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NexEco AI

LLM caching that optimizes AI costs

Stop building separate caching logic for every provider. NexEco AI simplifies LLM caching through one integration, so your team can focus on building products.

  • Lower token costs – skip reprocessing repeated context.
  • Faster responses – return cached responses with lower latency.
  • One integration – no per-provider cache logic to maintain.
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NexEco AI Gateway cache routes requests to cached responses or an LLM provider

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What is LLM caching?

LLM caching stores and reuses previously processed prompts or responses. When identical or similar queries share a system prompt or repeated prompt prefixes, the LLM caching system returns a stored response instead of recomputing it – fewer redundant computations, lower token costs, faster responses.

Contexts commonly cached

This is especially useful for AI applications that repeatedly send large blocks of shared context, from API-driven agents to an AI workspace for multiple LLMs, such as:

Agent instructions

System instructions

Tool definitions

Long documents or codebases

Examples and reference material

Conversation history

Benefits of LLM caching

Save engineering time

Save engineering time

NexEco AI, an all-in-one AI platform, centralizes provider-specific caching implementations behind one integration, so your engineers spend less time maintaining it and more time building products.

Reduce AI costs

Reduce AI costs

A higher cache hit rate means less repeated context to reprocess and fewer input tokens to pay for, direct savings on every cached query, alongside the Gateway's Smart LLM routing and cost controls.

Improve response times

Improve response times

On a cache hit, NexEco AI returns the stored response directly, no call to the model, no generation time. This matters most for requests with large repeated context.

Scale AI without rebuilding caching

Scale AI without rebuilding caching

Caching gets harder to manage with every new agent, model, or provider you add. NexEco AI keeps it working through one integration, so it scales with your stack, no rebuild, no lock-in.

How NexEco AI improves LLM caching

NexEco AI works on two levels. Exact repeats are answered instantly by the Gateway's own cache, no provider call needed. Everything else is structured to make the most of the provider's own caching, so nothing gets processed at full price unnecessarily.

No workflow changes

Developers keep using the same models, prompts, coding agents, and applications. Caching improvements happen through the Gateway underneath, so cost savings show up without redesigning how your team works.

Catch missed opportunities

Repeated context still gets processed at full price when caching is missing or misconfigured. NexEco AI helps identify repeated context and improve how effectively provider-side prompt caching is applied.

Handle provider differences

Providers differ in how prompt caching is enabled, structured, and reported. NexEco AI manages those differences through one centralized integration, so you don't build a separate caching system for every provider.

Stay effective as you scale

New agents, changing prompts, and updated workflows can erode cache effectiveness over time. Centralized visibility helps teams monitor cache hit rates and keep caching working as AI usage grows.

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Two ways NexEco AI makes caching work

Caching saves money in two different places: at the Gateway and inside the provider. NexEco AI handles both, so you get the benefit either way. Here's how:

Response caching

The Gateway keeps its own cache of past requests and responses. When an identical request comes in again, NexEco AI returns the stored response directly, with no call to the provider at all. This is the fastest possible outcome: no generation, no wait, no cost.

Prompt caching

Prompt caching lives on the provider's side, reusing repeated prompt prefixes, like a shared system prompt, so requests that begin the same way skip reprocessing. NexEco AI makes sure your requests are structured to take full advantage of it, so you capture these savings without extra work.

The complexity of
LLM caching

Provider caching isn't "set and forget." It happens at the AI provider level, but keeping it effective is an ongoing job. Each provider has its own rules, so teams typically need to:

Configure parameters correctly

Small misconfigurations quietly kill your cache hit rate.

Validate cache hit rates

Confirm caching is actually firing, not just assumed.

Optimize prompts

Structure requests so repeated context is cacheable in the first place.

Maintain it across providers

Then do it all again for the next model.

And it never stops. New agents, new use cases, and evolving workflows mean these configurations need constant review.

How NexEco AI tackles
these challenges

NexEco AI turns caching from a per-provider chore into one integration your team sets up once. Instead of administering caching model by model, the NexEco AI Gateway manages it centrally, giving you visibility into cache effectiveness across every provider from one place.

  • Less implementation work

    No provider-specific caching to build and maintain for every model.

  • Savings you can actually see

    See exactly how many tokens, and how much cost, caching saved you.

  • Spot problems early

    Hit-rate data flags misconfigurations before they cost you savings.

  • No new setup per provider

    Connect a new provider without building separate caching logic for it.

  • Scales without extra setup

    Add new agents, models, or providers without adding new caching work.

Real-world results of LLM caching

Nord Security is cutting costs and improving cache performance for their coding agents running on NexEco AI, without changing how their teams work. And the results speak for themselves:

46%
Reduction in total LLM costs
77%
Cache hit rate, up from just 14%
Zero
Workflow changes required
Read the Nord Security case study

Stop paying twice
for the same context

Turn repeated context into lower AI costs with NexEco AI LLM caching.

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