How should Enterprises evaluate LLM Gateway for Scale?

Enterprises today are racing to harness the power of large language models (LLMs) in everything from customer service chatbots to advanced analytics pipelines. But as you move beyond proof-of-concepts into production, you’ll quickly discover that calling an LLM directly isn’t enough, especially when your SLAs demand rock-solid performance, tight security, and the flexibility to juggle multiple model providers or bring your own. That’s where an LLM gateway comes in, a thin, purpose-built layer that sits between your applications and the ever-evolving ecosystem of LLM endpoints.

In the sections that follow, we will walk through a five-pillar evaluation framework, covering performance and latency, model flexibility, operational controls, observability, and security compliance, that every enterprise should use before committing to a gateway solution. 

What Is an LLM Gateway?

An LLM gateway is a centralized proxy layer that standardizes and manages all interactions between your applications and diverse language model endpoints. Rather than duplicating authentication checks, retry mechanisms, and logging across individual services, you channel every request through this single service. The gateway then dispatches prompts to the appropriate backend, whether an on-premises LLaMA instance, a dedicated OpenAI deployment on Azure, or Amazon Bedrock, abstracting away provider-specific API differences.

Beyond simple request routing, a robust gateway delivers several essential capabilities:

• Authentication & Authorization
  TrueFoundry’s LLM Gateway integrates with enterprise identity systems (OIDC/SAML) to validate each incoming request’s credentials. Once authenticated, the gateway applies role‑based access control (RBAC) policies defined in declarative YAML to restrict which users or service accounts can invoke specific models or endpoints. This two‑step process ensures that only authorized actors gain access and that permissions are enforced consistently across your organization.

• Resilience Controls
  The gateway enforces configurable rate limits at per‑user, per‑team, and per‑model scopes to prevent traffic surges from overwhelming model hosts. It dynamically distributes requests across replicas using real‑time CPU and latency metrics. 

• Observability & Auditing
  Captures detailed traces of each prompt and response, including latency metrics and contextual metadata. Logs are stored in a high-performance backend (for example, ClickHouse or S3) and exposed via dashboards and APIs for compliance and troubleshooting.
  

• Operational Governance
  TrueFoundry’s gateway enforces governance by integrating model access and control into GitOps workflows. This is achieved through declarative, versioned YAML policies that define model access rules and permissions. Access is controlled with role‑based permissions, restricting which teams or service accounts can call specific models and endpoints. Usage caps and quotas are defined alongside access rules to ensure consistent enforcement and clear audit trails. All policy changes follow pull‑request workflows, enabling peer reviews, CI validation, and straightforward rollbacks.

For enterprises, consolidating these concerns into a gateway yields significant benefits. Development teams consume a single, uniform API rather than juggling multiple provider SDKs. Security and compliance teams gain a unified enforcement point. Operations teams can benchmark end-to-end throughput and identify bottlenecks. And as new model endpoints, public or private, become available, adding them to the gateway instantly extends access across all applications. In short, an LLM gateway transforms disparate API calls into a secure, scalable, and manageable platform.

Why Enterprises Should Evaluate LLM Gateways

Adopting an LLM is only half the battle; ensuring it operates reliably at scale is the other. Without a gateway, each service integrates directly with model endpoints, leading to fragmented implementations, inconsistent security postures, and unpredictable performance under load. For enterprise use cases, these gaps translate into missed SLAs, compliance risks, and opaque troubleshooting.

1. First, a gateway centralizes traffic management. You can enforce consistent rate limits, retries, and routing rules from one place, eliminating ad-hoc implementations that often break when demand spikes. 

2. Second, it standardizes security. Rather than scattering token validation and SSO integrations across multiple codebases, you configure authentication and authorization once at the gateway. This unified approach simplifies audits and reduces the surface area for misconfigurations.

3. Third, a gateway offers end-to-end observability. Instead of piecing together logs from different microservices, you capture every prompt and response in a consistent format, with detailed timing and metadata. That visibility is critical for root-cause analysis and capacity planning. 

Finally, as new models and providers emerge, be they self-hosted, open source, or managed cloud services, a gateway allows you to onboard them with minimal code changes. In sum, evaluating LLM gateways is not optional for enterprises, it is a necessary step to ensure reliability, security, and operational clarity as usage scales.

Five Dimensions of Gateway Evaluation

When assessing an LLM gateway, enterprises should rigorously test across five critical dimensions. Each pillar ensures your platform meets production demands from both technical and operational perspectives. 

1. Performance & Latency

Measure the gateway’s own overhead under real-world conditions. Start by recording baseline round-trip times for single requests, then increase traffic in stages, for example, from 10 to 300 requests per second. Observe how latency scales, does it remain steady or spike as throughput climbs? Identify any providers that introduce inconsistent delays. Consistent low-latency performance means your applications can meet tight response-time SLAs even under heavy load.

2. Model Agnosticism

Confirm the gateway supports registering and invoking models from diverse sources without code changes. Test onboarding an on-prem LLaMA deployment, a dedicated OpenAI endpoint, and AWS Bedrock all within the same gateway instance. Validate that authentication, request formats, and streaming responses work uniformly. True model agnosticism lets you switch providers or add private endpoints seamlessly as pricing, performance, or regulatory needs evolve.

3. Control Knobs

To manage rate-limiting between multiple teams, assign each team a specific daily budget for GPT-4 usage, such as $100 for the LLM Engineering team, $30 for the Product team, and $20 for the Other team. Once a team's budget is exhausted, requests are automatically routed to cost-effective fallback models like LLaMA-3 or GPT-3.5. This approach ensures that each team stays within their allocated quota while still maintaining functionality with alternative models. For concurrent traffic, the system independently tracks each team's usage and enforces limits, providing seamless fallback without disruption. This structure allows granular control over model usage, ensuring fair distribution and cost efficiency across teams.

4. Observability & Governance

Test end-to-end tracing by issuing a complex prompt and reviewing the detailed audit log. Ensure each invocation records timestamps, latency breakdowns, and metadata such as user ID and model version. Verify that logs flow into your chosen backend, for example, ClickHouse or S3, and appear correctly on dashboards or via APIs. Comprehensive observability is vital for troubleshooting, capacity planning, and meeting compliance audits.

5. Security & Compliance

Validate integration with your identity provider using both OIDC and SAML flows. Confirm that only authenticated and authorized requests succeed while unauthorized calls are blocked with appropriate error codes. Review Helm chart defaults and override resource limits, read-only file system settings, and PodSecurity policies to match corporate security baselines. Strong security and governance controls are non-negotiable when handling sensitive data at scale.

Beyond Core Features: Additional Evaluation Criteria

Once a gateway meets the basic pillars, these five extra considerations help you choose a platform that aligns with your broader enterprise needs:

1. Vendor Support & SLAs
   Look for guaranteed uptime commitments, clearly defined incident response windows, and a dedicated support channel. Strong SLAs minimize downtime risk and keep your teams productive.
   
2. Cost Transparency & Billing Controls
   Evaluate whether the platform provides granular usage reports (by model, endpoint, team) and tools to enforce budget limits. Predictable pricing and real-time alerts prevent bill shock.
   
3. Integrations & Ecosystem
   Check for ready-made SDKs, CLI tools, and connectors for common frameworks (e.g., Python, Java, Terraform). Seamless integration accelerates development and reduces maintenance.
   
4. Customization & Extensibility
   Ensure you can inject custom preprocessing or post-processing logic—via webhooks, plugins, or serverless functions—to tailor model inputs and outputs to your unique workflows.
   
5. Compliance Certifications
   Verify certifications like SOC-2, ISO 27001, GDPR, or HIPAA readiness. Confirm that data residency options and encryption controls meet your security and regulatory requirements.

TrueFoundry Enterprise LLM Gateway: Scale with Confidence

Struggling to scale your LLM workloads securely and reliably? TrueFoundry’s Gateway delivers enterprise-grade performance, governance, and observability—so you can focus on AI, not infrastructure.

• Unified API: Single REST endpoint for 250+ LLMs.
• ~3 ms Overhead: Sub-5 ms latency at 250 RPS on 1 vCPU.
• Built-In Tracing: OpenTelemetry spans to ClickHouse dashboards.
• Enterprise Security: OIDC/SAML SSO, YAML RBAC, TLS 1.3.

Get Started with Truefoundry

Features of TrueFoundry’s LLM Gateway

TrueFoundry’s gateway is engineered to excel across the five evaluation pillars, blending high performance, seamless management, and enterprise-grade controls. Below, we break down each core feature in a structured format.

Unified API & Multi-Model Support

TrueFoundry exposes a single RESTful interface that abstracts away provider-specific quirks. Whether you’re calling an on-prem LLaMA instance or a managed OpenAI endpoint, your code stays the same.

• Register new models via declarative YAML or API calls
• Normalize request formats, authentication headers, and streaming payloads
• Auto-generate client SDKs for popular languages (Python, Java, JavaScript)
  

This unified model access layer minimizes integration effort and future-proofs your applications. You can add or swap providers without touching existing code.

Ultra-Low-Latency 

TrueFoundry’s LLM Gateway maintains near-zero overhead by design. Real-world benchmarks show that adding the gateway introduces just 3 ms of latency at up to 250 requests per second and 4 ms once you exceed 300 requests per second. On a minimal footprint, a single vCPU and 1 GB of RAM, the gateway scales linearly until approximately 350 RPS, at which point CPU utilization reaches 100 percent. For higher throughput, simply add CPU capacity or replicas.

For example, a t2.2xlarge AWS spot instance (approximately $43 per month) can sustain around 3000 RPS without any performance degradation. Because the gateway can be deployed at the edge, close to your applications, network hops are minimized, and response times remain consistent. These documented metrics demonstrate that TrueFoundry’s LLM Gateway delivers predictable high-throughput performance even under heavy load, enabling teams to maintain SLA commitments without over-provisioning infrastructure.

GitOps-Driven Configuration

Every aspect of your gateway’s behavior lives in version-controlled Git repositories. Helm charts and YAML files like the rate-limiting config.YAML defines model endpoints, rate-limit rules, load-balancing settings, and prompt templates, ensuring full auditability.

• Treat configuration changes like code with PR reviews and approvals
• Automate deployments via CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI)
• Roll back to known states instantly if a policy update misbehaves

By embedding these policies in Git (and deploying them via the TrueFoundry CLI), you enforce best practices, reduce human error, and accelerate policy governance across teams. The screenshot above illustrates how easy it is to author and version a complex rate-limit rule, then push it through your existing review process.

Built-In Observability & Prompt Analytics

TrueFoundry captures rich telemetry on every invocation, from timestamps and latency to input/output logs. Data streams into ClickHouse for real-time querying or S3 for long-term archival.

• Full trace visualization of prompt → model → response flows
• Prebuilt dashboards for request volumes, error rates, and latency heatmaps
• API endpoints for ad-hoc log retrieval and compliance reporting
  

With this level of insight, you can troubleshoot in minutes, track usage trends, and demonstrate audit trails to regulators. Your team gains confidence in operational clarity.

Comprehensive Security Controls

Security is baked into every layer of the gateway, from authentication to runtime hardening. Integrations with OIDC and SAML providers and PodSecurity policies ensure compliance.

• Enforce user- and role-based permissions via enterprise SSO
• Harden pods with resource limits, read-only filesystems, and CIS benchmarks
• Encrypt data at rest (via customer-managed keys) and in transit (TLS 1.3)
  

TrueFoundry’s security posture meets even the strictest enterprise requirements. Sensitive data remains protected without sacrificing performance.

TrueFoundry at Scale: Enterprise-Grade Excellence

TrueFoundry’s LLM gateway does more than meet evaluation pillars—it elevates the standard for production deployments. By combining a lightweight in-memory proxy, GitOps governance, and hardened controls, it delivers consistency and resilience across global environments.

First, the FastLight proxy operates entirely in memory and adds under 5 ms of overhead even as you grow from tens to thousands of requests per second. Pods provision and deprovision automatically based on traffic, so you avoid both over-provisioning and cold-start delays. Second, the hub-and-spoke control plane keeps management centralized and lean, while regional gateway pods live near your users or data for minimal latency.

Operationally, your entire configuration is stored in Git. Adjust rate limits or introduce a new private endpoint by updating a Helm chart, merging a pull request, and letting CI/CD pipelines roll out changes. If an update misbehaves, simply revert the PR to return to a known good state.

TrueFoundry also embeds enterprise security by default. Role-based access controls, SSO integration, and PodSecurity policies accompany every deployment. Audit logs stream to ClickHouse or S3, giving security teams real-time visibility as usage scales.

Whether you run 100 RPS in one region or 10 K RPS across five continents, TrueFoundry’s gateway delivers the performance, reliability, and control that enterprises require. It shifts LLM operations from “making it work” to “making it scale.”

Conclusion

Evaluating an LLM gateway is a critical step in scaling AI applications securely and reliably. By focusing on performance, model flexibility, control policies, observability, and security, you can select a gateway that supports both current needs and future growth. TrueFoundry’s in-memory FastLight proxy, GitOps-driven governance, and enterprise-grade controls make it an ideal choice for organizations that demand scale without compromise. Start your evaluation today and turn LLM operations into a competitive advantage.