DEV Community: Lior Ben-David

9 Best AI Gateways with Audit Logging for Compliance

Lior Ben-David — Thu, 23 Jul 2026 21:25:39 +0000

A comparison of the top AI gateways with the audit logging capabilities required for SOC 2, HIPAA, and GDPR compliance. For enterprises in regulated industries, Bifrost provides the most comprehensive and performant solution.

As enterprises deploy AI applications in production, the AI gateway has become a critical layer of infrastructure. It centralizes routing, enforces security policies, and provides observability into cost and performance. For organizations in regulated industries like healthcare, finance, and the public sector, one capability stands out as non-negotiable: the audit log. A robust, immutable audit trail is essential for meeting compliance standards such as SOC 2, HIPAA, and GDPR.

An AI gateway's audit log provides a verifiable record of every request, capturing who made the call, which model was invoked, what data was processed, and the resulting outcome. This traceability is a cornerstone of compliance, enabling security reviews, incident investigations, and satisfying auditor requests. This article compares nine of the best AI gateways on the market, with a specific focus on their audit logging features and suitability for enterprise compliance.

How to Evaluate AI Gateways for Compliance

When selecting an AI gateway for a regulated environment, the evaluation criteria extend beyond basic performance and model support. Key compliance-focused capabilities include:

Immutability and Tamper Evidence: Logs must be stored in a way that prevents modification or deletion. Techniques like hash-chaining can provide architectural immutability.
Granularity: The log must capture sufficient detail, including user identity, timestamps, source IP, the virtual key or credential used, the full request path, and the response status.
Attribution: It should be possible to trace every action back to a specific user or service account, a common gap in many AI deployments.
Export and Integration: The ability to export logs to a Security Information and Event Management (SIEM) system or a long-term, write-once storage bucket is crucial for retention and analysis.
Deployment Control: For sensitive data, the ability to deploy the gateway within a VPC, on-premises, or in an air-gapped environment is often a requirement.

The Top 9 AI Gateways for Audit & Compliance in 2026

This ranking assesses each gateway based on its audit logging features, enterprise security capabilities, and overall fitness for compliance-driven organizations.

1. Bifrost

Bifrost is an open-source AI gateway from Maxim AI, engineered for high-performance and enterprise-grade governance. It is designed to meet the stringent requirements of regulated industries.

Best for: Enterprise teams in regulated industries (healthcare, finance) that require a self-hosted, high-performance gateway with audit-grade logging for SOC 2, HIPAA, and GDPR compliance.

Audit Logging & Compliance Features:
Bifrost Enterprise treats audit logs as a primary feature, designed to provide the evidence trail required by auditors.

Immutable Audit Trails: Bifrost generates immutable, timestamped audit trails for every API call and configuration change, making it suitable for SOC 2, HIPAA, GDPR, and ISO 27001 evidence requirements.
Granular Event Logging: It captures detailed event data, including the authorizing identity (via virtual keys), the tool or model invoked, and the parent request, creating a unified trace of agent activity.
SIEM and Data Lake Export: Logs are designed for export to external systems like SIEMs (e.g., Splunk, Datadog) or data lakes (S3, BigQuery), allowing for long-term retention and analysis.
Deployment Control: Bifrost supports full in-VPC, on-premises, and air-gapped deployments, ensuring that no data leaves the organization's network boundary, a key requirement for HIPAA and other data residency rules.
Performance: Published benchmarks show that Bifrost adds only 11 microseconds of overhead at 5,000 requests per second, ensuring that compliance logging does not become a performance bottleneck.

2. LiteLLM

LiteLLM is a popular open-source proxy that provides a unified interface to over 100 LLM providers. Its enterprise tier adds the necessary governance features for compliance.

Best for: Platform teams that need a flexible, self-hosted, open-source solution and are able to upgrade to an enterprise license for essential compliance features.

Audit Logging & Compliance Features:
While the open-source version provides basic request logging, true audit logging is an enterprise feature.

Enterprise Audit Logs: The enterprise license enables audit logs that track administrative actions and changes to entities like keys, teams, and models, including who performed the action and when.
Log Export: Audit logs can be exported to external storage like S3, with options to use a separate, more secure bucket for compliance records.
Retention Policies: The enterprise version allows for the configuration of retention policies for audit logs.
Self-Hosted Control: As a self-hosted solution, teams retain full control over the infrastructure and log storage, but are also responsible for securing it to meet compliance standards.

3. Kong AI Gateway

The Kong AI Gateway extends the widely-used Kong API Gateway with AI-specific capabilities. It's a strong choice for enterprises already invested in the Kong ecosystem.

Best for: Large enterprises that already use Kong for API management and want to apply consistent governance and security policies to their AI traffic.

Audit Logging & Compliance Features:
Kong's audit capabilities are part of its enterprise offering, providing deep integration with its existing API management platform.

Enterprise Audit Log Plugin: Kong Gateway Enterprise includes a powerful audit log feature that records all administrative actions and API requests. This is a separate and more comprehensive feature than standard request logging.
Standardized Format: The gateway provides a standardized JSON format for AI plugin logs, simplifying the aggregation and analysis of usage data.
SIEM Integration: Audit logs can be integrated with external systems like AWS CloudTrail Lake via Lambda functions, feeding compliance data into a central security hub.
RBAC Integration: Log entries include RBAC context, showing which user or role performed an action, which is critical for compliance attribution.

4. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed service that leverages Cloudflare's global network to provide caching, analytics, and logging for AI applications.

Best for: Teams that prioritize ease of use, global performance, and a managed solution, particularly those already using the Cloudflare ecosystem.

Audit Logging & Compliance Features:
Cloudflare provides robust logging capabilities as a core part of its platform, with specific features for AI Gateway.

Default Audit Logs: Audit logs summarizing changes to gateway configurations are enabled by default for all Cloudflare plans.
Detailed Request Logging: The gateway dashboard provides detailed logs of individual requests, including prompts, responses, tokens, and cost. This can be disabled for privacy and compliance.
Log Export: Logs can be securely exported to external storage via Cloudflare Logpush, supporting compliance and long-term retention requirements. Encrypted log export is also available.
Compliance Support: Cloudflare's platform maintains numerous compliance certifications, and features like its DLP integration help support GDPR, HIPAA, and PCI DSS requirements.

5. IBM AI Gateway

Part of the IBM API Connect platform, the IBM AI Gateway is designed for enterprises seeking to apply strong governance and compliance controls to their AI services.

Best for: Large enterprises, particularly those in finance and other regulated sectors, that require a comprehensive, governance-focused solution integrated with a broader API management suite.

Audit Logging & Compliance Features:
IBM emphasizes centralized governance and audit trails as a core benefit of its gateway.

Centralized Audit Trails: The gateway funnels all LLM API traffic through a single point of control, enabling the creation of comprehensive audit trails for compliance.
Detailed Logging: It captures extensive logs for every AI request and response, which is crucial for debugging, auditing AI decisions, and post-incident analysis.
Policy Enforcement: Auditability is tied directly to the enforcement of other policies like data masking and role-based access control.

6. SS&C Blue Prism AI Gateway

The SS&C Blue Prism AI Gateway is a governance-first platform built specifically for enterprises in regulated industries that need to ensure control and auditability.

Best for: Large, compliance-focused organizations that need a managed, enterprise-scale platform with built-in guardrails and non-repudiable audit trails.

Audit Logging & Compliance Features:
The platform is explicitly "compliant by design," with audit trails as a key feature.

Non-Repudiable Audit Trails: The gateway is designed to provide strong, verifiable audit logs to prevent data exposure and prove model access history.
Built-in Compliance: The gateway includes built-in guardrails, audit trails, and role-based access controls to support compliance with frameworks like SOC 2, HIPAA, and GDPR.
Full Metadata Logging: It captures full metadata for every AI interaction, ensuring that audit records are complete and useful for regulatory reviews.

7. KrakenD

KrakenD is a high-performance, open-source API gateway that has extended its capabilities to handle AI workloads.

Best for: Teams that require a high-performance, stateless, open-source gateway and have the expertise to configure its flexible logging components for compliance purposes.

Audit Logging & Compliance Features:
KrakenD provides a flexible telemetry system that can be configured for detailed logging.

Access and Application Logs: The gateway separates access logs (user activity) from application logs (system events), allowing for distinct configuration.
Syslog and Stdout: Logs can be written to standard output or sent to a local or remote Syslog server, which can then feed into a SIEM.
ELK Stack Integration: KrakenD offers documented integration with the ELK stack (Elasticsearch, Logstash, Kibana) for centralized log analysis and dashboarding.

8. HAProxy

HAProxy is one of the world's most widely used software load balancers, and it now offers AI gateway capabilities as part of the HAProxy One platform.

Best for: Organizations already leveraging HAProxy for load balancing and security that want to extend the same performant, battle-tested infrastructure to their AI workloads.

Audit Logging & Compliance Features:
HAProxy provides extremely detailed logs that are foundational for observability and compliance.

Detailed Connection and Request Logging: HAProxy logs a wealth of information about traffic, including timing data, connection counters, headers, and status codes, with millisecond accuracy.
Syslog Export: It natively integrates with Syslog servers, allowing logs to be forwarded to centralized logging platforms for analysis and retention.
Centralized Observability: When used with the HAProxy Fusion Control Plane, it offers centralized visibility into over 150 metrics specific to performance, security, and AI queries.

9. AWS API Gateway (with Lambda)

While not a dedicated AI gateway product, AWS API Gateway is a common choice for teams building their own serverless AI control plane on AWS. When combined with AWS Lambda for logic and CloudWatch for logging, it can form a compliant solution.

Best for: Teams with deep AWS expertise that prefer to build a custom, serverless AI gateway using managed AWS services.

Audit Logging & Compliance Features:
The compliance capabilities come from integrating several AWS services.

CloudWatch Logs: API Gateway access logs can be sent to Amazon CloudWatch Logs, providing a centralized and durable log repository.
Detailed Access Logging: Custom access logging can be configured to capture granular details for each request, which is essential for audit purposes.
Lambda for Logic: A Lambda function behind the API Gateway can enforce business logic, enrich log data, and ensure that all necessary information is captured before invoking a model via services like Amazon Bedrock.

Conclusion: Choosing the Right Gateway for Compliance

For organizations where compliance is a primary concern, an AI gateway is not just a tool for routing and cost control; it is a critical component of the governance, risk, and compliance (GRC) strategy. The choice depends on balancing the need for deep, audit-grade logging with factors like performance, deployment model, and existing infrastructure.

While many gateways offer logging, solutions like Bifrost stand out by treating immutable audit trails and enterprise security as core design principles, rather than add-ons. Its combination of high performance, self-hosted control, and features explicitly built for SOC 2 and HIPAA evidence makes it a leading choice for teams building the next generation of compliant AI applications.

Sources

HIPAA, GDPR, and SOX Don't Have an AI Exemption - Kiteworks
AI Governance Audit Trail: Immutable Ledger for HIPAA & DORA - The BrightByte
SOC 2 vs HIPAA: Key Compliance Differences - ISMS.online
Bifrost Enterprise Documentation - Audit Logs - Maxim AI
LiteLLM Documentation - Audit Logs - LiteLLM

What Is AI Infrastructure? The 2026 Stack, Explained

Lior Ben-David — Tue, 14 Jul 2026 14:42:46 +0000

The 2026 AI infrastructure stack encompasses hardware, data, and software layers that enable scalable AI applications. This guide explains its core components, from compute to governance, with Bifrost as a key AI gateway for enterprise deployment.

The rapid adoption of artificial intelligence has propelled AI infrastructure into the core digital fabric for enterprises worldwide. By 2026, AI is no longer a niche technology; it is a foundational component of business operations, requiring specialized infrastructure that far exceeds traditional IT capabilities. This complex, layered system supports everything from data processing and model training to reliable AI inference and continuous deployment. Understanding its core components and how they interconnect is essential for organizations building and scaling AI applications. Bifrost, an open-source AI gateway built in Go, represents a critical layer within this modern stack, helping teams manage the complexities of model access and governance.

Defining the AI Infrastructure Stack

AI infrastructure refers to the combination of hardware and software components specifically designed to support AI workloads, including machine learning, deep learning, and large-scale data processing. Unlike conventional IT infrastructure, AI infrastructure is optimized to handle the intensive computational requirements and massive datasets characteristic of AI applications. It forms the underlying system that enables data processing, model training, AI inference, deployment, and lifecycle management.

The AI infrastructure stack consists of several interconnected layers, working in concert to facilitate the entire AI development and deployment lifecycle. These layers typically include compute resources, storage systems, networking, operating environments, orchestration platforms, and supporting software frameworks. A well-designed stack enables scalable training, reliable AI inference, and consistent deployment across various environments, from cloud to on-premises to edge locations.

Core Components of the 2026 AI Infrastructure

Modern AI infrastructure is a sophisticated blend of specialized hardware, robust data pipelines, intelligent software layers, and comprehensive management tools.

Compute and Hardware

At the foundation of any AI infrastructure lies high-performance computing hardware. Graphics Processing Units (GPUs) continue to dominate AI training workloads due to their parallel processing capabilities, which significantly accelerate neural network computations. Tensor Processing Units (TPUs) from Google, more specialized Application-Specific Integrated Circuits (ASICs), are increasingly used for deep learning tasks due to their high throughput and efficiency. Alternative accelerators are gaining traction for inference due to their energy efficiency and cost-effectiveness.

Beyond processors, AI hardware incorporates high-performance servers with ample memory and storage, vital for handling the massive datasets used in model training. High-bandwidth, low-latency networking solutions are also crucial, ensuring data moves rapidly between storage and compute units. As power densities increase, advanced cooling solutions, including liquid cooling, are becoming standard in data center designs.

Data Management and Pipelines

AI systems are only as good as the data they consume. Effective data management involves robust pipelines for data ingestion, cleaning, transformation, and augmentation. These processes ensure that AI models receive high-quality input, which is critical for accurate training and reliable performance. Vector databases, for instance, have become essential for storing embeddings and powering retrieval-augmented generation (RAG) architectures, allowing models to access trusted, external knowledge sources. Organizations are strategically modernizing data pipelines by improving data quality, governance, and integration across systems.

Model Development and Management

This layer encompasses the tools and practices for building, training, evaluating, and managing AI models throughout their lifecycle. Machine learning frameworks such as TensorFlow and PyTorch provide the building blocks for model development. MLOps (Machine Learning Operations) practices combine ML, DevOps, and data engineering to automate and streamline workflows, from continuous integration/continuous delivery (CI/CD) pipelines for AI applications to version control and deployment. The trend towards smaller, more specialized LLMs, optimized for inference, is gaining momentum, supporting the rise of agentic AI and edge decision systems by offering faster, cheaper, and more predictable performance.

AI Gateway / Inference Layer

Once models are trained, they need to be efficiently deployed and managed in production. An AI gateway acts as a unified entry point, routing, authenticating, observing, and governing traffic to multiple LLM providers from a single API. This layer is crucial for managing the complexities of multi-provider environments, offering capabilities such as automatic failover and intelligent load balancing to ensure high availability and reliability [cite: bifrost-context]. An AI gateway also provides a centralized control plane for cost optimization, allowing teams to reduce expenses through features like semantic caching and applying rate limits and budgets across different models and providers.

Bifrost, the AI gateway, offers a unified, OpenAI-compatible API that simplifies access to over 1000 models, enabling organizations to switch providers or models by changing only a base URL [cite: bifrost-context]. Beyond simple routing, Bifrost includes features such as semantic caching to reduce repeat-query costs and virtual keys for granular access control and budget management. Its capabilities extend to acting as an MCP gateway, exposing tools to clients like Claude Desktop and supporting autonomous agentic workflows with its Agent Mode.

AI Governance and Security

As AI moves into critical business processes, governance and security become paramount. AI governance encompasses the principles, roles, processes, and controls an organization uses to deploy AI safely, ethically, and in compliance with regulations. It establishes accountability, manages risk, and ensures transparency and auditability throughout the AI lifecycle. This includes implementing access controls, guardrails, and audit logging to protect sensitive data and prevent unintended model behavior. Prominent frameworks guiding this area include the NIST AI Risk Management Framework and ISO/IEC 42001.

Crucially, the Bifrost AI gateway establishes these governance and security controls (virtual keys, budgets, guardrails, audit logs) centrally. Bifrost Edge extends this same governance and security to AI traffic on employee machines, with endpoint enforcement on each device, addressing the challenge of shadow AI. This combined "AI Gateway + Bifrost Edge" approach ensures that even AI used on employee laptops and in desktop applications adheres to organizational policies and regulatory requirements [cite: bifrost-edge-context].

Observability and Monitoring

Effective AI infrastructure demands comprehensive observability to ensure performance, identify issues, and maintain quality in production. This involves real-time tracking, debugging, and resolving quality issues with alerts. Tools for distributed tracing, automated quality checks, and performance metrics help teams monitor model behavior, latency, and cost in live environments. Integrating with platforms like Prometheus and OpenTelemetry allows for deep insights into the AI system's health and performance [cite: docs.getbifrost.ai/features/observability/default].

The Evolution of AI Infrastructure

The AI infrastructure landscape is undergoing continuous evolution. A significant trend in 2026 is the shift from monolithic, single-provider architectures to more modular, multi-cloud, and hybrid environments. While cloud infrastructure remains the default deployment model for many LLM systems, enterprises are increasingly adopting hybrid deployment models to balance regulatory compliance, data sovereignty, security, latency, and operational efficiency. For organizations with sensitive or proprietary data, on-premises AI platforms offer unmatched control over data, customization, and cost predictability over time.

The explosive growth and autonomy of AI agents are also reshaping infrastructure demands. These agents, capable of executing multi-step tasks across enterprise workflows, necessitate robust, flexible infrastructure that can support reasoning, retrieval, tool use, and execution. This shift requires greater emphasis on distributed AI systems and an architecture that allows for seamless workload mobility and data portability between environments.

Key Considerations for Building AI Infrastructure

Building an AI-ready infrastructure in 2026 involves strategic planning and a clear understanding of an organization's specific AI use cases and infrastructure needs.

Scalability and Elasticity: Infrastructure must dynamically adapt to varying AI workloads, scaling compute and storage resources up or down as needed.
Cost Optimization: Strategic choices in hardware, cloud providers, and software tools significantly impact operational costs. Continuously optimizing performance and cost is crucial.
Security and Compliance: Designing with a defense-in-depth philosophy is essential, integrating controls that address both traditional cybersecurity threats and unique AI safety risks. This includes robust AI governance frameworks and data protection pipelines.
Flexibility and Integration: The stack should be designed for modular integration, allowing organizations to adapt their architecture as needs and technologies evolve. This often involves supporting hybrid and multi-cloud environments.
Observability and Governance: Implementing observability and governance across the entire stack provides the necessary oversight for safe, reliable, and compliant AI deployment. Defining workloads before comparing vendor offerings is critical to avoid infrastructure evaluation mistakes.

The 2026 AI infrastructure stack is a dynamic and evolving ecosystem. Successfully navigating this landscape requires a holistic approach that balances performance, cost, security, and governance. Leveraging purpose-built tools, like the Bifrost AI gateway, can streamline complexity and accelerate the deployment of reliable, enterprise-grade AI applications. Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository.

Sources

Mirantis. (February 3, 2026). AI Infrastructure Stack: Essentials & Guidelines.
Cloudian. (2026). AI Infrastructure: Key Components and 6 Factors Driving Success.
Makebot.ai. (January 9, 2026). 10 Key LLM Market Trends for 2026.
Kong Inc. (June 26, 2026). What is AI Governance? 2026 Framework Guide.
AIntelligenceHub. (2026). AI Infrastructure in 2026: Chips, Cloud, and Capacity Choices.

Best Tools for Benchmarking LLM Provider Performance

Lior Ben-David — Thu, 09 Jul 2026 09:32:21 +0000

Measuring LLM provider performance is critical for production AI applications. This post explores leading tools for benchmarking latency, throughput, cost, and reliability across providers, positioning Bifrost as a top choice for integrated performance and enterprise-grade reliability.

The operational challenges of running AI applications at scale often extend beyond model quality. Engineers building production AI systems must contend with variable provider latency, unexpected outages, and escalating token costs. Without robust benchmarking, these factors can significantly impact user experience and the bottom line. Evaluating the performance of Large Language Model (LLM) providers is therefore essential for optimizing deployments and ensuring reliability. Bifrost, an open-source AI gateway built in Go by Maxim AI, provides integrated tools and capabilities that address these challenges directly, offering a powerful option for teams focused on enterprise-grade performance and control.

Why Benchmark LLM Provider Performance?

Benchmarking LLM provider performance goes beyond simply comparing model outputs. It involves assessing the underlying infrastructure's responsiveness, stability, and cost-effectiveness under real-world conditions. This process helps teams:

Optimize Costs: LLM pricing varies significantly across providers and models, typically based on token usage. Benchmarking helps identify the most cost-efficient options for specific workloads.
Enhance User Experience: Latency is a critical factor for interactive AI applications. Measuring and optimizing for Time to First Token (TTFT) and overall response time ensures a smooth user experience.
Ensure Reliability: Providers can experience outages or performance degradation. Benchmarking allows teams to test failover strategies and identify providers that offer consistent uptime and low error rates.
Plan for Scale: Understanding throughput (requests per second or tokens per second) helps predict how an application will perform under heavy load and aids in capacity planning.

Core Metrics for LLM Provider Benchmarking

Effective LLM provider benchmarking focuses on several key performance indicators:

Latency: This refers to the time it takes for a request to be processed. Key metrics include:
- Time to First Token (TTFT): The delay until the first token of a streaming response is received, crucial for perceived responsiveness.
- Time to Last Token (TLT): The total time until the entire response is received.
- Inter-Token Latency: The delay between consecutive tokens, impacting the fluidity of streaming responses.
- P50, P95, P99 Latency: Percentile measurements that indicate the latency experienced by the majority of requests, as well as the long-tail latency affecting a smaller percentage.
Throughput: This measures the volume of requests or tokens a provider can handle per unit of time.
- Requests Per Second (RPS): The number of inference requests an LLM can handle per second, directly indicating system capacity.
- Output Tokens Per Second (tokens/s): How quickly the model generates response tokens, reflecting generation speed.
Cost: The financial expenditure associated with using a particular LLM provider, typically calculated per million input and output tokens.
Reliability: The consistency of service, measured by factors like success rates, error rates, and the effectiveness of failover mechanisms under adverse conditions.

Bifrost: Integrated Performance and Reliability Benchmarking

For enterprises running mission-critical AI workloads, integrated benchmarking capabilities within an AI gateway are invaluable. Bifrost is designed to address the complexities of multi-provider LLM deployments, offering both low-overhead performance and robust tooling for evaluation. In sustained benchmarks at 5,000 requests per second, Bifrost adds only 11 microseconds of overhead per request, making it virtually transparent in a production environment.

As an AI gateway, Bifrost provides:

Built-in Benchmarking: The platform offers tools to run custom benchmarks and analyze real-world performance metrics, allowing teams to quantify latency, throughput, and error rates across different LLM providers and models. Benchmarking guides are available to help users run their own benchmarks and understand performance on various machine specifications, such as t3.medium and t3.xlarge instances.
Automatic Failover and Load Balancing: Bifrost routes requests across providers and keys, automatically falling back to healthy alternatives during outages. This built-in reliability is critical for maintaining uptime and can be measured directly through the gateway's observability features.
Centralized Observability: Bifrost provides native Prometheus metrics and OpenTelemetry (OTLP) integration, allowing teams to monitor latency, token usage, and errors in real-time. This observability is essential for understanding performance under load and identifying bottlenecks.
Governance and Cost Control: With virtual keys, budgets, and rate limits, Bifrost enables granular control over LLM consumption, helping teams track and optimize spending across different projects and users.
Enterprise-Grade Capabilities: Beyond performance, Bifrost supports advanced features like clustering for high availability, adaptive load balancing, and in-VPC deployments, all of which contribute to measurable improvements in reliability and operational efficiency. It also extends its robust governance and security controls through Bifrost Edge, which applies policies to AI traffic on employee machines and actively governs approved applications and MCP servers at the endpoint.

Specialized Tools for Low-Level Performance Analysis

While a robust AI gateway offers comprehensive insights, specialized tools delve deeper into the raw inference performance of LLM infrastructure:

NVIDIA GenAI-Perf and AIPerf: These tools are purpose-built for detailed LLM performance benchmarking, particularly for understanding inference-level metrics like throughput and latency across different GPU configurations. They provide insights into optimizing hardware for LLM workloads.
LLMPerf: An open-source tool aiming for reproducibility and clarity in LLM performance benchmarks. It offers insights into performance, accuracy, and reliability across various LLM inference providers.
LLM Locust: Designed to overcome the limitations of traditional load testing tools for LLM-specific characteristics like streaming and token-level granularity. It tracks metrics such as Time to First Token (TTFT), Output Tokens per Second (tokens/s), and Requests per Second (RPS) under load.

These tools are particularly useful for hardware sizing, infrastructure optimization, and comparing the raw capabilities of different model serving setups.

Open-Source Frameworks for Application-Level Evaluation

Several open-source frameworks focus more broadly on LLM evaluation, often emphasizing model output quality and application behavior rather than raw provider infrastructure performance. However, many can still capture latency metrics during their evaluations:

OpenAI Evals: This open-source framework from OpenAI allows teams to define custom evaluations and run LLMs against datasets, scoring outputs based on various criteria. While its primary focus is on model quality and capability, it can be configured to track performance metrics like latency.
LangChain Evaluation: LangChain provides tools for evaluating LLM applications, including methods like LLM-as-a-Judge and deterministic testing. It helps assess accuracy, relevance, and completeness, and also includes mechanisms for tracking latency. LangSmith, an observability platform for LangChain, integrates with these evaluations to visualize results and track trends.
LiteLLM: As an open-source Python library and proxy, LiteLLM provides a unified API for over 100 LLM providers. It includes basic benchmarking functionality and publishes its own performance benchmarks, though comparisons suggest it may introduce higher overhead than Go-based alternatives like Bifrost under high concurrency.

These frameworks are valuable for ensuring the quality and correctness of AI applications, but teams should use them in conjunction with tools that provide deep infrastructure performance insights when provider-level metrics are the primary concern.

LLM Cost Comparison Tools

Cost is a crucial factor in LLM deployment. Several online tools assist in comparing pricing across various models and providers:

WhatLLM.org: This platform aggregates benchmark data, real-world pricing, and throughput metrics for hundreds of LLMs from numerous providers, offering a unified interface for comparison.
Langtail and Rows Calculators: Tools like the LLM Price Comparison Tool by Langtail and Rows' LLM API Price Calculator allow users to estimate costs based on input/output tokens and model choices across different providers.

These tools provide an essential overview for budget planning and model selection.

Choosing the Right Benchmarking Strategy

Selecting the optimal LLM benchmarking tools depends on an organization's specific needs. For teams focused on deep, low-level inference performance and hardware optimization, specialized tools like NVIDIA GenAI-Perf or LLM Locust offer granular control and insights. For evaluating the functional correctness and output quality of LLM applications, frameworks like OpenAI Evals and LangChain provide robust solutions.

For enterprises building and operating production AI at scale, an integrated approach often yields the most comprehensive results. This typically involves combining dedicated performance benchmarking with a robust AI gateway that offers built-in observability, governance, and reliability features. A gateway like Bifrost can serve as the central point for routing and managing LLM traffic, simultaneously collecting critical performance data and enforcing policies.

Next Steps

Organizations evaluating their LLM infrastructure and seeking to optimize provider performance can explore Bifrost for its integrated benchmarking, low overhead, and enterprise-grade features. Teams can request a Bifrost demo or review its open-source repository to understand its capabilities for high-performance, reliable AI deployments.

Sources

Bifrost: The Fastest Open-Source Enterprise LLM Gateway. Maxim AI.
LLM performance benchmarks | LLM Inference Handbook. BentoML.
LLM Locust: Benchmarking LLM Performance at Scale. Truefoundry.
WhatLLM.org: Compare LLMs by Benchmarks, Price & Speed — Live Rankings. WhatLLM.org.
LLM Evaluation Tools: Frameworks, Benchmarks & Metrics. LangChain.