Introduction (100–200 words)
LLM gateways and model routing platforms sit between your applications and one or more language model providers. In plain English: they give you a single, controllable “front door” for AI calls—so you can switch models, enforce policies, observe usage, and optimize cost/latency without rewriting every app.
This category matters even more in 2026+ because teams rarely rely on a single model. They mix frontier LLMs, smaller fast models, local/open-source models, and specialized reasoning or multimodal models—often across multiple vendors and regions. Gateways and routers help keep that complexity manageable and auditable.
Common use cases include:
- Fallback routing when a provider is down or throttled
- Cost-aware routing (cheap model for simple tasks; premium model for complex tasks)
- Centralized security controls (keys, rate limits, redaction, policy)
- Observability + chargeback by team/app/customer
- Progressive migration from one provider/model to another
What buyers should evaluate:
- Multi-provider support and API compatibility
- Routing logic (rules, experiments, eval-driven routing, fallback)
- Governance (keys, budgets, quotas, approvals)
- Observability (logs, traces, prompt/version tracking, cost analytics)
- Security controls (RBAC, audit logs, encryption, data handling)
- Deployment options (cloud, self-hosted, hybrid, data residency)
- Reliability features (retries, circuit breakers, caching)
- Latency overhead and throughput
- Integrations (SDKs, OpenTelemetry, SIEM, data warehouse)
- Pricing model and unit economics at scale
Best for: developer teams shipping AI features in production, IT/platform engineering teams standardizing AI access, and SaaS companies needing tenant-level metering, governance, and reliability across multiple models/providers.
Not ideal for: hobby projects or single-model prototypes where direct provider SDK calls are simpler; also teams that only need prompt experimentation (a full gateway may be heavier than necessary).
Key Trends in LLM Gateways & Model Routing Platforms for 2026 and Beyond
- Eval-driven routing: automatic model selection based on offline/online evaluation results, task difficulty scoring, and quality thresholds.
- Policy-as-code for AI: centrally managed rules for data handling, PII redaction, allowed models, and prompt/response constraints—often enforced at the gateway layer.
- Multi-modal normalization: unified handling for text, vision, audio, tool calls, and structured outputs across providers with different schemas.
- Latency engineering becomes a feature: streaming optimizations, response caching, speculative decoding support (where applicable), and region-aware routing.
- Cost governance at org scale: budgets, quotas, per-tenant caps, and automated downgrades to cheaper models when spend spikes.
- Security expectations rising: stronger RBAC, audit trails, encryption defaults, secrets isolation, and enterprise SSO—plus deeper vendor risk reviews.
- Hybrid and edge deployments: demand for “near data” inference routing (including private networks) and edge-based control planes for latency and residency.
- Standardized telemetry: OpenTelemetry-style traces and consistent token/cost metrics across providers to support SRE workflows.
- Provider volatility planning: gateways used to absorb breaking API changes, deprecations, and model churn without app rewrites.
- Agentic workflows increase gateway responsibility: tool-use policies, allowlists for external actions, and rate limiting for multi-step agent loops.
How We Selected These Tools (Methodology)
- Prioritized tools with clear positioning as an LLM gateway, proxy, and/or model router (not just a prompt playground).
- Considered market mindshare among developers and platform teams (community usage, common mentions in engineering stacks).
- Evaluated feature completeness: routing, fallback, auth/key management, observability, and governance.
- Looked for reliability signals: production patterns like retries, rate limiting, caching, and safe rollout mechanisms.
- Assessed security posture signals: RBAC, audit logs, SSO support, and deployment flexibility (cloud vs self-hosted).
- Checked integration breadth: SDK compatibility, OpenAI-style APIs, OpenTelemetry, and compatibility with common agent frameworks.
- Included a balanced mix: developer-first SaaS, enterprise gateways, open-source options, and hyperscaler platforms.
- Weighted inclusion toward tools that remain relevant in 2026+ (multi-model, multi-modal, and governance-forward roadmaps).
Top 10 LLM Gateways & Model Routing Platforms Tools
#1 — LiteLLM
Short description (2–3 lines): LiteLLM is a developer-focused LLM proxy that helps teams standardize API calls across many model providers. It’s commonly used for routing, fallbacks, spend tracking, and OpenAI-compatible API unification.
Key Features
- OpenAI-compatible API proxy for many providers
- Model routing and fallback patterns (rules-based)
- Centralized key management and usage tracking (capabilities vary by setup)
- Request/response logging options and metadata tagging
- Rate limiting and retry patterns (implementation-dependent)
- Works well in containerized environments for platform teams
- Extensible configuration for provider normalization
Pros
- Strong fit for teams that want control + portability across providers
- Popular for self-hosted deployments and internal platforms
- Helps reduce provider lock-in by normalizing APIs
Cons
- Requires engineering ownership (config, scaling, operations)
- Some enterprise governance needs may require additional tooling
- UI/analytics depth depends on your deployment choices
Platforms / Deployment
- Cloud / Self-hosted / Hybrid
Security & Compliance
- RBAC/audit logs/SSO: Varies / Not publicly stated (depends on deployment and edition)
- Compliance certifications: Not publicly stated
Integrations & Ecosystem
LiteLLM is often integrated as an internal gateway behind your apps or agent services, with compatibility patterns that map well to OpenAI-style SDKs and tooling.
- OpenAI-compatible client integrations
- Multi-provider backends (varies by configuration)
- Container/Kubernetes deployments
- Observability integrations (varies / user-implemented)
- Works alongside agent frameworks (integration approach varies)
Support & Community
Strong developer mindshare and active community usage; support tiers vary by offering/edition. Documentation quality is generally considered practical, but operational success depends on internal platform maturity.
#2 — OpenRouter
Short description (2–3 lines): OpenRouter is a model routing platform that provides a unified API for accessing multiple models. It’s often used by developers who want fast multi-model experimentation and simplified billing across providers.
Key Features
- Unified API for multiple model providers
- Model selection and routing across a catalog
- Centralized usage tracking and cost visibility (platform-dependent)
- Quick switching between models without code rewrites
- Useful for benchmarking and comparative testing workflows
- Developer-friendly onboarding for multi-model access
- Supports rapid iteration for prompts and model choices
Pros
- Very fast path to multi-model access for small teams
- Reduces friction when evaluating multiple providers
- Helpful for prototyping routing behavior before building your own gateway
Cons
- Less control than self-hosted gateways for strict governance needs
- Enterprise compliance and residency requirements may not fit all orgs
- Deep customization of routing/policy may be limited vs DIY stacks
Platforms / Deployment
- Web
- Cloud
Security & Compliance
- SSO/SAML, RBAC, audit logs: Not publicly stated
- Compliance certifications: Not publicly stated
Integrations & Ecosystem
Typically used via API from apps, scripts, and LLM tooling that supports an OpenAI-like interface.
- API-based integration for apps and services
- Works with many OpenAI-compatible SDK patterns
- Commonly paired with prompt tooling and eval harnesses
- Developer workflow integrations (varies)
- Web console usage for exploration (where available)
Support & Community
Community visibility is strong in developer circles; formal enterprise support offerings are not publicly stated.
#3 — Cloudflare AI Gateway
Short description (2–3 lines): Cloudflare AI Gateway is designed to sit in front of LLM providers to improve observability, caching, and control. It’s a fit for teams already using Cloudflare for edge, security, and traffic management.
Key Features
- Gateway/proxy layer for LLM traffic
- Observability for requests, latency, and usage (feature set varies)
- Caching options to reduce repeated calls (when applicable)
- Rate limiting and traffic control patterns
- Central management for API usage across apps
- Edge-adjacent deployment benefits for latency-sensitive workloads
- Works well as part of broader Cloudflare traffic/security stack
Pros
- Strong fit if you already run traffic through Cloudflare
- Helps reduce operational overhead for monitoring and control
- Can improve performance characteristics for some patterns (e.g., caching)
Cons
- Best value often depends on broader Cloudflare adoption
- Some advanced routing logic may require additional components
- Compliance specifics for AI Gateway features: Not publicly stated
Platforms / Deployment
- Web
- Cloud
Security & Compliance
- Encryption in transit: Expected (platform-based), details vary
- RBAC/audit logs/SSO: Varies / Not publicly stated by feature
- SOC 2 / ISO 27001 / HIPAA: Not publicly stated for the AI Gateway feature specifically
Integrations & Ecosystem
Often used alongside existing Cloudflare services and standard HTTP-based app architectures.
- Works with multiple LLM providers (varies)
- API-based integration for web and backend services
- Can pair with edge functions/workers (where applicable)
- Observability exports (varies)
- Fits into broader Cloudflare security controls (varies)
Support & Community
Support experience typically aligns with Cloudflare plan level; community resources are broad, but AI Gateway-specific depth varies.
#4 — Portkey
Short description (2–3 lines): Portkey is an LLM gateway platform focused on routing, observability, and governance. It’s commonly positioned for teams that want a managed control plane without building a full internal platform.
Key Features
- Multi-provider LLM gateway with unified API patterns
- Routing rules (fallbacks, conditional routing; capabilities vary by plan)
- Request logging and analytics for cost and performance
- Key management and access controls (feature depth varies)
- Prompt and request metadata management for debugging
- Rate limiting and guardrail-style controls (varies)
- Useful for staging-to-production rollout patterns
Pros
- Faster time-to-value than rolling your own gateway
- Good balance of routing + observability in one product
- Helpful for teams operating multiple apps/tenants
Cons
- Deep customization may be constrained vs self-hosted tooling
- Total cost depends on traffic volume and plan structure (Varies)
- Compliance attestations: Not publicly stated
Platforms / Deployment
- Web
- Cloud
Security & Compliance
- RBAC/audit logs/SSO: Not publicly stated
- Compliance certifications: Not publicly stated
Integrations & Ecosystem
Typically integrates via SDK/API with common backend stacks and LLM frameworks.
- API and SDK integrations (language support varies)
- Multi-provider connectivity (varies)
- Works with agent frameworks via OpenAI-like patterns
- Observability and logging exports (varies)
- Webhooks/automation hooks (varies)
Support & Community
Documentation is oriented toward developers; support tiers and SLAs are not publicly stated.
#5 — Helicone
Short description (2–3 lines): Helicone is best known for LLM observability, and it can also act as a proxy layer in front of model providers. It’s used by teams that want visibility into prompts, latency, costs, and failures with minimal code changes.
Key Features
- Proxy-based logging for LLM calls (provider-dependent)
- Request/response tracing and debugging workflows
- Cost and usage analytics (based on tracked traffic)
- Tagging/metadata for per-feature or per-customer views
- Experiments/A-B style analysis support (feature availability varies)
- Alerting/monitoring patterns (varies)
- Supports production troubleshooting and regression detection
Pros
- Strong fit for observability-first teams
- Useful when multiple services call LLMs and you need centralized logs
- Helps shorten incident resolution time for LLM-related failures
Cons
- Not a full enterprise gateway by default (policy/routing depth varies)
- Self-hosting and advanced governance may require extra work
- Compliance certifications: Not publicly stated
Platforms / Deployment
- Web
- Cloud / Self-hosted (availability varies by offering)
Security & Compliance
- RBAC/audit logs/SSO: Not publicly stated
- Data handling controls: Varies / Not publicly stated
- Compliance certifications: Not publicly stated
Integrations & Ecosystem
Helicone typically integrates at the HTTP/proxy layer with LLM SDKs and backend services.
- Proxy integration with common LLM providers (varies)
- Works with OpenAI-style SDK patterns (implementation-dependent)
- Export/analysis workflows (varies)
- Common backend frameworks (language-agnostic via HTTP)
- Works alongside evaluation pipelines (varies)
Support & Community
Community usage is visible among developers; formal support tiers vary / not publicly stated.
#6 — Kong AI Gateway
Short description (2–3 lines): Kong AI Gateway extends API gateway patterns to LLM traffic. It’s a fit for organizations already standardizing on Kong for API management, security, and traffic control, and now want AI-specific policies.
Key Features
- API gateway controls tailored for LLM endpoints
- Authentication, rate limiting, and quota enforcement patterns
- Policy plugins and extensibility (plugin availability varies)
- Centralized routing and traffic management
- Governance alignment with broader API lifecycle tooling
- Observability integration patterns typical of API gateways
- Supports enterprise patterns (tenancy, environments) depending on edition
Pros
- Strong choice if Kong is already your API gateway standard
- Mature operational model for SRE/Platform teams
- Good for consistent policy enforcement across APIs (AI and non-AI)
Cons
- AI-specific features may require configuration and plugins
- Can be heavyweight for small teams compared to SaaS gateways
- Licensing and enterprise features: Varies / not publicly stated
Platforms / Deployment
- Linux
- Cloud / Self-hosted / Hybrid
Security & Compliance
- RBAC, audit logs, SSO/SAML: Varies by edition / Not publicly stated here
- Compliance certifications: Not publicly stated
Integrations & Ecosystem
Kong’s ecosystem is typically strongest in gateway plugins and enterprise API management integrations.
- Plugin ecosystem (auth, rate limiting, logging)
- Works with common IdPs (varies by edition)
- Observability tools integration (varies)
- Service mesh / microservices environments (varies)
- LLM provider integration via upstream routing (implementation-dependent)
Support & Community
Strong enterprise presence; support tiers vary by edition. Community resources exist, but AI Gateway specifics depend on product maturity and release cadence.
#7 — Tyk AI Gateway
Short description (2–3 lines): Tyk AI Gateway builds on Tyk’s API management foundation to support AI traffic governance. It’s aimed at teams that want API-gateway-grade controls (auth, quotas, policy) applied to LLM usage.
Key Features
- Gateway approach for controlling LLM API consumption
- Policies for authentication, rate limiting, and quotas
- Traffic routing patterns consistent with API management
- Extensibility for custom logic (varies)
- Analytics/monitoring integration patterns (varies by setup)
- Multi-environment promotion (dev/stage/prod) patterns
- Aligns AI usage with existing API governance processes
Pros
- Good fit for organizations already invested in Tyk
- Strong governance posture for platform/IT teams
- Works well for standardizing access across multiple internal apps
Cons
- AI routing sophistication may be less “out of the box” than AI-native routers
- Requires operations and gateway expertise
- Compliance details: Not publicly stated
Platforms / Deployment
- Linux
- Cloud / Self-hosted / Hybrid
Security & Compliance
- RBAC/audit logs/SSO: Varies by edition / Not publicly stated here
- Compliance certifications: Not publicly stated
Integrations & Ecosystem
Tyk’s integration story is typically strongest around API management workflows and extensibility.
- Identity provider integrations (varies)
- Logging/monitoring exports (varies)
- CI/CD workflows for policy deployment (varies)
- Microservices and Kubernetes environments
- Upstream LLM providers via routing configuration
Support & Community
Enterprise support tiers vary; community presence is established for Tyk generally, with AI Gateway specifics depending on adoption.
#8 — Envoy AI Gateway
Short description (2–3 lines): Envoy AI Gateway is an emerging approach built around Envoy-based traffic management for AI workloads. It’s best suited for platform teams that want fine-grained, self-managed control and already run Envoy in their infrastructure.
Key Features
- Proxy/gateway architecture aligned with Envoy ecosystems
- Policy enforcement and routing patterns (feature maturity varies)
- Fit for Kubernetes-native and service-mesh-adjacent environments
- Extensibility for custom filters and transformations
- Potential for standardized telemetry and tracing patterns
- Designed for high-performance proxy use cases
- Enables centralized control without embedding logic in apps
Pros
- Strong match for teams with existing Envoy expertise
- High control over performance and network behavior
- Good foundation for standardized observability practices
Cons
- Maturity and “batteries included” experience may vary
- Requires significant platform engineering investment
- Enterprise compliance packaging: Not publicly stated
Platforms / Deployment
- Linux
- Self-hosted / Hybrid
Security & Compliance
- RBAC/audit logs/SSO: Varies / Not publicly stated
- Compliance certifications: Not publicly stated
Integrations & Ecosystem
Integrates best where Envoy is already part of the stack (Kubernetes, service mesh, standardized ingress/egress).
- Kubernetes and container environments
- Service mesh ecosystems (varies)
- Observability stacks (implementation-dependent)
- Works with LLM providers via upstream configuration
- Custom filters for transformation/policy (varies)
Support & Community
Community strength depends on current adoption and release maturity; support is typically community-driven unless packaged by a vendor.
#9 — Amazon Bedrock
Short description (2–3 lines): Amazon Bedrock is a managed platform for accessing multiple foundation models through AWS. While it’s broader than a pure gateway, it functions as a central access layer for model selection, governance, and integration inside AWS environments.
Key Features
- Access to multiple models through a unified AWS service interface
- AWS-native identity and access controls (IAM-based patterns)
- Integration with AWS networking for private connectivity patterns (varies)
- Managed scaling characteristics (service-dependent)
- Governance patterns aligned with AWS accounts and org structures
- Tooling around safety/guardrails (availability varies by region/service)
- Fits regulated environments that standardize on AWS primitives
Pros
- Strong choice for AWS-first organizations
- Simplifies multi-model access inside a single cloud ecosystem
- Leverages mature AWS operational tooling (logging, monitoring, IAM)
Cons
- Primarily optimized for AWS environments (portability trade-off)
- Not a drop-in “universal gateway” for all non-AWS providers
- Specific compliance attestations for Bedrock: Not publicly stated in this article (varies by AWS service/region)
Platforms / Deployment
- Web
- Cloud
Security & Compliance
- Access control: AWS IAM patterns (fine-grained controls vary by integration)
- Encryption/audit logs: Varies by AWS configuration and services used
- SOC 2 / ISO 27001 / HIPAA: Not publicly stated for Bedrock specifically here (AWS compliance varies by service/region)
Integrations & Ecosystem
Bedrock integrates deeply with AWS-native services and typical enterprise cloud architectures.
- AWS IAM and account-based governance
- Cloud monitoring/logging tools (AWS-native)
- VPC/private networking patterns (varies)
- Serverless/container compute integration (varies)
- Data services integration patterns (varies)
Support & Community
Support typically aligns with AWS support plans; community knowledge is broad for AWS, with Bedrock-specific best practices evolving.
#10 — Google Vertex AI (Model access + routing patterns)
Short description (2–3 lines): Google Vertex AI is a broader AI platform that includes access to multiple model types and deployment options. For teams on Google Cloud, it can serve as a centralized model access layer with governance and MLOps adjacencies, even if it’s not a dedicated “LLM gateway” product.
Key Features
- Central platform for model access and deployment workflows
- Governance patterns aligned with Google Cloud identity and projects
- Integration with data and analytics tooling in the same cloud ecosystem
- Managed endpoints and operational tooling (service-dependent)
- Supports production-grade deployment patterns for AI services
- Enables standardization across teams building AI features
- Works well with enterprise cloud controls (networking, logging)
Pros
- Good fit for organizations already standardizing on Google Cloud
- Easier operationalization when your data stack is in GCP
- Helps centralize access and governance for AI initiatives
Cons
- Less of a “vendor-neutral router” compared to dedicated gateways
- Portability can be limited depending on APIs used
- Compliance specifics for Vertex AI components: Not publicly stated here (varies by service/region)
Platforms / Deployment
- Web
- Cloud
Security & Compliance
- Identity/access: Google Cloud IAM patterns (details vary)
- Audit logs/encryption: Varies by configuration
- SOC 2 / ISO 27001 / HIPAA: Not publicly stated for specific Vertex AI features here (varies by service/region)
Integrations & Ecosystem
Vertex AI typically shines when paired with GCP-native services and data pipelines.
- Google Cloud IAM and org policies
- Logging/monitoring within GCP
- Data warehouse/lake integrations (varies)
- CI/CD and MLOps-style workflows (varies)
- Application integration via APIs and client libraries (varies)
Support & Community
Support generally follows Google Cloud support tiers; community resources are extensive for GCP, with LLM platform patterns continuing to mature.
Comparison Table (Top 10)
| Tool Name | Best For | Platform(s) Supported | Deployment (Cloud/Self-hosted/Hybrid) | Standout Feature | Public Rating |
|---|---|---|---|---|---|
| LiteLLM | Self-hosted multi-provider normalization + routing | Varies / N/A | Cloud / Self-hosted / Hybrid | OpenAI-compatible proxy across many providers | N/A |
| OpenRouter | Rapid multi-model experimentation | Web | Cloud | Unified multi-model API with simplified switching | N/A |
| Cloudflare AI Gateway | Edge-adjacent control, caching, traffic management | Web | Cloud | Gateway + caching/observability patterns | N/A |
| Portkey | Managed gateway with routing + observability | Web | Cloud | Gateway control plane for routing/governance | N/A |
| Helicone | LLM observability with proxy-based capture | Web | Cloud / Self-hosted (varies) | Centralized logging/analytics for LLM calls | N/A |
| Kong AI Gateway | Enterprise API gateway teams applying policies to AI | Linux | Cloud / Self-hosted / Hybrid | API-gateway-grade policy enforcement | N/A |
| Tyk AI Gateway | API management-first AI governance | Linux | Cloud / Self-hosted / Hybrid | Policy-driven quotas/auth for AI traffic | N/A |
| Envoy AI Gateway | Platform teams wanting Envoy-native AI routing | Linux | Self-hosted / Hybrid | High-control proxy patterns for AI workloads | N/A |
| Amazon Bedrock | AWS-first enterprises needing centralized model access | Web | Cloud | Multi-model access inside AWS governance | N/A |
| Google Vertex AI | GCP-first teams standardizing AI access and ops | Web | Cloud | Central AI platform + governance in GCP | N/A |
Evaluation & Scoring of LLM Gateways & Model Routing Platforms
Scoring model (1–10 per criterion) with weighted total:
- Core features – 25%
- Ease of use – 15%
- Integrations & ecosystem – 15%
- Security & compliance – 10%
- Performance & reliability – 10%
- Support & community – 10%
- Price / value – 15%
| Tool Name | Core (25%) | Ease (15%) | Integrations (15%) | Security (10%) | Performance (10%) | Support (10%) | Value (15%) | Weighted Total (0–10) |
|---|---|---|---|---|---|---|---|---|
| LiteLLM | 9 | 6 | 8 | 6 | 7 | 7 | 8 | 7.55 |
| OpenRouter | 7 | 9 | 7 | 5 | 7 | 6 | 7 | 7.10 |
| Cloudflare AI Gateway | 7 | 7 | 7 | 7 | 8 | 7 | 6 | 7.05 |
| Portkey | 8 | 8 | 7 | 6 | 7 | 6 | 6 | 7.10 |
| Helicone | 7 | 8 | 7 | 6 | 7 | 7 | 7 | 7.20 |
| Kong AI Gateway | 7 | 6 | 8 | 7 | 8 | 7 | 5 | 6.80 |
| Tyk AI Gateway | 7 | 6 | 7 | 7 | 7 | 6 | 6 | 6.65 |
| Envoy AI Gateway | 6 | 4 | 7 | 6 | 9 | 5 | 7 | 6.20 |
| Amazon Bedrock | 8 | 7 | 8 | 8 | 8 | 8 | 6 | 7.55 |
| Google Vertex AI | 7 | 6 | 8 | 8 | 7 | 7 | 6 | 6.95 |
How to interpret these scores:
- Scores are comparative and scenario-dependent, not absolute judgments.
- A lower “Ease” score often reflects self-hosting and platform effort, not product quality.
- “Value” varies heavily by traffic patterns, model mix, and existing cloud commitments.
- Use the weighted total to shortlist, then validate with a pilot focused on latency, failure modes, and governance fit.
Which LLM Gateways & Model Routing Platforms Tool Is Right for You?
Solo / Freelancer
If you’re building a single product or prototype, favor fast setup and minimal ops:
- OpenRouter: good for experimenting with many models quickly.
- Helicone: useful if you’re iterating and want visibility into prompts, failures, and costs without building analytics.
When to avoid gateways: if you only use one provider and one model, direct SDK calls are usually simpler.
SMB
SMBs often need basic governance + cost control without hiring a dedicated platform team:
- Portkey: strong managed-gateway option if you want routing and oversight in one place.
- Cloudflare AI Gateway: compelling if your app traffic already runs through Cloudflare and you want centralized control/observability.
If you have a small but capable infra team and want portability:
- LiteLLM (self-hosted) can be the “single front door” for multiple apps.
Mid-Market
Mid-market teams typically feel pain from multiple teams, multiple services, and cost surprises:
- LiteLLM: good for building an internal AI platform with consistent routing and provider abstraction.
- Helicone: strong for cross-service observability and debugging.
- Cloudflare AI Gateway: good when edge/performance and centralized traffic controls matter.
If you already run API management:
- Kong AI Gateway or Tyk AI Gateway may fit better than adopting a separate AI-native gateway.
Enterprise
Enterprises typically prioritize security posture, auditability, data residency, and standardization:
- Amazon Bedrock: best when you’re AWS-first and want governance aligned to AWS accounts, policies, and operational tooling.
- Google Vertex AI: best when you’re GCP-first and want centralized control and integration with the GCP data ecosystem.
- Kong AI Gateway / Tyk AI Gateway: strong when your enterprise gateway program is the center of policy enforcement.
Enterprises that need vendor neutrality plus strict controls often combine:
- A self-hosted router (e.g., LiteLLM or Envoy AI Gateway)
- With enterprise security and observability tooling (SIEM, OpenTelemetry collectors, data warehouses)
Budget vs Premium
- Budget-optimized: self-hosting (LiteLLM, Envoy AI Gateway) can reduce SaaS fees but increases engineering cost.
- Premium/managed: Portkey, Cloudflare AI Gateway, and cloud platforms reduce ops overhead—often worth it when AI is revenue-critical.
Feature Depth vs Ease of Use
- If you want deep routing customization: LiteLLM and Envoy-style approaches typically offer more control.
- If you want speed and convenience: OpenRouter and managed gateways can be simpler.
- If you want enterprise policy alignment: Kong/Tyk integrate well with established gateway governance patterns.
Integrations & Scalability
- Heavy microservices/Kubernetes shops often prefer self-hosted gateways integrated with existing ingress/egress patterns.
- If you need consistent telemetry and incident response, prioritize tools that fit your observability stack (or can export cleanly).
Security & Compliance Needs
- If you need strict tenant isolation, audit trails, and centralized controls, choose products with:
- Clear RBAC and audit logging
- Support for SSO (if required)
- Deployment options that match data residency requirements
- For regulated workflows, ensure your gateway supports data minimization (redaction, structured logging, retention controls) and can be deployed in your approved environment.
Frequently Asked Questions (FAQs)
What is an LLM gateway, in practical terms?
An LLM gateway is a proxy layer that standardizes and controls how apps call language models. It can enforce policies, route requests across models/providers, and centralize logging and cost tracking.
How is a model router different from a gateway?
A gateway is the “front door” (auth, quotas, logging). A router is the decision engine that picks which model/provider to call based on rules, cost, latency, or evaluation results. Many products combine both.
What pricing models are common in this category?
Common models include per-request fees, usage-based pricing tied to volume, seat-based plans for analytics/governance, or enterprise licensing. For open-source/self-hosted, infrastructure and ops time become the main cost drivers.
What’s the biggest mistake teams make when adopting a gateway?
Treating it as a simple pass-through. The real value comes from defining routing rules, budgets/quotas, logging standards, and failure playbooks—otherwise you add complexity without gaining control.
Do gateways increase latency?
Usually there’s some overhead, but it can be small if the gateway is well-designed and deployed close to your services. Some gateways can reduce effective latency via caching, retries, or smart routing.
Can I use a gateway for both chat and agent workflows?
Yes, but make sure it supports tool calls/structured outputs and can handle multi-step traffic patterns. Agent loops can amplify spend and rate-limit risk, so quotas and observability are critical.
How do gateways help with reliability?
They can implement retries, timeouts, circuit breakers, and provider fallback routing. This helps prevent a single provider outage or throttling event from taking down your product.
What should I log (and not log) at the gateway?
Log enough to debug and meter usage (timestamps, latency, model, token counts, status codes, hashed identifiers). Avoid logging sensitive content unless required, and add redaction/retention controls where possible.
How hard is it to switch gateways later?
Switching is easier if your app uses a stable interface (often OpenAI-compatible) and you keep routing rules/policies externalized. It’s harder if your gateway becomes the home for app-specific logic without versioning.
Are hyperscaler platforms (AWS/GCP) “gateways”?
They’re often broader platforms, but they can serve as a centralized model access layer with governance in a single cloud. If you need vendor-neutral routing across many non-native providers, a dedicated gateway may still be necessary.
What are alternatives to an LLM gateway?
For small scopes: direct provider SDK calls plus basic retries and logging. For observability-only needs: an LLM monitoring tool without routing. For strict enterprise control: a general API gateway with custom AI policies.
Conclusion
LLM gateways and model routing platforms have shifted from “nice to have” to core infrastructure for teams running AI in production—especially as multi-model strategies, agentic workflows, and governance requirements become standard in 2026+.
The best choice depends on what you’re optimizing for:
- Portability and control (often self-hosted)
- Speed of adoption (managed gateways and routers)
- Enterprise governance alignment (API-gateway and hyperscaler ecosystems)
Next step: shortlist 2–3 options, run a two-week pilot that tests routing/fallback, logging/retention controls, and integration with your identity + observability stack, then decide based on real latency, failure modes, and operational effort.