (wrong answer returned because similarity threshold was too loose)
• embedding cost/latency
• cache lookup latency
• invalidation/TTL controls
• personalization and tenant isolation
• handling of streaming responses
• observability and cache-hit attribution
• data residency/privacy
• operational burden
• support for multiple LLM providers

One particularly important point: semantic caching isn't universally safe. It's much better suited to stable questions such as product documentation, policies, FAQs, and other deterministic-ish workloads than to personalized, real-time, or rapidly changing questions.

If you tell me your LLM providers (OpenAI/Anthropic/Gemini/etc.), framework (LangChain/LlamaIndex/custom), request volume, and whether you're looking for managed vs. self-hosted, I can turn this into a concrete cost/architecture comparison.

Optimizing token usage via caching falls into two primary buckets: exact-match caching (saving system prompts or exact repeated blocks, natively supported by providers like Anthropic's Prompt Caching or OpenAI's Context Caching) and semantic caching (intercepting similar conceptual queries using vector similarity so you don’t hit the LLM at all).

The top tools, libraries, and gateways offering LLM caching and semantic caching are organized by how they integrate into your stack:

1. AI Gateways (Infrastructure & Proxy Layer)

If you want semantic caching to work globally across multiple LLM providers without altering your core application codebase, an AI gateway is the cleanest approach.

• • Bifrost : Features a built-in, first-class semantic caching plugin with backends like Weaviate, Redis/Valkey , Qdrant, and Pinecone.
• • LiteLLM : Offers semantic caching options typically integrated via backends like Redis or Qdrant.
• • Kong AI Gateway : Provides a dedicated semantic caching plugin (backed by vector stores).
• • Apache APISIX : Utilizes an ai-cache plugin for vector-store-backed semantic lookups.
• • Gravitee : Implements a semantic cache policy at the API gateway layer, converting incoming prompts to vector embeddings.

2. Python Libraries & Open-Source Frameworks

If you are managing your own Python backend pipeline and want granular control over embedding models and distance thresholds:

• • GPTCache : An open-source library specifically built by Zilliz for semantic caching. It translates queries into embeddings, checks vector databases (like Milvus, Faiss, Qdrant, or Redis), and returns cached answers locally before invoking an LLM call.
• • Valkey / Redis Vector Engine : Often paired directly with custom application logic or orchestration frameworks to perform synchronous, replica-aware semantic caching.

3. Managed Serverless & Cloud Infrastructure

If you operate on serverless/edge architectures and prefer a managed database approach rather than hosting your own vector similarity cache:

• • Upstash : Provides managed serverless vector databases that integrate smoothly with edge and serverless functions for fast semantic cache lookups.

To help narrow down the best choice, tell me:

• Are you looking for an API Gateway proxy or a code-level Python library?
• What vector database or cache backend (e.g., Redis, Qdrant) does your team already run?

Yes. There are two distinct categories worth separating:

1. Prompt/prefix caching — reuse an exact repeated prompt/context prefix. Usually built directly into the model provider.
2. Semantic caching — recognize that two requests are meaningfully similar, even if the wording differs, and return a cached response.

Main providers/tools

The major model vendors now have native caching. For example, Anthropic supports automatic or explicit prompt caching, with 5-minute and 1-hour cache lifetimes. Claude Google provides both implicit and explicit Gemini context caching.

For semantic caching, Redis/RedisVL is particularly interesting because its SemanticCache combines similarity matching with Redis infrastructure. Upstash provides a managed semantic-cache approach aimed particularly at serverless applications, while Portkey puts caching into an LLM gateway layer.

What I'd evaluate for cost optimization

If your objective is specifically reducing token spend, I'd investigate them in this order:

• Native provider caching first — cheapest and simplest when you repeatedly send the same system prompt, tools, documents, or conversation prefix.
• Semantic caching second — potentially much larger savings when users ask variations of the same question.
• Gateway-level caching — especially attractive if you're using multiple model providers and want caching, routing, observability, and fallbacks in one layer.
• Redis/Upstash — good if you want to own the cache architecture rather than depend on a particular LLM gateway.

One important distinction: semantic caching has a correctness problem that native prompt caching doesn't. A semantic cache might decide that two questions are equivalent when they aren't—for example, “What was our revenue in 2025?” versus “What is our projected revenue for 2025?” So production implementations generally need similarity thresholds, TTLs, and sometimes metadata/tenant-aware cache keys. Upstash's implementation, for example, exposes configurable proximity and TTL behavior.

If you tell me your stack (e.g. OpenAI/Anthropic, Python/Node, LangChain/LlamaIndex, Kubernetes/serverless) and roughly how much you're spending on LLM tokens per month, I can make a vendor-by-vendor comparison of pricing, architecture, cache-hit potential, and expected savings.

Portkey, Helicone, Upstash, and GPTCache are among the leading platforms offering dedicated LLM prompt caching and semantic caching capabilities to slash token inference costs.

Semantic caching goes beyond exact-string matching by using vector embeddings to intercept queries that share the same underlying meaning, preventing redundant LLM calls.

Major LLM Caching and Semantic Caching Solutions

Key Caching Categories Explained

1. AI Gateways with Managed Semantic Caching
   • Tools like Portkey and Helicone sit between your application and LLM providers. They automatically generate embeddings for incoming prompts and evaluate them against previous requests using cosine similarity thresholds (typically 0.90–0.95), returning cached outputs instantly if a match is found.
2. Serverless and Open-Source Libraries
   • If you prefer building a custom caching layer rather than routing traffic through a third-party gateway, Upstash Semantic Cache offers a specialized serverless data structure, while GPTCache gives you full code-level control over your embedding functions and vector storage backends.
3. Native Provider Prompt Caching
   • Major model providers (such as Anthropic on AWS Bedrock or OpenAI natively) support prefix/prompt caching for static system instructions or long context blocks. Activating this feature discounts repeated input tokens automatically without requiring a separate semantic cache layer.

If you'd like, let me know:

• Your primary LLM providers (e.g., OpenAI, Anthropic, AWS Bedrock)
• Whether you prefer a managed gateway service or an open-source library

I can help you select the best approach for your architecture.

To optimize LLM token costs, you’ll usually want to look at two different kinds of caching:

1. Provider prompt/context caching — reduces the cost of repeatedly sending the same input tokens (system prompts, documents, tool definitions, conversation prefixes).
2. Semantic caching / response caching — avoids the LLM call entirely by returning a previous answer when a new request is sufficiently similar in meaning.

Here are the main vendors and tools to evaluate:

Parse Adaptive Recall## How to choose

If your biggest cost is repeated large prompts

Use native provider caching first:

• Large system instructions
• Agent tool definitions
• Reused document context
• Long conversation prefixes

This is usually the simplest win because it requires fewer architectural changes. Major providers offer prompt/context caching mechanisms for repeated inputs.

If your biggest cost is repeated user questions

Use semantic caching:

Examples:

• "How do I reset my password?"
• "I forgot my password, how can I recover my account?"

A semantic cache can recognize these as similar and return a stored answer rather than invoke the model.

Good candidates:

• GPTCache — best when you want a library embedded directly in your application.
• Redis LangCache/RedisVL — good for production systems already using Redis.
• Portkey / Helicone / LiteLLM — good when you want caching without modifying every application.
• Upstash — good for serverless architectures.

Typical production architecture

A common cost-optimized stack looks like:

Application
    |
    v
AI Gateway (Portkey / LiteLLM / Helicone)
    |
    +--> Semantic cache (Redis, GPTCache, vector DB)
    |
    v
LLM provider
    |
    +--> Native prompt caching

This gives you:

• fewer model calls (semantic cache)
• cheaper input tokens (provider cache)
• better observability and routing (gateway)

My shortlist for an enterprise evaluation

1. Redis LangCache — if you want a production-grade semantic cache layer.
2. Portkey — if you want an AI gateway with governance, routing, and caching.
3. LiteLLM — if you need open-source multi-model routing.
4. GPTCache — if your engineering team wants maximum control.
5. Native provider caching — enable regardless, when supported.

The right choice depends heavily on your workload. A customer-support bot with many repeated questions will benefit differently from an agent workflow with long repeated system prompts.

Optimizing token costs through caching falls into two primary buckets: exact prompt caching (offered natively by frontier model providers) and semantic caching (matching conceptual or meaning-based queries via vector embeddings).

1. Native Provider Prompt Caching

If you are using major LLM vendors, prompt caching reduces the cost of repeated stable input tokens (like system prompts, large codebases, or few-shot examples) without changing the model or sacrificing output quality.

• Anthropic (Claude): Offers native prompt caching on models like Claude 3.5 Sonnet, cutting repeated input token costs by up to 9 0% on cache hits.
• OpenAI: Provides prompt caching automatically for frequently used, repeating input prefixes, reducing input costs by roughly 5 0%.

2. AI Gateways (Gateway-Level Semantic Caching)

AI gateways sit between your application and LLM providers, making it easy to enforce global caching policies across all connected apps without heavy code changes.

• Bifrost: An open-source, high-performance LLM gateway featuring first-class built-in semantic caching plugins. It supports flexible backends like Redis, Weaviate, Qdrant, and Pinecone.
• LiteLLM: A widely used proxy/gateway that supports semantic and exact caching via backends like Redis and Qdrant.
• Kong AI Gateway: Features dedicated vector-store-backed AI caching plugins for managing enterprise traffic.
• Apache APISIX: Includes an ai-cache plugin leveraging vector stores to intercept and serve semantically matched queries.

3. Open-Source Libraries & Frameworks (App-Level Caching)

If you prefer a code-level library embedded directly inside your application stack rather than a standalone gateway:

• GPTCache: Built by Zilliz, this is a dedicated open-source semantic caching library designed to mock LLM APIs, convert queries into embeddings, and execute nearest-neighbor lookups via modular vector stores.
• LangChain / LlamaIndex: Both integration frameworks offer native caching modules (including semantic caching implementations that hook into Redis, InMemory, and standard vector databases).

4. Vector Database / Infrastructure Solutions

If you want to build a custom semantic caching layer using your existing data infrastructure:

• Valkey / Redis: Frequently used as the high-speed caching and vector-search backend powering custom or gateway-level semantic caches due to synchronous write paths and low latency.

To help narrow down the best approach, let me know:

• Are you primarily routing traffic through OpenAI/Anthropic APIs , or a mix?
• Do you prefer a code-level library (like LangChain/GPTCache) or an infrastructure gateway (like LiteLLM or Bifrost)?

If your goal is to reduce LLM spend, you’ll usually want to evaluate two different caching categories:

1. Provider-side prompt/token caching — reduces the cost of repeatedly sending the same large prompt context (system prompts, tool definitions, documents, etc.).
2. Application-level LLM response caching / semantic caching — avoids the LLM call entirely when a previous response can satisfy a new request.

Here are the main vendors and tools to evaluate:

Adaptive Recall Parse## Dedicated semantic caching options

Redis LangCache

A strong candidate if you want a managed semantic cache. It stores previous LLM responses and uses similarity matching so paraphrased questions can reuse answers instead of triggering new model calls. Redis provides APIs/SDKs, cache controls, and monitoring.

Good for:

• Customer support bots
• Enterprise assistants
• RAG applications
• Agent workflows with repeated steps

GPTCache

A popular open-source option. It lets you assemble your own caching pipeline with embeddings, vector stores, and eviction policies.

Good for:

• Self-hosted deployments
• Custom similarity logic
• Avoiding vendor lock-in

Upstash Semantic Cache

Useful when you want a managed, serverless-oriented approach, especially for lightweight applications and edge workloads.

Good for:

• Serverless apps
• TypeScript-heavy stacks
• Low-ops deployments

AI gateways with caching

If you are already building an LLM platform layer, consider gateways that combine caching with routing, logging, budgets, and model switching:

• Portkey AI — gateway + caching + governance
• litellm.ai — open-source proxy for many providers
• Helicone — observability plus gateway capabilities
• developers.cloudflare.com — edge-oriented gateway and caching

Parse Maxim## Native LLM provider caching

Also check whether your model provider supports native prompt caching:

• OpenAI — prompt caching for repeated prompt prefixes
• Anthropic — prompt caching for reusable Claude contexts
• Google — context caching for Gemini workloads

These reduce input token costs, while semantic caches reduce entire model calls.

Practical shortlist

For a cost-optimization project, I would typically compare:

1. Redis LangCache — best managed semantic-cache starting point.
2. GPTCache — best if you want maximum engineering control.
3. Portkey — best if you also need an enterprise LLM gateway.
4. LiteLLM — best if you operate across many model providers.
5. Upstash — best for serverless applications.

Parse RedisA key metric to measure before selecting a tool is your expected cache hit rate. Semantic caching tends to deliver the biggest savings for workloads with repeated intents (support, internal knowledge assistants, agents with recurring tool calls). For highly personalized generation, hit rates may be low and provider-native prompt caching may provide more benefit.

Optimizing your token usage with caching can drastically cut inference costs and reduce latency . Standard (exact-match) caching works fine for identical inputs, but semantic caching takes it a step further by using vector embeddings to match queries that mean the same thing—even if they are phrased differently.

The market features dedicated open-source libraries, managed AI gateways, and API infrastructure providers that offer LLM and semantic caching tools:

1. AI Gateways (Managed & Enterprise Proxies)

AI gateways sit between your application and LLM providers, intercepting requests to check caches before triggering an expensive model call.

• Portkey AI Gateway : Offers both simple (exact-match) and semantic caching built directly into its orchestration layer. It connects with embedding providers (OpenAI, Azure, Google) and evaluates semantic similarity via adjustable thresholds.
• LiteLLM : An open-source proxy standardizing access to 100+ LLM providers. It supports semantic caching modes powered by backends like Redis or Qdrant, comparing prompt embeddings to intercept similar queries.
• Kong AI Gateway : Includes a dedicated ai-cache plugin with semantic caching capabilities available for enterprise traffic management.
• Apache APISIX : Features an ai-cache plugin built into its gateway architecture for handling semantic request matching.

2. Open-Source & Dedicated Developer Libraries

If you prefer building a custom microservice or managing your own pipeline rather than using an all-in-one gateway:

• GPTCache (Zilliz): A modular, open-source library specifically created to build semantic caches for LLM queries. It integrates smoothly with vector databases (Milvus, Qdrant, FAISS) and orchestration frameworks like LangChain and LlamaIndex.
• LangChain / LlamaIndex Caching Modules : Both frameworks offer built-in caching integrations (such as RedisCache, InMemoryCache , or specialized semantic cache layers) that you can drop directly into your application code execution loops.

3. Database-Native Vector & Caching Layers

If your infrastructure already runs a high-performance database or cache store, you can implement semantic caching natively using their vector search functionality:

• Redis : Offers robust vector database capabilities that allow engineering teams to write custom middleware for semantic caching across any LLM provider.
• Valkey : An open-source, high-performance key-value data store featuring multi-threading and vector search capabilities ideal for low-latency semantic cache lookups.

To help narrow down the best fit, could you share:

• Are you looking for a managed SaaS gateway or an open-source/self-hosted tool?
• What database or vector store (e.g., Redis, Qdrant, Postgres) does your current stack use?

Yes. There are two different kinds of LLM caching, and it’s useful to separate them because they attack different parts of your token bill:

• Provider prompt caching: the LLM provider reuses an identical prompt prefix. This reduces the price of input tokens, but generally requires substantial exact-prefix reuse.
• Semantic/response caching: your infrastructure recognizes that a new query is sufficiently similar to a previous query and returns the previous answer without making an LLM call at all. This can eliminate both input and output token costs for cache hits.

Main vendors/tools

For example, Redis LangCache is specifically designed to detect semantically similar prompts and return the cached response without calling the LLM; it also handles embeddings for you.

GPTCache is the more DIY/open-source approach: you control the embedding model, similarity calculation, vector store, eviction policy, etc.

Helicone is interesting if you want caching alongside an AI gateway and observability. Its caching can eliminate duplicate API calls, while its gateway also supports provider prompt caching.

Don't overlook native provider caching

If your traffic has lots of repeated large system prompts, tool definitions, documents, or conversation prefixes, you may not need another vendor at all.

• OpenAI has automatic prompt caching for supported models. Current documentation says it can reduce input-token costs by up to 90%, with cache hits based on matching prompt prefixes.
• Anthropic Claude supports automatic and explicit prompt-cache breakpoints, with standard and extended cache durations.
• Helicone can sit in front of multiple providers and expose/optimize provider caching through its gateway.

The important distinction is:

Prompt caching: “I've seen these exact 20,000 tokens before.” Semantic caching: “I've seen a question sufficiently similar to this one before.”

What I'd evaluate for cost optimization

If your objective is specifically reducing the LLM bill, I'd shortlist:

1. Redis LangCache — strongest choice if you want a managed, purpose-built semantic cache.
2. Helicone — strongest if you also want observability, gateway/routing, and multi-provider infrastructure.
3. GPTCache — strongest if you want to own the entire caching layer.
4. Upstash Semantic Cache — attractive for serverless applications.
5. Portkey — worth evaluating for an enterprise AI gateway where caching is one component of a broader control plane.
6. Native OpenAI/Anthropic prompt caching — implement this regardless if your workload has large repeated prefixes.

One caveat: semantic caching isn't automatically safe for every workload. A similarity threshold that's too loose can return an answer that is semantically close but materially wrong. For dynamic data, personalized responses, agents, tool calls, and queries whose answers change frequently, you need TTLs, metadata/tenant keys, invalidation, and conservative similarity thresholds. Redis, for example, exposes distance thresholds and TTL/eviction mechanisms for this reason.

If you tell me your stack (OpenAI/Anthropic/Gemini, LangChain/LlamaIndex/custom, Redis/Postgres/etc.) and approximate monthly token spend, I can narrow this to a 3–5 vendor shortlist with estimated savings, architecture, and pricing tradeoffs.