Your traditional monitoring dashboards may show perfect infrastructure health, but that doesn't guarantee your large language model (LLM) application is running correctly. AI observability is the crucial fourth pillar that adds behavioral telemetry to detect and troubleshoot unique issues like hallucinations, policy violations, and runaway costs in real time.
Your dashboards scream 99.9% uptime. Latency stays well under 200ms. Error rates dwell at zero. These operational metrics look perfectly all right.
Yet customer complaints continue to hit all-time highs. Your AI assistant prescribes incorrect medical dosages. Your cloud bill tripled overnight. While traditional monitoring looks perfect, reality paints a different picture.
That’s the paradox of LLM operations. Your system runs flawlessly, going by every classic metric. But it hallucinates facts, violates safety policies, and burns through budgets at an alarming rate.
Between traditional monitoring and reality sits **AI observability**. Traditional monitoring tracks infrastructure health. It can't detect hallucinations, runaway costs, or policy violations. AI observability adds behavioral telemetry on top of standard logs, metrics, and traces.
Though traditional monitoring tells you the patient has a pulse, AI observability tells you if the diagnosis is correct, the treatment is affordable, and the hospital isn't leaking data.
In this post, you'll learn:
AI observability is the disciplined collection and analysis of telemetry across all AI system components to fully comprehend performance, cost, quality, and safety in real time.
This practice enables you to answer critical questions, such as:
Modern LLM applications face unique challenges. They are[ non-deterministic, resource-hungry, and challenging to test and control](https://neptune.ai/blog/llm-observability) non-deterministic, resource-hungry, and challenging to test and control. The standard monitoring approaches fall short in monitoring them.
Traditional observability works well for deterministic systems — environments where the same input reliably produces the same output. AI systems challenge these assumptions in several important ways.
Non-deterministic outputs
With LLMs, the same prompt can generate different responses, especially when temperature settings are above zero. This variability makes simple pass/fail checks ineffective. Instead, teams need continuous quality assessment and probabilistic evaluation frameworks to understand whether outputs meet expectations.
Datadog notes these applications present ["challenges due to the complexity of LLM chains, their non-deterministic nature and the security risks they pose"](https://datadog.gcs-web.com/news-releases/news-release-details/datadog-llm-observability-now-generally-available-help/)"challenges due to the complexity of LLM chains, their non-deterministic nature and the security risks they pose".[1] Every response requires individual quality scoring. In practice, this means every response must be evaluated on its own merits rather than assumed to be correct.
Continuous evaluation over binary checks
Traditional systems typically either work or fail. LLMs operate on a spectrum of performance. A response may be technically successful yet factually incorrect, or safe to deliver but unnecessarily expensive. Success is no longer a single metric — it spans quality, safety, cost, and user experience.
Emergent behaviors
LLMs can develop unexpected capabilities and failure modes once deployed. As Neptune AI observes: ["When an LLM application returns an unexpected response or fails with an error, we're often left in the dark"](https://neptune.ai/blog/llm-observability)"When an LLM application returns an unexpected response or fails with an error, we're often left in the dark".[2] Models drift, behaviors evolve, and subtle performance shifts often go undetected by classic dashboards.
Traditional observability rests on three pillars:
AI systems demand a fourth pillar: **behavioral signals**.
Behavioral signals capture dimensions that infrastructure metrics alone cannot, such as quality, safety, and cost. Datadog’s LLM observability, for example,[ "provides end-to-end tracing across AI agents with visibility into inputs, outputs, latency, token usage, and errors at each step"](https://www.datadoghq.com/product/llm-observability/) "provides end-to-end tracing across AI agents with visibility into inputs, outputs, latency, token usage, and errors at each step".
The fourth pillar encompasses the following.
While traditional observability signals remain crucial, AI systems generate unique behaviors that require domain-specific measurement. These AI-specific signals move beyond infrastructure health to provide deep visibility into response quality, cost, and user impact, ensuring your LLMs perform reliably and effectively in production.
Foundational observability signals remain essential for AI systems. While the technology is evolving rapidly, these core signals continue to provide the visibility teams need to operate reliably at scale.
Logs/events
Logs tell the story of how your AI system behaves in production. Capture key details such as model identifiers, timestamps, and request-response pairs to create a clear operational record. Before storing any data, make sure Personally Identifiable Information (PII) is properly redacted to reduce privacy and compliance risk. It’s equally important to retain error messages and system state changes, as these signals are often critical during troubleshooting.
Developers ["gain insight by recording prompts and user feedback, tracing user requests through the components, monitoring latency and API usage"](https://neptune.ai/blog/llm-observability)"gain insight by recording prompts and user feedback, tracing user requests through the components, monitoring latency and API usage". Structuring logs for searchability and analysis makes it much easier to investigate incidents, identify patterns, and continuously improve system performance.
Metrics
Metrics provide quantifiable indicators of system health and help teams understand how AI workloads perform under real-world conditions. Track resource utilization across:
Traces
Tracing maps the complete journey of every request, offering end-to-end visibility into how your AI system operates. Modern LLM applications rely on complex, multi-step workflows in which requests move through embedding generation, vector retrieval, model inference, and post-processing layers. Distributed tracing helps teams pinpoint bottlenecks, diagnose failures faster, and better understand how each component contributes to overall latency and reliability.
AI systems generate behaviors that traditional telemetry cannot fully capture. Domain-specific signals provide deeper visibility into how LLMs perform in production, helping teams move beyond infrastructure health to understand response quality, reliability, and user impact.
Focus on metrics that directly shape the user experience. Monitoring these indicators helps teams detect slowdowns early, maintain responsiveness, and ensure interactions feel smooth and reliable as workloads scale.
Token accounting underpins cost management. Most LLM providers charge 3-5X more for output tokens than for input tokens. This ratio reflects the computational difference between processing and generating text.
Evaluate how accurate and relevant your model’s responses are.
Safety metrics help teams monitor policy compliance and protect systems from misuse.
The[ OpenTelemetry](https://konghq.com/blog/engineering/opentelemetry-observability-kong-konnect-dynatrace) OpenTelemetry (OTel) community developed standards for AI observability. These conventions[ "establish a standard schema for tracking prompts, model responses, token usage, tool/agent calls, and provider metadata"](https://www.datadoghq.com/blog/llm-otel-semantic-convention/) "establish a standard schema for tracking prompts, model responses, token usage, tool/agent calls, and provider metadata".[8]
The key attributes include:
gen_ai.system: LLM vendor (such as OpenAI, Anthropic)gen_ai.request.model: Specific model requestedgen_ai.usage.input_tokens: Input token countgen_ai.usage.output_tokens: Output token countgen_ai.response.finish_reasons: Why the generation stoppedExample: Python implementation
1from opentelemetry import trace
2from opentelemetry.semconv.gen_ai import GenAIAttributes
3
4tracer = trace.get_tracer(__name__)
5
6with tracer.start_as_current_span("chat.completion") as span:
7 # Set GenAI attributes
8 span.set_attribute(GenAIAttributes.GEN_AI_SYSTEM, "openai")
9 span.set_attribute(GenAIAttributes.GEN_AI_REQUEST_MODEL, "gpt-4")
10 span.set_attribute(GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE, 0.7)
11
12 # Execute LLM call
13 response = llm.complete(prompt)
14
15 # Record usage metrics
16 span.set_attribute(GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS,
17 response.usage.prompt_tokens)
18 span.set_attribute(GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS,
19 response.usage.completion_tokens)This standardized approach ensures portability across observability platforms.
Maintaining financial sustainability requires granular cost-visibility and defining Service Level Objectives (SLOs) to keep spending predictable and aligned with business goals.
Performance metrics help define Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for your system. These targets should be adjusted to reflect the needs and expectations of your specific use case.
**Example TTFT Targets**
Research from Glean shows[ "for every additional input token, the P95 TTFT increases by ~0.24ms"](https://www.glean.com/blog/glean-input-token-llm-latency) "for every additional input token, the P95 TTFT increases by ~0.24ms"
Inter-token latency
Industry benchmarks provide useful guidance, but your targets should ultimately align with your application’s needs. Consider the following illustrative thresholds:
These targets directly influence perceived fluency, as lower latency makes interactions feel smoother and more responsive.
Throughput metrics
Throughput metrics help you understand how much workload your system can handle and how efficiently it processes requests. The key indicators include:
Maintaining financial sustainability requires granular cost-visibility. Organizations often define Service Level Objectives (SLOs) to keep spending predictable and aligned with business goals.
Example token accounting SLOs
The following examples illustrate how teams may set cost targets:
Budget controls
Establishing guardrails helps prevent unexpected cost spikes. The best practices include:
Optimization metrics
Tracking optimization metrics helps teams improve efficiency without sacrificing performance. Typical targets include:
Assessing quality remains essential and often challenging for AI systems. Clear Service Level Objectives (SLOs) help teams define acceptable performance and maintain user trust.
Example factual accuracy SLOs
These aspirational targets can vary widely depending on the model and use case:
Recent benchmarks show meaningful variation in hallucination rates across leading models. As of December 2024, many state-of-the-art systems report rates below 5%. GPT-4o, for example, demonstrates a hallucination rate of approximately 1.5%, while Llama-3.1-405B-Instruct and Anthropic Claude 3.5 Sonnet report rates of 3.9% and 4.6%, respectively. However, even advanced models can exceed 15% hallucination rates when analyzing provided statements, underscoring the importance of evaluating performance within the context of your specific application, according to [AI Hallucination: Compare top LLMs like GPT-5.2 in 2026](https://research.aimultiple.com/ai-hallucination/)AI Hallucination: Compare top LLMs like GPT-5.2 in 2026.
User Experience Metrics
User experience metrics help teams understand how effectively AI meets user needs. While targets should reflect your specific application, the following examples provide useful reference points:
These indicators offer insight into overall satisfaction, usability, and the system’s ability to guide users toward successful outcomes.
Safety metrics are essential for protecting users, maintaining compliance, and reducing organizational risk. Elastic highlights the importance of monitoring policy-based interventions and contextual grounding, with built-in support for tools such as Amazon Bedrock Guardrails, Azure AI Foundry, and Azure OpenAI content filters. Read more [here](https://www.elastic.co/observability/llm-monitoring)here.
Example policy compliance SLOs
The following targets illustrate how organizations often define safety expectations:
Security monitoring
In addition to policy compliance, teams should continuously watch for emerging threats, including:
Proactive monitoring helps teams respond quickly, strengthen defenses, and maintain trust in AI systems.
Effective LLM observability requires a focused strategy to monitor and troubleshoot critical areas like latency, cost, quality, and safety. Here are some common LLM issues and how to diagnose them.
Symptoms:
Latency problems often become apparent when time-to-first-token (TTFT) jumps from milliseconds to several seconds, making responses feel frozen to users.
**Check these metrics:**
Start by reviewing the following.
Together, these signals can help pinpoint where delays are taking place.
**Root causes:**
Symptoms:
Cost issues often become apparent when daily token usage suddenly doubles, and budget alerts trigger earlier than expected. These signs usually indicate that consumption is outpacing projections and deserves immediate investigation.
**Check these metrics:**
Start by reviewing the following.
These metrics provide a clear picture of where tokens are being used inefficiently and can help pinpoint the source of unexpected spending.
**Root causes:**
*Verbose system prompts — *When system prompts are overly detailed, every request carries the full prompt cost, which can quickly inflate overall usage. *Solution*: Minimizing system prompts, enabling prompt caching, and applying compression techniques can significantly reduce token consumption, although results will vary by implementation.
*Infinite loops — *Failed requests can sometimes trigger retry storms, causing usage — and costs — to spike without delivering additional value. *Solution*: Implement exponential backoff, add circuit breakers, and monitor retry patterns to prevent runaway consumption.
*Missing output limits — *If output limits are not properly configured, models may generate the maximum number of tokens even when shorter responses would suffice. *Solution*: Set appropriate max_tokens for each use case and monitor actual versus requested usage to ensure tokens are being used efficiently.
Symptoms:
Quality issues often arise when groundedness scores start declining and users report receiving nonsensical or unreliable answers. These signals typically suggest that the model is drifting away from trusted data or that supporting systems are not performing as expected.
**Check these metrics:**
Start by reviewing the following.
Examining these indicators together can help teams determine whether the issue arises from the model itself, the retrieval layer, or the quality of the supplied context.
**Root causes:**
Symptoms:
Safety concerns often become visible when policy violations spike and users begin reporting inappropriate or harmful content. These patterns typically indicate gaps in filtering, shifting user behavior, or emerging threats that require closer attention.
**Check these metrics:**
Start by reviewing the following.
**Root Causes:**
Symptoms:
Rate-limit issues typically arise when 429 errors occur and response times degrade under heavier load. These signs often indicate that demand is exceeding system capacity or that requests aren’t being managed efficiently.
**Check these metrics:**
Start by reviewing the following.
These metrics can reveal whether constraints are infrastructure-related or due to traffic patterns that should be better controlled.
**Root Causes:**
[Retrieval-augmented generation (RAG)](https://konghq.com/blog/learning-center/what-is-rag-retrieval-augmented-generation)Retrieval-augmented generation (RAG) systems require specialized observability to ensure the retrieval pipeline delivers relevant, high-quality context to the model. Without strong visibility into retrieval performance, even advanced models can produce weak or inaccurate responses.
**Core metrics:**
Several established metrics help teams evaluate how effectively their systems provide relevant information.
Performance targets:
In addition to relevance metrics, monitoring pipeline performance is critical for maintaining responsive user experiences. Teams often track embedding generation time, with targets typically below 50ms, and vector search latency, which commonly aims for under 100ms. It’s also important to watch reranking overhead, as excessive processing at this stage can offset the benefits of fast retrieval.
As EdenAI notes, ["Retrieval performance in LLM observability focuses on evaluating the effectiveness of the retrieval component in RAG systems"](https://www.edenai.co/post/top-5-paid-observability-platforms-for-llms-unlocking-advanced-monitoring-for-ai-systems)"Retrieval performance in LLM observability focuses on evaluating the effectiveness of the retrieval component in RAG systems". Together, they help organizations ensure their retrieval layer supports accurate, timely, and contextually grounded AI responses.
Maintaining high-quality context is essential for reliable AI outputs, particularly in retrieval-augmented generation (RAG) systems. When the context supplied to a model is incomplete, redundant, or unsupported, response quality can quickly decline. Ongoing assessment helps ensure that retrieved information is relevant, trustworthy, and structured in a way the model can use effectively.
Grounding and citation tracking
Monitor whether retrieved documents genuinely support the claims generated in responses. Tracking citation accuracy and coverage helps confirm that answers are rooted in verifiable sources rather than inferred or fabricated information.
Document redundancy
Duplicate content can crowd the context window and reduce the visibility of unique, high-value information. Identify repeated documents, monitor source diversity, and track information density to ensure the model receives a balanced and comprehensive set of inputs.
Context window utilization
Measure how much of the available context window is actually being used and look for patterns where important information is ignored. Optimizing document ordering based on relevance can improve comprehension and help the model prioritize the most useful material.
**Hallucination detection**
Compare generated statements against source documents and flag unsupported claims in real time. Early detection reduces the risk of misleading responses reaching users.
Citation accuracy
Verify that cited sources truly contain the information being referenced and monitor formatting for consistency. Strong citation practices reinforce trust and make responses easier to validate.
The [OWASP Top 10 for LLM Applications](https://konghq.com/blog/engineering/owasp-top-10-ai-and-llm-guide)OWASP Top 10 for LLM Applications highlights some of the most critical security risks organizations face when deploying large language models in production. Understanding these threats — and actively monitoring for them — helps teams strengthen defenses and reduce exposure to emerging attack patterns.
**Monitor the following:**
Fiddler AI emphasizes[ "LLMOps tackles the unique risks of deploying large language models in production—such as hallucinations, prompt injections, jailbreaks"](https://www.fiddler.ai/llmops) "LLMOps tackles the unique risks of deploying large language models in production—such as hallucinations, prompt injections, jailbreaks". Proactive monitoring allows organizations to detect these threats early and maintain safer, more resilient AI systems.
Strong governance frameworks help organizations manage risk, maintain compliance, and build trust in AI systems. Adopting established standards provides structure for responsible deployment while ensuring that oversight keeps pace with rapid technological change.
**NIST AI risk management framework**
The NIST AI Risk Management Framework encourages organizations to track fairness metrics, maintain transparency logs, and document risk assessments as part of a disciplined approach to AI oversight. These practices promote accountability and help teams identify potential issues before they escalate. The [NIST AI RMF](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10)NIST AI RMF offers comprehensive guidance for implementing these controls.
**ISO/IEC 42001:2023**
Recognized as the[ world's first AI management system standard](https://www.iso.org/standard/42001) world's first AI management system standard, ISO/IEC 42001:2023 establishes formal requirements for governing AI responsibly. Organizations are expected to maintain complete audit trails, document risks, validate system performance, and keep detailed stakeholder communication logs. These measures support operational transparency and reinforce regulatory readiness.
Protecting sensitive data is a foundational requirement for responsible AI operations. Organizations should take a proactive approach to safeguarding information throughout its lifecycle, ensuring that security practices keep pace with growing data usage.
PII redaction
Automatically detect and mask personally identifiable information (PII) to reduce the risk of exposure. Tokenization can add another layer of protection by replacing sensitive references with secure placeholders while preserving analytical value.
Retention policies
Clear retention policies help prevent unnecessary data storage and reduce compliance risk. Define data lifecycles, implement automatic expiration, and continuously monitor adherence to ensure information is retained only as long as needed.
Prompt logging
Prompt logs support observability and troubleshooting, but they must be managed carefully to protect user privacy. Balance visibility with discretion by using sampling strategies and applying anonymization techniques wherever possible.
The first phase focuses on establishing foundational telemetry so teams can quickly gain visibility into system behavior. Prioritizing these early improvements creates a strong operational baseline and makes it easier to scale observability as adoption grows.
Instrument LLM calls
Start by adding OpenTelemetry to all endpoints to ensure consistent tracing across requests. Capture key attributes such as the model being used, prompt length, and response length to build a reliable dataset for performance and cost analysis.
1from opentelemetry.instrumentation.openai import OpenAIInstrumentor
2
3# Auto-instrument OpenAI calls
4OpenAIInstrumentor().instrument()
5
6# Create custom metrics
7meter = metrics.get_meter(__name__)
8token_counter = meter.create_counter(
9 "llm.tokens.total",
10 unit="tokens"
11)Core metrics setup
Implement essential monitoring capabilities early. Configure time to first token (TTFT) tracking, enable token counting, and create basic dashboards so teams can quickly identify trends and detect anomalies.
Log aggregation
Standardize the logging format to simplify analysis and improve cross-system visibility. Implement PII detection to reduce the risk of sensitive data exposure, and set up centralized log collection so operational insights are accessible from a single location.
In this phase, the focus shifts from foundational visibility to deeper operational insight. Expanding your observability capabilities helps teams better understand system behavior, improve reliability, and manage risk as AI usage scales.
**Quality monitoring**
Strengthen quality oversight by implementing groundedness scoring to evaluate how well responses align with trusted sources. Add user feedback loops to capture real-world performance signals, and deploy A/B testing to compare model behavior and guide optimization decisions. As Datadog notes, their platform ["now natively supports OpenTelemetry GenAI Semantic Conventions, allowing you to instrument your LLM applications once with OTel"](https://www.datadoghq.com/blog/llm-otel-semantic-convention/)"now natively supports OpenTelemetry GenAI Semantic Conventions, allowing you to instrument your LLM applications once with OTel".
**Safety and compliance**
Enhance governance by deploying violation tracking to identify policy breaches early. Implement audit logging to support accountability and regulatory readiness, and add prompt injection detection to defend against manipulation attempts.
**Dashboards and alerts**
Translate insights into action by building SLO-driven dashboards that highlight system health and performance. Implement anomaly detection to catch unusual patterns promptly, and create team-specific dashboards so stakeholders can focus on the metrics most relevant to their roles.
By this stage, the goal is to focus on observability maturity through automation and continuous optimization. Advanced capabilities help teams respond faster to issues, improve system resilience, and support long-term scalability.
**RAG pipeline instrumentation**
Deepen visibility into the retrieval pipeline by adding retrieval-specific metrics, tracking how effectively the context window is utilized, and monitoring reranking performance. These insights help ensure the system consistently delivers relevant, high-quality information to the model.
**Anomaly detection**
Deploy machine learning–based detection to identify unusual patterns before they escalate into larger problems. Pair this with root cause analysis to accelerate troubleshooting, and build self-healing capabilities so certain issues can be resolved automatically without manual intervention.
**Continuous evaluation**
Establish a culture of ongoing improvement by creating regression tests that catch performance drift early. Deploy canary releases to validate changes in controlled environments, and build feedback loops that continuously inform tuning and optimization efforts.
With a vendor-neutral approach, organizations can stay flexible as technologies evolve. By avoiding tight dependencies on a single provider, teams can adapt more easily to changing requirements, control costs, and adopt new capabilities without major rework.
As organizations continue to operationalize AI in 2026, observability is evolving from a technical capability into a strategic requirement. Several emerging trends are shaping how teams monitor performance, control risk, and scale AI responsibly.
These trends signal a broader shift: AI observability is no longer optional infrastructure; it’s quickly becoming a core pillar of production-ready AI.
To illustrate the transformative power of AI Observability, consider the experience of Writer, an enterprise-focused generative AI platform. When Writer set out to launch their AI Studio—a suite designed to streamline the creation of generative AI applications—they faced the daunting task of r[evamping their API infrastructure to accommodate public access](https://konghq.com/blog/enterprise/writer-customer-story)evamping their API infrastructure to accommodate public access.
Their challenge was multifaceted: they needed to implement a public Gate API capable of handling rate limits, security requirements, and seamless integration with existing internal platforms. This was where Kong Konnect came into play, offering a robust solution that not only met these requirements but also enhanced their API management strategy.
Jack Viers, a Backend Developer at Writer, shared insights into how [Kong Konnect's fully managed services ](https://konghq.com/products/kong-konnect)Kong Konnect's fully managed services streamlined their deployment process. The integration enabled Writer to maintain public availability while ensuring security and flexibility through customizable plugins like KeyAuth and ACL, which catered to their fine-grained capability needs. The ability to abstract and simplify their underlying platforms through Kong's intuitive interface proved invaluable, allowing Writer to focus on delivering a top-tier user experience.
The results were significant. Writer achieved a seamless deployment with minimal operational costs and enhanced security, crucial for the success of AI Studio. Viers emphasized the importance of Kong's comprehensive support, which enabled Writer to swiftly address integration challenges, ensuring a smooth rollout of their new product.
This customer story underscores the critical role of AI Observability and specialized tools like AI Gateways in managing complex LLM deployments. It exemplifies how leveraging these technologies can transform potential obstacles into opportunities, driving innovation while maintaining reliability and security. Writer's story is a testament to the strategic advantage of embracing AI Observability, not just to meet current demands, but to future-proof AI initiatives in an ever-evolving technological landscape.
Traditional monitoring tells you whether your system is running. AI observability tells you whether it is running **correctly**. As organizations move from experimentation to production, this distinction becomes critical.
The complexity of large language models (LLMs) demands a new operational mindset. Non-deterministic outputs, fluctuating response quality, and evolving safety risks require specialized monitoring that goes far beyond infrastructure health. Without comprehensive observability, teams lack the visibility needed to make informed decisions and operating AI without that visibility is essentially flying blind.
Organizations deploying LLMs need observability strategies now, not later. In many cases, the difference between simply having AI capabilities and operating production-ready AI comes down to observability maturity.
**Take the following steps now:**
**Scale systematically**
Follow a phased implementation roadmap, invest in team training, and build alignment across engineering, operations, security, and leadership. Adopting emerging standards will further strengthen your foundation and help future-proof your architecture.
Hidden complexity has a way of becoming tomorrow’s crisis if left unaddressed. Implementing robust AI observability today gives organizations the confidence to innovate at scale while maintaining safety, quality, and cost control.
Ready to transform your LLM observability strategy? Explore how Kong’s [AI Gateway](https://konghq.com/products/kong-ai-gateway)AI Gateway delivers rate limiting, analytics, and centralized observability to support AI workloads at scale.
The Gateway Evolution An unoptimized AI inference endpoint can burn through thousands of dollars in minutes. This isn't hyperbole. It's the new reality of artificial intelligence operations. When GPT-4 processes thousands of tokens per request, tradi
[](https://konghq.com/blog/learning-center/api-gateway-vs--ai-gateway)
Kong Gateway is an API gateway and a core component of the Kong Konnect platform . Built on a plugin-based extensibility model, it centralizes essential functions such as proxying, routing, load balancing, and health checking, efficiently manag
[](https://konghq.com/blog/engineering/ai-voice-agents-kong-ai-gateway-cerebras)
AI governance establishes the principles, roles, processes, and controls for responsible AI deployment. It transforms abstract ethics into concrete practices. Think of AI governance as a rulebook for how to use AI in a secure, ethical, observable,
[](https://konghq.com/blog/learning-center/what-is-ai-governance)
Why AI guardrails matter It's natural to consider the necessity of guardrails for your sophisticated AI implementations. The truth is, much like any powerful technology, AI requires a set of protective measures to ensure its reliability and integrit
[](https://konghq.com/blog/engineering/ai-guardrails)
Introduction to OWASP Top 10 for LLM Applications 2025 The OWASP Top 10 for LLM Applications 2025 represents a significant evolution in AI security guidance, reflecting the rapid maturation of enterprise AI deployments over the past year. The key up
[](https://konghq.com/blog/engineering/owasp-top-10-ai-and-llm-guide)
As businesses increasingly harness the power of artificial intelligence (AI) and large language models (LLMs), a new challenge emerges: managing the deluge of AI requests flooding systems. This exponential growth in AI traffic creates what could be
[](https://konghq.com/blog/enterprise/how-to-master-aillm-traffic-management-with-intelligent-gateways)
The explosion of AI-native applications is upon us. With each new week, massive innovations are being made in how AI-centric applications are being built. There are a variety of tools developers need to consider, be it supplying live contextual data
[](https://konghq.com/blog/product-releases/securing-observing-governing-mcp-servers-with-ai-gateway)