Portkey Named a Cool Vendor in the 2025 Gartner® Cool Vendors™ in LLM Observability Report

As AI adoption moves from experiments to production, observability has become a core part of running reliable systems. Teams now need to know exactly what’s happening under the hood, how models behave, how prompts evolve, and how agents make decisions in real time.

Modern AI applications aren’t simple API calls anymore. They include multi-step agents, retrieval pipelines, and dynamic context flows across multiple models and tools. Each layer introduces its own points of failure, drift, and cost variation. Without visibility, teams risk losing control over performance, safety, and spend.

That’s why AI observability has become indispensable. It helps teams trace every model call, monitor agent behavior, understand response quality, and enforce guardrails across complex pipelines.

In this guide, we’ll break down what makes AI observability different from traditional monitoring, what capabilities matter most in a tool, and why Portkey stands out among the best — recognized in 2025 Gartner® Cool Vendors™ in LLM Observability

Why AI observability needs to evolve beyond traditional app monitoring

Traditional observability was built for predictable systems, applications that behave the same way when given the same inputs. Metrics, logs, and traces were enough to tell you whether a system was up, slow, or failing.

AI systems, especially LLM-powered agents, don’t work that way. Their behavior changes based on prompts, retrieved context, tool outputs, and prior reasoning steps. The same query can trigger different results, tool calls, or even reasoning chains.

Failures here aren’t HTTP 500s. They’re hallucinations, poor reasoning paths, or policy breaches that traditional monitoring can’t detect. And as agentic workflows chain together multiple model calls and tools, the complexity multiplies. You’re no longer observing a single endpoint, but an evolving network of autonomous decisions.

To manage this, AI observability must go beyond logs and latency. It needs to capture behavioral traces, decision paths, and guardrail outcomes — giving teams a way to audit what the model did, why it did it, and whether it aligned with expectations.

The new dimensions of AI observability

Modern AI systems operate across multiple layers — models, agents, tools, vector stores, and protocols like MCP, all interacting dynamically. Observability now needs to capture not just what happened, but how and why it happened.

Here are the new dimensions that define AI observability today:

• Prompt and context tracing: Track every input, retrieved context, and system message that shaped a model’s response.
• Model performance analytics: Measure latency, cost, token usage, and output quality across different models and providers.
• Tool and MCP call visibility: Log every function call, external API interaction, and MCP request made by models or agents including parameters, response times, and outcomes.
• Decision flow tracking for agents: Visualize how an agent planned, selected tools, and reasoned through multiple steps. Observability here helps distinguish between logic errors, tool failures, and model drift.
• Guardrail and policy monitoring: Record when outputs fail moderation or compliance checks, so teams can trace violations back to the originating step or call.
• Cost and usage visibility: Break down spend by user, model, tool, and provider to keep budgets under control and surface inefficiencies early.
• Cross-model routing insights: Understand how traffic is distributed across models (GPT-5, Claude 4.5, Gemini 1.5, etc.) and how latency, accuracy, and reliability differ.
• Data governance and privacy tracking: Detect when sensitive information is used or exposed in prompts, tool calls, or responses.

AI observability has expanded from metrics and traces to full-fidelity behavioral visibility across models, agents, and protocols, enabling teams to trust what their AI systems are doing in production.

What to look for in an AI observability tool

AI observability spans multiple layers — from prompts to agents, tools, and MCP calls. The right platform should bring all of that together while remaining scalable, interoperable, and governance-ready.

While you look for everything above i.e., prompt tracing, agent reasoning, tool and MCP visibility, guardrail checks, and cost analytics, make sure the platform also delivers on these fundamentals:

• Unified visibility across providers: A single pane of glass for OpenAI, Anthropic, Mistral, Gemini, and any in-house or open-weight model.
• Custom metrics for model behavior: Support for defining and tracking behavioral KPIs like coherence, moderation flags, or output confidence.
• Built-in cost governance: Rate limits, budgets, and spend tracking across users and teams, not just models.
• Scalable architecture: Low-latency ingestion of millions of events, deployable on cloud or on-prem setups.
• Open standards integration: Native support for OpenTelemetry (OTel) and compatibility with other tools.
• Role-based access and audit trails: Workspace-level separation and compliance-friendly visibility for enterprise environments.

Why Portkey stands out

Recognized in 2025 Gartner® Cool Vendors™ in LLM Observability, Portkey takes a distinct approach as it goes beyond just monitoring models and observes the entire AI gateway that connects them.

Unlike standalone observability tools, Portkey provides visibility above the gateway. This means every request whether from a user, agent, or MCP client, can be traced back to its workspace, API key, configuration, and team. You can see exactly who made the request, through which model, and under what settings. That level of attribution makes cost, performance, and compliance tracking effortless at scale.

Portkey also supports all major agent frameworks, providing full observability into tool use, reasoning chains, and decision paths. And with its MCP Gateway, it extends the same visibility and governance to MCP calls and servers, allowing organizations to monitor and control every tool interaction that happens through the protocol.

By combining observability, governance, and control within one AI Gateway, Portkey gives enterprises a single source of truth for everything their AI systems do and the guardrails to ensure it’s done responsibly.

Conclusion

As AI systems evolve into complex networks of models, agents, and MCP servers, observability isn’t optional — it’s how teams ensure performance, reliability, and trust.

With Portkey providing a complete stack to take AI apps and agents to production including observability, governance, and control teams can confidently ship to production and maintain visibility at every layer.

Book a demo to see how Portkey helps enterprises operate AI safely and at scale.