AI Deployment, Observability & Evaluation

See how teams are getting models from lab to production — and keeping them performant and accountable once live. Here you’ll find workflows, tools, and frameworks enabling responsible deployment.

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Sage and vendor perspectives on LLMOps, model drift, continuous evaluation, and scalable serving architectures.

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Trending Products

The most endorsed AI deployment, observability, and evaluation solutions on Sagetap, backed by real-world validation from enterprise teams.

Meet Anonymously

Straiker offers AI-native security solutions specifically designed for agentic AI applications. It addresses the challenges that arise from the rapid creation of these applications, such as over-permissioned agents and unpredictable behaviors. With a focus on speed and accuracy, Straiker observes, isolates, and neutralizes malicious activities in real-time, ensuring that a single compromised agent does not compromise the entire system. The product boasts 99% detection accuracy by analyzing behavioral signals from various layers of AI technology, providing robust security measures across the entire application stack. Through tools like Ascend AI for vulnerability assessment and Defend AI for real-time protection, Straiker empowers users to build and scale their AI applications with confidence.

Sagetap - Stealth

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At Sagetap, we are transforming how technology solutions are discovered and evaluated. Our platform is crafted to help tech Vendors connect with leaders in the tech industry, or Sages, effortlessly and efficiently. Our platform exists to facilitate meetings between these tech sellers and tech buyers in a way that ensures buyer anonymity is always protected, preventing any spam or unwanted follow-ups.

SwarmOne AI NeoOrchestrator

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SwarmOne is an inference orchestration platform built for heterogeneous silicon- NVIDIA, AMD, Intel, and Groq - delivering the lowest cost per million tokens through dynamic prefill/decode disaggregation, SLO-driven autoscaling, and heterogeneous GPU packing. The result: 30% utilization becomes 90%+, TCO drops, and production inference finally runs the way it should.

Fiddler AI Observability & Security Platform

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Fiddler helps enterprises monitor, conduct root cause analysis, improve, and secure ML, LLM, and GenAI models and applications. * Unified platform for ML, LLM, GenAI, and agentic systems * Custom metrics and dashboards that provide visibility and a holistic view into the performance of how AI systems contribute to organizational KPIs * Fiddler Trust Service which comprises purpose-built, fine tuned models that score LLM prompts and responses at a 6x less expensive rate (compared to LLM as a judge) and are scalable * Fiddler Guardrails that offer <100ms latency and can run in cloud or VPC environments * Available natively in AWS SageMaker * Integrations with NVIDIA NeMO Guardrails and NVIDIA NIM

Highflame AI Security Fabric

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Highflame is an enterprise-grade AI security platform built for the age of autonomous agents — providing context-aware guardrails, real-time observability, and unified governance across every AI interaction. Unlike traditional defenses that treat prompts in isolation, Highflame interprets multi-turn context and intent to enforce policies continuously throughout agent workflows, preventing gradual exploit chains, drift, and unsafe actions. At its core, Highflame delivers: • Context-Aware Guardrails: Intent-aware policies that adapt to conversation state and evolving behavior. • Unified Policy Engine: A high-performance policy language with sub-millisecond enforcement across models, agents, tools, and data flows. • Full Interaction Visibility: Live mapping of agents, models, and tools into an Agentic Context Graph for debugging, auditability, and risk insight. • Adaptive Runtime Security: Dynamic enforcement that blocks unsafe content, risky tool calls, data leakage, and malware at the moment it occurs. • MCP Hardening & Tool Protection: Scanning and verifying Model Context Protocol configurations to eliminate downstream risk. • Enterprise-Ready Governance: Continuous audit trails, telemetry, compliance alignment, and dashboards that turn regulation into always-on assurance, not a checkbox. Highflame enables organizations to build, secure, and scale AI systems with confidence, delivering deep visibility, continuous governance, and context-rich defenses across the full lifecycle of agentic workflows.

Highflame Autonomous Agent Testing

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Highflame Red is an autonomous, continuous AI red-teaming engine designed to stress-test modern LLM applications and agentic workflows at scale. Unlike traditional, point-in-time testing, Red uses swarms of specialized adversarial AI agents that simulate realistic attacker behavior — probing, adapting, and escalating across multi-turn interactions — to uncover vulnerabilities static scans and manual exercises miss. With research-based attack engines, dynamic test generation, and a massive arsenal of curated exploits covering prompt manipulation, data leakage, context drift, model robustness, and unsafe tool use, Highflame Red continuously adapts as your stack evolves. It not only reveals hidden risks but also feeds precise mitigation steps back into runtime guardrails and policy controls to harden defenses automatically. Built for enterprise-grade resilience, Highflame Red delivers: • Autonomous adversarial testing that mirrors real-world threat tactics • Continuous risk discovery across multi-turn, multi-agent scenarios • Guardrail recommendations tailored to your models, tools, and workflows • Resilience scoring & reporting to track posture improvements over time • CI/CD integration for automated testing during development and deployment cycles. By turning red teaming into a continuous learning loop, Highflame Red ensures that defenses evolve alongside AI threats — so teams can find and fix weaknesses before they are exploited.

Recent Initiatives

Peer-driven AI deployment, observability, and evaluation projects in motion, with direct access to the Sage leading each initiative.

Gathering Requirements

Last Modified: May 04 '26

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Financial Governance for AI/LLM Token Consumption

To identify and evaluate tools, frameworks, and accelerators for financial governance of AI/LLM token consumption. Also expanded to include Agentic AI observability for tracking multi-step agent workflows, tool calls, and decision chains.

Akka Agentic AI Platform

Akka Agentic AI Platform

Secuvy.ai | AI Governance

Secuvy.ai | AI Governance

HoneyHive

Highflame AI Security Fabric

Highflame AI Security Fabric

Consumer Goods

Last Modified: May 04 '26

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Training

Training Platform for internal and external customers to adopt new technologies, products, and enhance awareness across the multiple disciplines of the organization. The ability to provide AI centric training content is a requirement to reduce manual content creation

LearnUp AI

Automotive

Last Modified: Feb 06 '26

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Ai solution discovery and deployment

Explore various Ai solutions for right fit deployments. We currently have a MS fabric/PowerBI environment and a mature, but continuing to mature analytics platform. We are now looking for Ai solutions to work with structured and/or unstructured data

Cequence.AI

Unstract

Construction

Last Modified: Jan 25 '26

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Enterprise Data Abstraction & Legacy Modernization

We are looking to break the "POC cycle" and implement a production-grade AI solution that unlocks value from our massive, disconnected legacy data estates (Petabytes of historical R&D logs, legacy ERP data). Currently, our progress is stalled because our data engineering resources are consumed by manual pipeline maintenance and "firefighting" data quality issues, leaving little time for high-value AI modeling. We need to abstract this complexity to build tailored AI agents that can reason across our disparate internal systems. We are building a composable AI stack and are evaluating partners for the following specific workstreams. We are open to "best-of-breed" point solutions for individual layers: Layer 1: Data Abstraction & Automation: - Automated Engineering Overhead: We require a solution that automates low-value tasks like pipeline building, testing, and schema mapping to free our engineers from manual ETL work. - Deep Data Abstraction: Capabilities to extract and structure dark data from complex, non-standard legacy formats and make it usable for AI agents. Layer 2: Security & Governance: - Agentic API Governance: We need a security layer that sits between our AI Agents and our internal APIs. We require the ability to discover which APIs agents are calling and enforce policy guardrails to prevent unauthorized or destructive actions. - Agent Configuration Governance (AI-SPM): We need a "source of truth" for our agent configurations. We require the ability to version-control system prompts, monitor for configuration drift, and audit changes to tool permissions to ensure our agents remain compliant over time.

DataFramer

42Crunch API Security Platform

42Crunch API Security Platform

Archil

Vero Security

Consumer Electronics

Consumer Electronics

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