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

Yes. There are two different caching layers worth separating:

• Prompt/prefix caching: avoids re-processing repeated input tokens, but the LLM still runs and generates a response.
• Semantic/response caching: recognizes that a new request is sufficiently similar to a previous request and returns the cached answer without calling the LLM at all. This can produce much larger savings, but requires careful similarity/quality controls.

Main vendors/tools

Redis's LangCache is particularly interesting if your primary objective is semantic caching rather than building the machinery yourself: it handles embeddings and returns cached responses without invoking the LLM on a semantic hit.

TrueFoundry similarly distinguishes gateway caching from provider prompt caching: semantic/exact cache hits can eliminate the model invocation entirely, whereas provider prompt caching merely reduces the cost of processing repeated input.

Provider-native prompt caching

You should also look at the LLM providers themselves before buying a caching layer.

Amazon Bedrock, for example, supports prompt caching for multiple models and says it can reduce costs by up to 90% for supported workloads. It caches reusable prompt prefixes rather than complete responses.

This is complementary to semantic caching. A useful architecture can therefore be:

Application → semantic cache → LLM gateway → provider prompt cache → model

A semantic hit skips everything downstream. A semantic miss can still benefit from provider-level prompt caching.

What I'd evaluate first

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

1. Redis LangCache — probably the cleanest dedicated semantic-cache option.
2. Portkey — strong choice if you also want an LLM gateway, routing, fallbacks, observability, and caching.
3. Helicone — attractive if usage analytics/observability are as important as caching.
4. GPTCache — best if you want to own the infrastructure and tune the semantic matching yourself.
5. TrueFoundry — worth evaluating for a more enterprise-oriented gateway/platform.
6. Upstash — particularly attractive for serverless applications.

There's also a useful distinction in the economics: exact-match caching is almost risk-free but has limited hit rate; semantic caching can have a dramatically higher hit rate but introduces the risk of returning an inappropriate cached answer. You therefore need similarity thresholds, namespace/user isolation, TTLs, and ideally cacheability rules for dynamic or personalized queries.

If you tell me your LLM providers (OpenAI/Anthropic/Gemini/etc.), approximate requests/month, average input/output tokens, and whether you're already using Redis/LangChain/LiteLLM, I can narrow this down to the 2–3 options most likely to produce meaningful savings and estimate the potential ROI.

Amazon Bedrock, for example, supports prompt caching for multiple models and says it can reduce costs by up to 90% for supported workloads. It caches reusable prompt prefixes rather than complete responses.

This is complementary to semantic caching. A useful architecture can therefore be:

Application → semantic cache → LLM gateway → provider prompt cache → model

A semantic hit skips everything downstream. A semantic miss can still benefit from provider-level prompt caching.

What I'd evaluate first

Optimizing token usage via caching generally splits into two categories: Exact Prompt Caching (native features offered directly by model providers for stable input prefixes) and Semantic Caching (using vector similarity to catch paraphrased or similar user queries and bypass LLM inference entirely).

1. Native Provider Prompt Caching (Exact Match / Prefix Caching)

Before building an external semantic caching layer, you can leverage native infrastructure from model providers that discount or cache repeated stable input tokens (like large system prompts, few-shot examples, or long documents).

• Anthropic (Claude): Offers robust prompt caching which reduces the cost of repeated stable input tokens by up to 90% on cache hits. It works automatically by marking cache control blocks in your API requests.
• OpenAI: Offers automatic prompt caching for frequently used prefixes and larger static contexts, reducing costs on repetitive input tokens by roughly 50%.
• Google Gemini / Vertex AI: Supports context caching mechanisms allowing developers to store large amounts of repetitive context (like massive codebases or video/audio transcripts) to reduce redundant billing.

2. Open-Source Libraries & Frameworks (Semantic Caching)

If you want to build custom semantic caching inside your application code using vector embeddings and similarity thresholds:

• GPTCache (by Zilliz): An open-source Python library designed specifically to build a semantic cache for LLM responses. It integrates seamlessly with frameworks like LangChain and LlamaIndex, allowing you to choose your own embedding models and vector/scalar storage backends.
• Upstash Semantic Cache: An open-source and managed fuzzy key-value store built on top of Upstash Vector . It handles embedding generation natively within the vector layer, simplifying setup.

3. AI Gateways & Infrastructure (Managed Semantic Caching)

If you route your LLM traffic through an infrastructure proxy or gateway, semantic caching is often available out-of-the-box as a configuration toggle or plugin:

• Portkey: An AI gateway and observability platform that features built-in semantic caching, letting you set similarity thresholds and instantly serve repeated/similar user intents from a cache layer.
• Bifrost: A high-performance, open-source AI gateway that provides semantic caching as a built-in plugin.
• NeuralTrust (TrustGate): Enforces gateway-layer caching and security policies consistently across connected applications.

4. Custom Database-Backed Implementations

Many engineering teams choose to build lightweight custom semantic caches using high-performance data stores that support vector indexing:

• Redis / Valkey: Frequently paired with an embedding model (like text-embedding-3-small or a local HuggingFace model) to perform fast cosine-similarity lookups on incoming prompt vectors before hitting downstream LLM endpoints.
• Vector Databases (Qdrant, pgvector, Milvus): Can be structured to store [Query Embedding] -> [LLM Response] , utilizing a threshold proximity score to decide whether a query is a "hit" or a "miss".

If you'd like to narrow this down, tell me:

• Are you primarily using a specific provider like OpenAI or Anthropic?
• Do you prefer a managed gateway or an open-source library inside your codebase?

I can recommend a precise implementation path or architecture pattern.

Optimizing token usage through caching is one of the most effective ways to slash LLM inference bills and latency . Solutions generally fall into three buckets: LLM Gateways/Proxies (easiest drop-in setup), Managed Database & Vector Layers (infrastructure-level), and Open-Source Libraries (custom implementation).

1. AI Gateways & Proxies (Built-in Caching)

These tools sit between your application and the LLM providers (OpenAI, Anthropic, etc.), offering configuration-driven exact and semantic caching out of the box.

• Portkey : An enterprise LLM gateway with native support for semantic and exact caching, performance tracking, and fallback routing.
• Bifrost : A high-performance, open-source AI gateway that includes semantic caching directly as a built-in plugin.
• Agnostic/Enterprise Gateways (like LiteLLM or Langfuse integration layers): Often feature basic proxy-level prompt caching or Redis-backed response caching hooks.

2. Managed Vector & Caching Infrastructure

If you manage your own data infrastructure or want a managed service provider to handle the vector math for semantic similarity lookups.

• Upstash Semantic Cache : A managed semantic caching layer built directly on top of Upstash Vector , making serverless semantic caching straightforward.
• Redis / Valkey : Frequently used in production (e.g., scaled architectures like Walmart's semantic cache) by combining standard in-memory L2 lookups with an L1 vector similarity search module.
• Zep : An AI memory and knowledge graph layer that handles conversational context and caching for agents.

3. Open-Source Python Libraries

If you prefer managing the caching logic within your own codebase without routing traffic through a third-party gateway.

• GPTCache : An open-source library created by Zilliz that integrates seamlessly into Python/LangChain application code, allowing you to mimic a Redis/Milvus-backed semantic cache for LLM prompts.
• LangChain / LlamaIndex Cache modules : Both frameworks have native integrations with various backends (Redis, InMemory, SQLite) to enable exact-match or semantic prompt caching.

If you can share which LLM providers you are using (e.g., OpenAI, Anthropic) and whether your app is stateless or uses RAG/Agents , I can recommend the most seamless integration path for your stack.

If your goal is to reduce LLM token spend, there are two main caching approaches:

1. Provider-side prompt/context caching — reuses identical prompt prefixes or context blocks (useful for long system prompts, documents, agent context).
2. Application-level response caching / semantic caching — avoids the LLM call entirely when a new request is similar in meaning to a previous one.

Here are the main vendors and tools to evaluate:

Parse Vercel## Shortlist by scenario

You want the easiest managed semantic cache

Redis LangCache

• Stores previous LLM responses and retrieves them based on semantic similarity.
• Handles embeddings, similarity matching, TTLs, and cache management.
• Works through APIs/SDKs rather than requiring you to build the vector-cache layer yourself.

Good for:

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

You want maximum control / self-hosting

GPTCache

• Open-source library designed specifically for LLM response caching.
• Lets you choose embedding models, vector stores, and similarity logic.
• Useful if you want to own the infrastructure.

You run serverless applications

Upstash Semantic Cache

• Designed for serverless/edge applications.
• Minimal infrastructure management.
• A common fit for Next.js/Vercel-style deployments.

You need an AI gateway, not just caching

Portkey or LiteLLM

• Add caching alongside: - model routing
• retries/fallbacks
• budgets
• usage tracking
• provider abstraction

Useful if you have many teams or multiple LLM providers.

Also consider native model-provider caching

For long prompts, many teams combine semantic caching with provider prompt caching:

• Prompt caching → saves money on repeated large context prefixes.
• Semantic caching → eliminates whole model calls for repeated intents.

They solve different problems and are often complementary.

Evaluation checklist

When comparing vendors, measure:

• Cache hit rate: percentage of requests avoided
• False-positive rate: cases where a cached answer is inappropriate
• Latency: cache lookup vs model generation time
• Embedding cost: semantic matching adds embedding overhead
• TTL strategy: how quickly answers become stale
• Privacy controls: whether prompts/responses leave your environment
• Multi-tenant isolation: important for enterprise apps

For many production teams, a practical starting shortlist is:

1. Redis LangCache — managed semantic caching
2. Portkey or LiteLLM — if you also need an LLM gateway
3. GPTCache — if you want self-hosted flexibility
4. Upstash — if you are building serverless applications

For reducing LLM token costs, you’ll usually want to evaluate two different caching layers:

1. Provider prompt/token caching — reduces the cost of repeatedly sending the same large prompt context (system prompts, documents, tool definitions, etc.).
2. Application-level response caching / semantic caching — avoids calling the LLM at all when a previous answer can be reused (exact match or meaning-based match).

Here are the main vendors and tools in this space:

Dedicated semantic caching options

Redis LangCache

A strong managed option if your goal is production cost reduction. It stores prior LLM responses and uses semantic similarity to reuse answers for similar queries. It provides cache management, TTL/eviction controls, and monitoring.

Good for:

• customer support bots
• enterprise assistants
• RAG systems
• agent workflows

GPTCache

An open-source semantic cache layer. It supports embedding-based similarity matching and multiple storage backends.

Good for:

• teams wanting full control
• experimenting before adopting a managed service

Upstash Semantic Cache

Useful for serverless architectures where you want a managed Redis/vector-style backend without running infrastructure.

---

AI gateways with caching

Helicone

Provides:

• LLM request proxying
• response caching
• prompt caching support
• cost analytics and tracing

Its response cache can return previously generated responses instead of making another model call.

Good for:

• teams already centralizing OpenAI/Anthropic/etc. traffic
• measuring cache hit rates and savings

Portkey

AI gateway approach:

• route requests
• manage providers
• add caching policies
• monitor spend

Good for enterprise deployments with multiple model vendors.

---

Framework-level options

LangChain

Supports:

• in-memory caching
• Redis caching
• semantic caching through integrations such as Redis semantic cache.

Good if your application is already built on LangChain.

LlamaIndex

Useful for RAG-heavy systems where caching can apply to:

• retrieval results
• embeddings
• LLM responses

---

Also consider native provider caching

Major model providers increasingly offer prompt/context caching:

• OpenAI — prompt caching for supported models
• Anthropic — cache control for reusable prompt blocks
• Google — context caching for Gemini

These reduce input token processing costs, while semantic caches reduce the number of LLM calls entirely.

---

Typical cost-optimization architecture

A common production stack looks like:

User request
      |
      v
Semantic cache (Redis / GPTCache / gateway)
      |
      | cache miss
      v
Prompt cache (OpenAI / Anthropic / Gemini)
      |
      v
LLM API
      |
      v
Store response + embedding

For many companies, the biggest savings come from combining:

• semantic caching for repeated user intents
• provider prompt caching for large repeated contexts
• routing to cheaper models when possible

If you share your stack (OpenAI vs Anthropic vs Gemini, RAG vs chatbot vs agents, request volume, and whether you need self-hosted), I can narrow this to a shortlist.

Optimizing token usage via caching is one of the highest-leverage ways to cut LLM inference costs . Solutions generally fall into three categories: AI Gateways (infrastructure proxies handling routing and caching globally), Open-Source Libraries (embedded directly into application code), and Database/Vector Layers (infrastructure you manage yourself).

1. AI Gateways (Managed & Open-Source Proxies)

Gateways sit between your application and LLM providers (OpenAI, Anthropic, etc.), intercepting traffic to apply caching, rate-limiting, and fallbacks transparently.

• Bifrost (by Maxim AI) : A high-performance, open-source AI gateway written in Go. It features a built-in dual-layer semantic caching plugin, virtual key governance, and ultra-low latency overhead.
• LiteLLM : A popular open-source Python proxy standardizing over 100 LLM providers. It supports semantic caching via backends like Redis or Qdrant using an embedding model and adjustable similarity thresholds.
• Kong AI Gateway : Extends the classic Kong API gateway with enterprise-focused plugins for model routing and semantic caching powered by vector databases.
• Cloudflare AI Gateway : A fully managed edge service running on Cloudflare's global network that offers native exact-match response caching, analytics, and rate limiting.
• TrueFoundry : Bundles an enterprise LLM gateway with a broader MLOps platform, supporting centralized semantic caching across multiple teams and services.

2. Open-Source Libraries & Application Frameworks

If you prefer an in-code approach rather than routing traffic through a separate gateway proxy, these libraries can be integrated directly into your application logic.

• GPTCache (by Zilliz) : An open-source Python library purpose-built for semantic caching. It lets you hook up custom embedding models and vector store backends (like Milvus, Qdrant, or Chroma) to cache responses locally or in your services.
• LangChain / LlamaIndex integrations : Both frameworks feature native semantic cache modules (e.g., RedisSemanticCache or integrations with SQLite/Postgres/PGVector) that you can drop straight into your chains or agent loops.

3. Managed Edge & Vector Database Backends

If you are building custom caching logic or want database-native vector search to handle proximity matching:

• Upstash Semantic Cache : A managed semantic caching layer built on top of Upstash Vector, designed specifically for serverless and edge environments with built-in embedding generation.
• Redis / Valkey : Widely used as the high-speed L2 backing store for semantic caching systems. By storing prompt embeddings and pairing them with a similarity search index, teams frequently roll their own lightweight semantic caches with sub-millisecond retrieval times.

To help narrow down the best fit, tell me:

• Are you looking for a self-hosted/open-source tool or a fully managed SaaS?
• What framework or language (Python, Go, Node.js) is your backend built on?

Yes. The market has split into three different caching layers, and it’s worth distinguishing them before choosing a vendor:

1. Provider prompt/prefix caching — avoids recomputing repeated prompt prefixes.
2. Exact-response caching — identical request → return the previous response without calling the LLM.
3. Semantic caching — a sufficiently similar request → return a prior response, even when the wording differs.

For reducing actual API token spend, #2 and especially #3 are usually the most interesting. Provider-side prompt caching is also valuable, but it generally discounts/reduces the cost of repeated input tokens rather than eliminating the model call.

Main vendors/tools

Redis is particularly interesting because LangCache is purpose-built as a managed semantic-cache service, rather than merely being a Redis database that you have to assemble into a cache yourself.

My shortlist

If your primary goal is token-cost reduction:

1. Redis LangCache — probably where I'd start for a dedicated semantic-cache evaluation. It gives you a managed semantic-cache layer without having to build the embedding → similarity search → cache-hit pipeline yourself.

2. Portkey — compelling if caching is only one part of a broader LLM infrastructure strategy. It combines gateway functionality with routing, retries, budgets, observability and caching.

3. GPTCache — good if you want maximum control and are comfortable operating the infrastructure. It's specifically designed around LLM response caching and can use embeddings/similarity search for semantic hits.

4. LiteLLM — particularly attractive if you also want a self-hosted gateway to standardize access to OpenAI, Anthropic, Gemini, local models, etc. Its caching story is more "assemble/configure the pieces" than a dedicated semantic-cache product.

5. Cloudflare AI Gateway — excellent for exact-match caching with minimal infrastructure. As of June 2026, its documented standard response cache is based on identical requests; Cloudflare says semantic caching is planned rather than being the primary current caching mechanism.

Don't overlook native provider caching

If you're using OpenAI/Anthropic/etc., you should evaluate native prompt caching before adding another caching layer. Prefix caching can reuse large, stable system prompts, tool definitions and conversation context. Cloudflare's current documentation, for example, notes that providers including Anthropic and OpenAI support this model of caching.

The important distinction is:

Prompt caching

large repeated prompt → cheaper processing

versus

Semantic caching

"How do I reset my password?" → cached answer

"What's the process for resetting my password?" → same cached answer

The second can eliminate the LLM call altogether, which is potentially a much larger cost reduction.

A practical architecture

For a production application, I'd seriously consider a layered approach:

Application → Semantic Cache → LLM Gateway → Provider

with:

• Semantic cache for repetitive/near-duplicate user questions
• Exact cache for deterministic requests
• Provider prompt caching for large stable prompts/context
• Gateway for routing, fallback, rate limits and spend controls
• Observability to measure cache hit rate and actual dollar/token savings

The key metric isn't simply "cache hit rate." You want to measure:

LLM calls avoided × average uncached cost

and separately:

cached input tokens × prompt-cache discount

because a 30% semantic hit rate can be enormously valuable for an expensive agent workload but almost irrelevant for a workload where responses are highly unique.

If you tell me your current stack (e.g. OpenAI/Anthropic, Python/Node, LangChain/LlamaIndex/direct SDK, cloud/self-hosted) and roughly monthly LLM spend, I can narrow this to the 3–4 caching products I'd actually evaluate, including architecture, pricing model, cache-hit considerations, and estimated savings.

Optimizing token spend through caching falls into three categories: native provider prompt caching (cheapest and lowest-risk for static prefixes), AI gateways/proxies (easy drop-in layers with exact/semantic matching), and infrastructure libraries/databases (for custom codebases).

1. Native Provider Prompt Caching (Exact Prefix Caching)

Before adding external tools, make sure you take advantage of native prompt caching if you reuse large system prompts, reference documents, or multi-turn chat histories. They cut input costs on the static prefix by up to 90%.

• Anthropic (Claude) : Automatically caches qualifying prefixes over a minimum token threshold if marked or structured correctly.
• OpenAI : Supports automatic context/prompt caching for supported models on repeated input blocks.
• Google Gemini & AWS Bedrock : Provide native context caching options for large-context workflows.

2. AI Gateways & Proxies (Gateway-Level Semantic Caching)

These sit between your application and LLM providers, intercepting requests to handle exact hashing and vector-based semantic similarity routing without changing your application core logic.

• Bifrost (by Maxim AI) : High-performance open-source AI gateway (written in Go) featuring dual-layer caching (exact hash matching + semantic vector search) supporting backends like Redis, Qdrant, and Pinecone.
• LiteLLM : Popular open-source Python proxy that standardizes 100+ LLM providers and supports semantic caching using Redis or Qdrant backends.
• Cloudflare AI Gateway : Fully managed edge-based gateway that lets you cache and manage LLM calls globally across Cloudflare's network.
• Kong AI Gateway : Extends standard API management with enterprise AI plugins supporting semantic intelligence and model routing via vector DBs.
• Helicone : Open-source/managed LLM observability and proxy platform that features integrated caching and cost tracking.

3. Infrastructure & Vector Databases (Database-Native Caching)

If you manage your own data infrastructure or run high-scale distributed caching, you can use specialized vector database features or dedicated caching layers.

• Redis / Valkey : Offers native vector search capabilities alongside traditional key-value storage. Tools like LangCache or LangChain's integration (RedisSemanticCache ) allow you to build low-latency semantic caches directly in Redis.
• Upstash : Provides Upstash Semantic Cache (leveraging Upstash Vector), a managed semantic layer ideal for serverless and edge environments that generates embeddings natively.

4. Open-Source Application Libraries

• GPTCache (by Zilliz) : A modular open-source Python library designed specifically to wrap around LLM clients (like OpenAI or LangChain), handling embedding generation, custom similarity evaluation, and cache evictions in-app.

To help narrow down the best fit, tell me:

• Are you looking for a managed cloud service , an open-source AI gateway , or a code library?
• Which LLM provider and model are driving the bulk of your current costs?

Optimizing token costs via caching typically involves three architectural layers: AI Gateways (drop-in proxy layers with built-in semantic caching), Database/Vector Vendors (infrastructure that lets you build your own semantic cache), and Open-Source Libraries/Frameworks (code-level implementations).

The primary providers and tools available for LLM exact-match and semantic caching are organized below by category:

AI Gateways & Proxies (Built-in Semantic Caching)

• Bifrost : An open-source, high-performance AI gateway (by Maxim AI) written in Go [1.13]. It features a built-in, dual-layer caching system combining exact hash matching (Layer 1) and vector similarity search (Layer 2) across various backends like Redis, Qdrant, and Pinecone [1.13].
• LiteLLM Proxy : A popular open-source Python proxy that standardizes calls to over 100 LLM providers [1.14]. It supports semantic caching using Redis or Qdrant as vector stores with configurable similarity thresholds and Time-to-Live (TTL).
• Kong AI Gateway : Extends traditional API management with an enterprise-grade AI Semantic Caching plugin, pairing with vector store backends like Redis to manage prompts at the gateway level [1.21].
• Cloudflare AI Gateway : A fully managed edge service running on Cloudflare's global network that handles basic caching, rate limiting, and analytics across multiple underlying model providers.
• TrueFoundry : Offers an enterprise LLM gateway featuring request-level semantic caching and custom namespace/threshold configuration headers [1.10, 1.15].

Database & Vector Infrastructure (Managed Semantic Caches)

• Redis / ElastiCache for Valkey : Highly utilized for production-grade semantic caching. Using vector search capabilities (such as Redis Stack or Amazon ElastiCache integrated with frameworks like LangChain), teams can match high-dimensional embeddings to bypass repeated LLM inference.
• Upstash Semantic Cache : A managed serverless semantic caching layer built directly on top of Upstash Vector [1.21]. It generates and indexes embeddings natively within the vector database, minimizing setup friction for edge and serverless apps [1.21].

Open-Source Libraries & Frameworks

• GPTCache : An open-source Python library created by Zilliz specifically designed for building customized semantic caching pipelines. It allows granular control over embedding models and local or distributed vector store choices.
• LangChain : Provides native caching modules (such as RedisSemanticCache or in-memory equivalents) that can be injected globally into application chains to store and retrieve responses based on semantic similarity.
• Zep : An open-source AI memory and knowledge graph layer that assists conversational agents by caching and managing contextual/semantic history over time.

If you can share your current tech stack (e.g., whether you use Python/Go, LangChain, or a specific cloud provider) and your anticipated traffic volume , I can help recommend which caching approach fits your architecture best.

Yes. There are two different categories worth separating:

1. Provider-native prompt caching — reuses the same token prefix/context and gives you a discounted input-token rate.
2. Application/semantic caching — sits in front of the LLM and returns a previous response when a new request is identical or semantically similar, potentially avoiding the LLM call entirely.

Main options

The important distinction for cost optimization

Suppose users ask:

"How do I reset my password?"

and later:

"What's the process for resetting my password?"

Native prompt caching generally won't treat those as the same request. It is primarily about reusing the same prompt prefix/context.

A semantic cache can recognize that the two requests have similar intent and potentially return the first response without making another LLM call. That's a much bigger potential cost reduction, but it introduces the risk of false cache hits—serving an answer that is similar but not actually appropriate. Recent research specifically highlights this as a security/reliability concern for semantic caches.

What I'd recommend

For a serious cost-optimization program, I'd use both layers:

Application
    │
    ▼
Semantic / exact response cache
    │  cache hit → return response ($0 LLM tokens)
    │
    ▼
LLM gateway
    │
    ▼
Provider-native prompt cache
    │  cache hit → discounted input tokens
    │
    ▼
LLM

This gives you:

• Exact cache → cheapest and safest
• Semantic cache → catches paraphrases and repeated intents
• Provider prompt cache → reduces the cost of unavoidable LLM calls with large repeated contexts

There is good evidence that provider-native caching can produce substantial savings for agentic workloads: a 2026 evaluation across OpenAI, Anthropic and Google found 45–80% API-cost reductions under its tested caching strategies.

If you're evaluating vendors, I'd shortlist Redis/RedisVL, GPTCache, Portkey, LiteLLM, Helicone, Upstash, and PromptCacheAI, then compare them on cache-hit rate, semantic false-hit rate, latency, TTL/eviction controls, multi-provider support, and—most importantly—whether the cache itself costs enough to erase the token savings.

If you tell me your stack (e.g. OpenAI/Anthropic/Gemini + Python/Node + Redis/AWS/GCP/Azure), I can narrow this to the 3–5 strongest options and compare pricing/architecture.