---
description: AI in insurance needs governed model access. Compare 5 AI gateways for insurers on PII and PHI redaction, NAIC-ready audit trails, budgets, and VPC deployment.
title: "AI in Insurance: 5 Best AI Gateways for Insurers in 2026"
image: https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2026/10/ai-in-insurance-5-best-ai-gateways-for-insurers-in-2026-bifrost-isometric.png
---

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**TL;DR**

- AI in insurance is now an examination topic: 25 states and the District of Columbia had adopted the NAIC AI Model Bulletin as of August 31, 2026.
- An AI gateway gives insurers one enforcement point for PII and PHI redaction, model access, spend limits, and audit evidence across claims and underwriting.
- Bifrost adds 11 microseconds of overhead per request at 5,000 RPS and runs inside the insurer's own VPC, on-prem, or air-gapped.
- Kong AI Gateway, LiteLLM, Azure API Management, and Cloudflare AI Gateway each cover parts of the insurance control set, with different deployment and governance trade-offs.
- Map gateway controls to the NAIC AIS Program, Colorado Regulation 10-1-1, and NYDFS Circular Letter No. 7 before shortlisting a vendor.

As of August 31, 2026, 25 states and the District of Columbia had adopted the NAIC model bulletin on insurers' use of AI, according to the [NAIC adoption map](https://content.naic.org/sites/default/files/legal-adoption-map-ai-model-bulletin.pdf), which turns AI in insurance from an innovation question into an examination question. [Bifrost](https://www.getmaxim.ai/bifrost), the [open-source AI gateway](https://github.com/maximhq/bifrost) built by Maxim AI, is the best choice for insurers running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. Every claims summary and underwriting extraction now needs controls a regulator can inspect. This guide compares five AI gateways for insurance on PII and PHI redaction, audit trails, cost controls per line of business, and VPC deployment.

## What AI in Insurance Requires From an AI Gateway

AI in insurance requires an enforcement layer between every application and every model. An AI gateway is the infrastructure layer that authenticates, routes, inspects, and logs all LLM traffic through one API, so an insurer can redact policyholder data, cap spend by line of business, and produce evidence for examiners from one place instead of from each application.

A typical carrier runs generative AI in insurance claims (first notice of loss triage, adjuster note summaries, medical record review), AI underwriting (submission intake, loss run extraction, ACORD form parsing), and policyholder service agents that call internal tools. Without a gateway, each team holds its own provider keys and decides on its own whether a Social Security number can leave the network. For a background on the category itself, see [how an AI gateway works](https://www.getmaxim.ai/articles/ai-gateway-explained-what-it-is-and-how-it-works/).

![Layered stack with claims, underwriting, and service applications on top, the Bifrost AI gateway enforcing keys, redaction, and logs in the middle, and model providers at the bottom](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-in-insurance-ai-gateways/ai-in-insurance-gateway-stack.png)

*Figure 1: Every claims, underwriting, and service workload crosses one governed layer before any policyholder data reaches a model.*

As Figure 1 shows, the gateway is the only layer every workload shares, which makes it the practical place to enforce four controls regulators and internal audit ask about first:

- **Data protection:** detect and redact PII and PHI in prompts and responses before data reaches a third-party model.
- **Access control:** limit which teams, applications, and agents can call which models and tools.
- **Cost control:** allocate and enforce budgets per line of business, team, and application.
- **Evidence:** keep request logs and administrative audit trails that survive a market conduct examination.

The same pattern appears across regulated sectors; the [AI governance guide for regulated sectors including insurance](https://www.getmaxim.ai/articles/ai-governance-in-regulated-sectors-hr-healthcare-finance-and-insurance/) covers it from the policy side.

## NAIC AI Model Bulletin and State Rules That Shape Gateway Requirements

The NAIC AI Model Bulletin, adopted on December 4, 2023, expects every insurer to maintain a written AI Systems (AIS) Program covering governance, risk management controls, internal audit, and third-party AI oversight. Colorado and New York add their own requirements on top. An AI gateway does not satisfy these rules alone, but it produces much of the evidence they ask for.

The [NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers](https://content.naic.org/sites/default/files/cmte-h-big-data-artificial-intelligence-wg-ai-model-bulletin.pdf.pdf) applies across the insurance life cycle, explicitly naming underwriting, rating and pricing, claim administration and payment, and fraud detection. It asks the AIS Program to address the protection of non-public consumer information, data and record retention, and due diligence on AI systems developed by third parties, including audit rights in vendor contracts. In a market conduct action, insurers can expect to be asked how AI systems are developed, deployed, and used.

Colorado goes further. SB21-169 (2021) prohibits unfair discrimination resulting from external consumer data and information sources (ECDIS), algorithms, and predictive models. The Colorado Division of Insurance's [amended Regulation 10-1-1](https://doi.colorado.gov/announcements/notice-of-adoption-amended-regulation-10-1-1-governance-and-risk-management-framework), effective October 15, 2025, extends its governance and risk management framework requirements from life insurers to private passenger auto and health benefit plan insurers.

In New York, [DFS Insurance Circular Letter No. 7](https://www.dfs.ny.gov/industry-guidance/circular-letters/cl2024-07) (July 11, 2024) sets expectations for AI systems and ECDIS in underwriting and pricing, and makes clear that insurers remain responsible for vendor-supplied tools.

| Regulatory expectation | Source | Gateway control that produces evidence |
| --- | --- | --- |
| Protect non-public consumer information | NAIC Bulletin, Sec. 3.5 | Input and output guardrails that redact PII and PHI |
| Data and record retention | NAIC Bulletin, Sec. 3.6 | Request logs and audit logs archived to object storage |
| Third-party AI system oversight | NAIC Bulletin, Sec. 4; NYDFS CL 7 | Per-provider routing, allow-listed models, per-vendor usage logs |
| Documented governance, roles, and accountability | NAIC Bulletin, Sec. 2; Colorado Reg. 10-1-1 | RBAC, SSO-backed identities, signed administrative audit logs |
| Monitoring, auditing, escalation | NAIC Bulletin, Sec. 2.3(d) | Real-time request monitoring, guardrail block events, budget alerts |

![Model requests feed request logs and configuration changes feed signed audit logs, both archived to S3 or GCS and assembled into documentation for a NAIC Model Bulletin examination](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-in-insurance-ai-gateways/ai-in-insurance-exam-evidence.png)

*Figure 2: Request logs answer what the AI system did; signed audit logs answer who changed the controls and when.*

Figure 2 separates the two record types examiners care about. Bifrost keeps them distinct: [request logs](https://docs.getbifrost.ai/features/observability/default) capture model calls, while [audit logs](https://docs.getbifrost.ai/enterprise/audit-logs) record administrative activity. For a deeper treatment of trail design, see [AI audit trail controls for LLM traffic](https://www.getmaxim.ai/articles/ai-audit-trail-controls-and-audit-logs-for-llm-traffic/).

## Key Criteria for Choosing an AI Gateway for Insurance

The right AI gateway for an insurer is the one that keeps policyholder data inside controlled infrastructure, redacts what must leave, ties every request to an accountable identity, and enforces spend per line of business.

| Criterion | Why it matters for insurers | What to verify |
| --- | --- | --- |
| Deployment model | NPI and claims files often cannot leave the carrier's network | In-VPC, on-prem, and air-gapped options |
| PII and PHI redaction | Medical records, SSNs, and policy numbers appear in claims prompts | Input and output inspection, redact vs block actions, log redaction |
| Audit trail | NAIC exams and internal audit need durable, attributable records | Signed admin events, retention, export to object storage |
| Cost controls | Claims and underwriting budgets belong to different P&Ls | Hierarchical budgets and rate limits per team and application |
| Access control | Agents and teams should reach only approved models and tools | SSO, RBAC, row-level data scoping, per-key model allow-lists |
| Reliability | FNOL intake and policyholder chat cannot stop during a provider outage | Retries, fallbacks across providers, clustering |
| Third-party model breadth | Vendor diversification is part of third-party risk management | Number of supported providers and self-hosted model support |

A structured checklist for these questions is in the [LLM gateway buyer's guide](https://www.getmaxim.ai/bifrost/resources/buyers-guide).

## Best AI Gateways for Insurance Compared at a Glance

Of the five gateways reviewed here, Bifrost covers the broadest share of the insurance control set in a single self-hosted gateway, across 25+ [supported providers](https://docs.getbifrost.ai/providers/supported-providers/overview). Kong AI Gateway and Azure API Management extend existing API management platforms, LiteLLM offers an open-source proxy with budgets and guardrails, and Cloudflare AI Gateway focuses on proxy-level caching, logging, and DLP scanning.

| Capability | Bifrost | Kong AI Gateway | LiteLLM | Azure API Management | Cloudflare AI Gateway |
| --- | --- | --- | --- | --- | --- |
| Self-hosted / in-VPC | Yes: in-VPC, on-prem, air-gapped | Self-hosted data planes or Konnect | Self-deployed proxy | Azure service | Not published |
| PII / PHI redaction | Guardrails with redact, block, or detect-only; log redaction | AI Sanitizer plugin | Guardrails | Azure AI Content Safety policy | DLP flag or block |
| Hierarchical budgets | Customer, team, and virtual key | Cost-based rate limiting | Per key and per user | Token limit policy per consumer | Rate limiting |
| Signed admin audit logs | Yes, HMAC-signed | Not published | Not published | Not published | Not published |
| Provider failover | Retries plus fallback chains | Not published | Load balancing and fallbacks | Backend pools with circuit breaker | Request retry and fallback |
| Open source | Yes | Not published | Yes | Managed Azure service | Not published |

## 1. Bifrost

The [Bifrost AI gateway](https://www.getmaxim.ai/bifrost) is open source, written in Go, and unifies access to 25+ providers and 10,000+ models through one OpenAI-compatible API. For insurers, it combines PII and PHI redaction, hierarchical budgets, signed audit logs, and in-VPC or air-gapped deployment in one gateway, adding 11 microseconds of overhead per request at 5,000 RPS.

**Best for:** Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, [MCP gateway](https://www.getmaxim.ai/mcp-gateway), and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

**PII and PHI redaction for claims and underwriting.** Bifrost [guardrails](https://docs.getbifrost.ai/enterprise/guardrails) run CEL-based rules on the input phase (before a request reaches the provider) and the output phase (after the provider responds), for LLM calls and MCP tool executions. Bifrost-managed profiles include Custom Regex, Secrets Detection, and Prompt Guardrails, and external profiles include Microsoft Presidio, Azure AI Language PII, AWS Bedrock Guardrails, Google Model Armor, and Singulr AI, which enforces PII and PHI policies. Supported profiles can detect only, block, or [redact with replace, mask, or hash strategies](https://docs.getbifrost.ai/enterprise/guardrails/redaction), and a logs-only mode keeps runtime content intact while redacting stored logs.

![A claims summarization request passes through virtual key checks and an input guardrail that redacts PII and PHI, is routed to a provider, then output guardrails run before redacted logging](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-in-insurance-ai-gateways/ai-in-insurance-redaction-path.png)

*Figure 3: Redaction runs on the input phase, so the provider only ever receives placeholders for detected policyholder identifiers.*

One detail matters for claims files. The built-in [PII Detection regex template](https://docs.getbifrost.ai/enterprise/guardrails/custom-regex) covers email addresses, US phone numbers, US Social Security numbers, and credit-card-like numbers; it is pattern-based rather than semantic. Claimant names and medical terms need an entity recognizer such as [Presidio](https://docs.getbifrost.ai/integrations/guardrails/presidio) (which detects entity types such as `PERSON`) or a PHI-aware external provider. The [PII redaction at the gateway layer guide](https://www.getmaxim.ai/articles/pii-redaction-at-the-gateway-layer-for-regulated-industries/) walks through layering these.

**Audit trails for examinations.** Bifrost [signed audit logs](https://docs.getbifrost.ai/enterprise/audit-logs) record administrative activity: who changed a guardrail, a budget, or a key, and when. Entries can be HMAC-signed for verification, retained for a configurable number of days, exported as JSON, JSON Lines, or Syslog, and archived to S3 or GCS. Separately, [log exports](https://docs.getbifrost.ai/enterprise/log-exports) offload request and response payloads to S3 or GCS while keeping searchable metadata in the logs database, which supports NAIC record retention expectations.

**Cost controls per line of business.** [Virtual keys](https://docs.getbifrost.ai/features/governance/virtual-keys) carry model and provider allow-lists, budgets, and token and request rate limits. [Budgets](https://docs.getbifrost.ai/features/governance/budget-and-limits) stack across customer, team, and virtual key levels, with every applicable budget checked on every request, and calendar-aligned resets for monthly or quarterly cycles.

**Identity and access.** Bifrost Enterprise supports [OIDC and SCIM 2.0 provisioning](https://docs.getbifrost.ai/enterprise/user-provisioning), [role-based access control](https://docs.getbifrost.ai/enterprise/rbac) with system and custom roles, and [data access control](https://docs.getbifrost.ai/enterprise/data-access-control) that scopes which rows a user can see to their own data, their team's data, or all data.

**Deployment and reliability.** [In-VPC deployments](https://docs.getbifrost.ai/enterprise/invpc-deployments) run Bifrost inside the insurer's AWS, GCP, or Azure environment with all data processing in the carrier's network, and on-prem and air-gapped installs are supported. [Automatic fallbacks](https://docs.getbifrost.ai/features/fallbacks) retry transient errors and move to the next provider in a chain, and clustering removes the single point of failure.

Published [benchmarks](https://www.getmaxim.ai/bifrost/resources/benchmarks) show 11 microseconds of overhead at 5,000 RPS with a 100% success rate. The [insurance industry page](https://www.getmaxim.ai/bifrost/industry-pages/insurance) shows how carriers apply these controls to claims, underwriting, and SIU workflows.

## 2. Kong AI Gateway

Kong AI Gateway extends Kong's API gateway with AI-specific policies for routing, data sanitization, prompt filtering, caching, and cost-based rate limiting. It suits insurers that already run Kong and want AI traffic on the same control plane.

Kong documents an AI Sanitizer policy that removes personal data from prompts before they reach the provider, an AI Prompt Guard policy, AI Semantic Cache, and AI Rate Limiting Advanced, which enforces spend limits based on each request's calculated cost. Deployment can use Konnect as a managed control plane or self-hosted Kong Gateway, with data plane nodes running self-hosted, in the cloud, or on Kubernetes.

**Best for:** Insurers standardized on Kong for API management that want AI policies added to an existing gateway footprint.

For insurance buyers, the key questions are how audit evidence for AI-specific configuration changes is captured and how budgets map to business units. Teams weighing a dedicated AI gateway can compare options in this review of [Kong AI Gateway alternatives](https://www.getmaxim.ai/articles/best-kong-ai-gateway-alternatives-in-2026/).

## 3. LiteLLM

LiteLLM is an open-source proxy server that exposes 100+ LLMs through an OpenAI-compatible interface, with virtual keys, spend tracking, budgets, and rate limits. It is common in engineering-led teams that want a self-deployed proxy with broad model coverage.

LiteLLM documents budgets and spend tracking per virtual key or user, guardrails, load balancing with fallbacks, secret manager integrations, logging and alerting, an MCP gateway, and an admin UI. The operator deploys it, so traffic stays inside infrastructure the insurer controls.

**Best for:** Engineering teams that want an open-source, self-deployed proxy with per-key budgets and broad provider support.

Insurers evaluating LiteLLM for regulated claims or underwriting workloads should confirm how audit records are signed and retained and how budgets roll up across lines of business. The [LiteLLM alternative comparison](https://www.getmaxim.ai/bifrost/resources/litellm-alternative) covers how Bifrost differs on performance and governance.

## 4. Azure API Management

Azure API Management offers AI gateway capabilities as policies on top of Azure's API management service, including token limits per consumer, token metrics, semantic caching, backend load balancing, and content safety checks. It fits carriers whose AI workloads already run on Azure OpenAI and Azure infrastructure.

Microsoft documents an `llm-token-limit` policy for tokens-per-minute limits or token quotas per consumer, an `llm-emit-token-metric` policy for consumption metrics in Azure Monitor, semantic caching backed by Azure Managed Redis or another RediSearch-compatible cache, and backend pools with round-robin, weighted, priority-based, and session-aware load balancing plus a circuit breaker. Prompts can be moderated with Azure AI Content Safety, and token usage, prompts, and completions can be logged to Application Insights and Azure Monitor.

**Best for:** Insurers committed to Azure that want AI traffic governed with the same policies and monitoring as their other Azure APIs.

The trade-off is scope: the controls are tied to one cloud's service. Multi-cloud carriers can review [Azure AI gateway alternatives for multi-cloud LLM traffic](https://www.getmaxim.ai/articles/top-5-azure-ai-gateway-alternatives-for-multi-cloud-llm-traffic-in-2026/).

## 5. Cloudflare AI Gateway

Cloudflare [AI Gateway](https://www.getmaxim.ai/articles/top-5-llm-gateways-in-2026-a-production-ready-comparison/) is a proxy for AI traffic that provides caching, rate limiting, logging, request retry and fallback, and analytics on requests, tokens, and cost. Its Data Loss Prevention feature scans user prompts and AI responses for sensitive data.

Cloudflare documents three DLP outcomes for responses: pass, flag (findings logged and attached to a `cf-aig-dlp` response header), and block (the provider response is discarded and replaced with an error).

**Best for:** Teams already using Cloudflare that want caching, analytics, and DLP scanning on AI traffic with minimal setup.

Self-hosted deployment is not published in the pages reviewed, which matters for carriers whose policies keep claims data inside their own network. Teams with that requirement can compare [Cloudflare AI Gateway alternatives](https://www.getmaxim.ai/articles/best-cloudflare-ai-gateway-alternative-in-2026/) built for full traffic control.

## How to Roll Out AI Gateway Controls for Claims and Underwriting

Roll out an AI gateway for insurance in three steps: put every claims and underwriting workload behind the gateway, map budgets and access to lines of business, then turn on redaction and audit retention before expanding use cases. This order produces examination evidence from day one.

**Step 1: Route all traffic through one endpoint.** Bifrost works as a [drop-in replacement](https://docs.getbifrost.ai/features/drop-in-replacement) for existing OpenAI, Anthropic, and other SDKs by changing the base URL. Use [routing rules](https://docs.getbifrost.ai/providers/routing-rules) to send each workload to approved models, for example keeping medical record summarization on a model hosted in your own cloud tenancy.

**Step 2: Map budgets to the organization chart.** Model each line of business as a customer, each claims or underwriting team as a team, and each application as a virtual key. A request must clear every budget above it.

![Two business units, personal lines and commercial lines, each hold teams for claims and underwriting, and each team issues virtual keys with their own budgets and rate limits](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/ai-in-insurance-ai-gateways/ai-in-insurance-budget-hierarchy.png)

*Figure 4: A request must clear every budget above it, so one runaway claims workload cannot spend another line of business's allocation.*

With the hierarchy in Figure 4, finance sees AI spend by line of business, and a misconfigured batch job in commercial claims stops at its team budget. [Access profiles](https://docs.getbifrost.ai/enterprise/access-profiles) apply the same policy template to every user in a role, which keeps AI underwriting teams on approved models as headcount changes. The [guide to hierarchical LLM budget management](https://www.getmaxim.ai/articles/llm-budget-management-virtual-keys-and-hierarchical-spend-controls/) covers sizing.

**Step 3: Turn on redaction and retention.** Start guardrails in detect-only mode on a sample of traffic to measure false positives on real claims notes, then switch to redact. Configure audit log signing and S3 or GCS archival so the AIS Program has durable records. Carriers with stricter isolation needs can review [air-gapped and on-prem AI gateway options](https://www.getmaxim.ai/articles/best-air-gapped-and-on-prem-ai-gateways-for-regulated-industries/), and the [governance overview](https://www.getmaxim.ai/bifrost/resources/governance) explains how these controls fit together.

## Frequently Asked Questions

### What are AI gateways?

AI gateways are infrastructure layers that sit between applications and LLM providers, exposing one API for all models. They authenticate callers, route requests, enforce budgets and rate limits, apply guardrails such as PII redaction, and log every request. For insurers, the gateway is where data protection and audit evidence are enforced consistently. The [AI gateway hub article](https://www.getmaxim.ai/articles/ai-gateway-explained-what-it-is-and-how-it-works/) covers the architecture in depth.

### Do I need an AI gateway?

An insurer needs an AI gateway once more than one team or application calls LLMs. Without one, provider keys, logging, redaction, and spend limits are implemented separately in each application, which makes NAIC AIS Program documentation and third-party oversight hard to prove. A gateway centralizes those controls and gives examiners one place to review how AI systems are used.

### What is the NAIC AI Model Bulletin?

The NAIC AI Model Bulletin is a model regulatory bulletin adopted by the National Association of Insurance Commissioners on December 4, 2023. It expects insurers to maintain a written AI Systems Program covering governance, risk management, internal audit, data protection, record retention, and third-party AI oversight. As of August 31, 2026, 25 states and the District of Columbia had adopted it.

### How does an AI gateway redact PII and PHI from claims data?

An AI gateway redacts PII and PHI by running guardrails on each request before it reaches the model provider and on each response before it returns. Detectors such as regex patterns or entity recognizers find values like SSNs, phone numbers, and names, and the gateway replaces, masks, or hashes them. Bifrost can also redact stored logs while leaving runtime content unchanged.

### Can an AI gateway run inside an insurer's VPC?

Yes. Self-hosted AI gateways can run entirely inside an insurer's virtual private cloud so prompts, responses, and logs never transit third-party infrastructure other than the chosen model provider. [Bifrost](https://www.getmaxim.ai/bifrost/enterprise) supports in-VPC deployment on AWS, GCP, and Azure, plus on-prem and air-gapped installations for carriers with stricter isolation requirements.

### Will insurance be replaced by AI?

No. AI in insurance supports underwriters, adjusters, and service staff, but insurers remain accountable for the decisions. The NAIC bulletin states that decisions made or supported by AI systems must comply with all applicable insurance laws, including unfair trade practice and unfair discrimination rules. That accountability is why governance infrastructure such as AI gateways is growing alongside adoption.

## Govern AI in Insurance With Bifrost

AI in insurance is moving from pilots into claims, underwriting, and policyholder service, and regulators now expect documented controls over every one of those AI systems. Bifrost gives insurers one open-source AI gateway for PII and PHI redaction, budgets per line of business, signed audit trails, and in-VPC or air-gapped deployment. To see how Bifrost fits your AIS Program, [book a demo with the Bifrost team](https://getmaxim.ai/bifrost/book-a-demo).

## Read next

[![Top 5 AI Gateways for Controlling Shadow AI in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/top-5-ai-gateways-for-controlling-shadow-ai-bifrost-isometric.png) Shadow AI is the use of AI tools, models, and MCP servers that security teams have not approved and cannot see. This guide ranks five AI gateways for controlling it, including Bifrost with Bifrost Edge, Kong AI Gateway, Cloudflare AI Gateway, and Gravitee.](https://www.getmaxim.ai/articles/top-5-ai-gateways-for-controlling-shadow-ai/)

[![Top 5 AI Gateways for SSO and RBAC in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/top-5-ai-gateways-for-sso-and-rbac-in-2026-bifrost-isometric.png) AI gateways with SSO and RBAC let enterprises tie every model request and every configuration change to a corporate identity. This guide compares Bifrost, Kong AI Gateway, Azure API Management, Gravitee, and Cloudflare AI Gateway on identity, roles, provisioning, and audit.](https://www.getmaxim.ai/articles/top-5-ai-gateways-for-sso-and-rbac-in-2026/)

[![Semantic Caching: The Top 5 AI Gateways in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/semantic-caching-the-top-5-ai-gateways-in-2026-bifrost-isometric.png) Semantic caching serves a stored LLM response when a new prompt means the same thing as an earlier one. This guide compares Bifrost, Kong AI Gateway, Azure API Management, and Cloudflare AI Gateway on match modes, vector stores, thresholds, TTLs, and cache scoping.](https://www.getmaxim.ai/articles/semantic-caching-the-top-5-ai-gateways-in-2026/)

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