WrangleAI Alternatives

BAND builds enterprise-grade interaction infrastructure for distributed AI agents. Its platform enables real-time, multi-peer collaboration across agents and humans, while providing a runtime control plane that enforces policy, authority boundaries, and visibility across heterogeneous systems. BAND supports developers, engineering teams, and enterprise platform leaders operating multi-agent ecosystems across internal systems, SaaS platforms, and partner environments.

FinOpsly is an AI Cost Governance platform. It brings AI, cloud, data platform and SaaS spend into one attribution, policy and control layer, so enterprises can price a workload before building it, attribute every dollar to an owner, hold spend inside budget under policy, and prove what landed in run-rate. Your AI invoice is not what your AI costs. One request draws on model tokens, retrieval, warehouse queries, GPU capacity and storage, and only the first shows up on the AI bill. FinOpsly resolves all of it, plus the seats in procurement and the compute in an untagged cloud account, to the same dimensions: owner, team, application, line of business, customer and tenant. An AI initiative's full cost becomes one figure, charged back through one hierarchy in one cycle. Workforce AI is the tools employees use: seats and per-user token draw across GitHub Copilot, Cursor, ChatGPT Enterprise and Microsoft 365 Copilot. Application AI is the AI your product ships: tokens, compute and data joined into cost-to-serve across OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex AI, SageMaker and Databricks. PLAN. Price a workload from its architecture before any resource exists, across model APIs, GPU capacity, data platform consumption and storage, with assumptions visible. Compare it across candidate models on your measured usage. EXPLAIN. Attribute spend to owner, team, application, line of business and business unit across 9+ hierarchy levels. Unified tagging reconciles providers that tag inconsistently, and AI-driven bulk labeling closes large key estates. Unattributed spend is reported in dollars. ACT. Budgets per project, team and API key, with daily burn-rate monitoring. Anomaly detection with root cause, routed to the owner. Waste detection using FinOpsly's own algorithms and ML models. Commitment planning across AWS, Azure and Google Cloud. Policy-driven parking of idle compute. PROVE. Chargeback across AI, cloud, data and SaaS in one cycle. Realized savings tracked into run-rate against a no-action baseline. Cost per call, cost per active user, and cost-to-serve per customer and tenant. proof: 100% attribution of AI spend; chargeback from 12.4 days to under one day across 9+ levels; 26% realized savings in AWS and 17%+ in Azure at a payments client. Built for CIOs, CTOs and platform leaders accountable for technology spend, FinOps and finance teams running chargeback, and engineering teams who need cost signal before they decide

MuleSoft is an agentic control plane designed to help enterprises govern, orchestrate, and secure AI agents, APIs, applications, models, and data across complex digital environments. The platform supports multi-agent governance, API management, integration, automation, and gateway federation from one unified control plane. With solutions such as MuleSoft Agent Fabric, MuleSoft Omni Gateway, Agent Registry, Agent Scanners, and Agent Broker, organizations can discover agents, manage interactions, reduce shadow AI, and coordinate workflows across ecosystems. MuleSoft also helps teams turn existing APIs and applications into governed tools that AI agents can safely discover and use. Its platform supports developers and business users with natural language development, prebuilt connectors, monitoring, API governance, and integration tools. MuleSoft is built to help enterprises scale AI adoption with stronger compliance, observability, security, and operational confidence.

Lunar.dev is an AI gateway and API consumption management platform that gives engineering teams a single, unified control plane to monitor, govern, secure, and optimize all outbound API and AI agent traffic, including calls to large language models, Model Context Protocol tools, and third-party services, across distributed applications and workflows. It provides real-time visibility into usage, latency, errors, and costs so teams can observe every model, API, and agent interaction live, and apply policy enforcement such as role-based access control, rate limiting, quotas, and cost guards to maintain security and compliance while preventing overuse or unexpected bills. Lunar.dev's AI Gateway centralizes control of outbound API traffic with identity-aware routing, traffic inspection, data redaction, and governance, while its MCPX gateway consolidates multiple MCP servers under one secure endpoint with full observability and permission management for AI tools.

Preloop is the open source AI agent control plane for agents that take real actions. It combines an MCP firewall for tool access, an AI model gateway for cost, safety, and attribution, policy-as-code with human approvals, runtime session observability, and audit trails in a single self-hostable platform. AI agents can deploy code, change infrastructure, move money, touch production data, and burn model spend in seconds, so Preloop helps teams control what agents can do, how much they spend, and which actions require human approval. It works with OpenClaw, Hermes, Claude Code, Codex CLI, Cursor, Gemini CLI, Windsurf, Cline, OpenCode, and any MCP-compatible agent or managed runtime. Access rules can inspect arguments and context, not just tool names, with CEL expressions for fine-grained conditions. Teams can start with observability, then layer in approvals and deny rules without SDKs or invasive app changes.

Cloptima is an AI and cloud FinOps platform that brings LLM spend governance, multicloud cost intelligence, Kubernetes optimization, query analysis, and engineering cost controls into one operating model. Its AI gateway lets teams use their own OpenAI, Anthropic, Gemini, Vertex AI, and Amazon Bedrock credentials behind encrypted controls, then apply virtual keys, model policies, token limits, budgets, guardrails, and attribution before calls reach providers. Spend analytics break down usage by provider, model, team, application, environment, user, agent session, tool, workflow, and dimensions, while agent controls track retries, loops, tool calls, and runaway-cost risk. Exact and semantic response caching can reduce repeated usage, and intelligent routing can shift eligible traffic to cheaper or faster models with canary rollout and rollback if quality, latency, or errors regress.

Singulr is an enterprise AI governance and security platform that provides a unified control plane to help organizations discover, secure, and optimize AI adoption at scale. It addresses the growing gap between rapid AI usage and limited governance by delivering complete visibility into all AI systems in use, including homegrown applications, embedded AI, public tools, and shadow AI that often remains invisible to security teams. It continuously discovers and inventories AI assets across the organization, creating a real-time map of agents, models, and services, while assessing their risk through contextual analysis of data handling, model lineage, vulnerabilities, and compliance implications. Through its Singulr Pulse intelligence layer, it evaluates millions of AI systems, assigns risk scores, and supports automated onboarding workflows that reduce approval cycles from weeks to hours without compromising security.

Klique is an enterprise AI control plane that centralizes model routing, AI service governance, and compute orchestration across on-premises, cloud, and hybrid environments. The platform routes AI requests to appropriate models based on factors such as cost, latency, policy, and data sensitivity while also directing workloads to suitable infrastructure. It provides centralized controls for budgets, quotas, virtual keys, single sign-on, audit trails, and access policies across users, agents, models, projects, and tools. Klique can manage in-house models, open-source models, third-party APIs, GPU clusters, CPUs, Kubernetes environments, and cloud AI services through a unified layer. Its orchestration capabilities support shared compute pools, fractional GPU usage, priority scheduling, training jobs, data processing, and live model deployments.

FinOps LLM is an AI cost management and LLM observability platform for engineering teams running production GenAI. It makes token spend visible across OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Azure, Groq, and other providers and reconciles internal usage data against provider invoices. Token-level costs can be filtered by provider, model, feature, team, customer, environment, and custom dimensions, giving every dollar a clear owner. Attribution and chargeback tools map usage to product surfaces and customer cohorts, support showback, and export data to NetSuite, QuickBooks, CSV, or APIs. Real-time anomaly detection monitors spend, latency, and quality against rolling feature baselines, sending alerts through Slack, PagerDuty, email, or webhooks when behavior changes. Optional budget enforcement and auto-throttling can stop runaway agents, retries, or model shifts before they become expensive.

JetStream Security is a security-first AI governance platform designed to give enterprises full visibility, control, and accountability over their AI systems by turning them from opaque, fragmented tools into managed, traceable infrastructure. It acts as a centralized control plane that connects identity, runtime governance, observability, and financial oversight into a single system, allowing organizations to “see every AI action, tie actions to accountable owners, [and] keep workflows inside approved boundaries” while enforcing policy at runtime. It introduces agentic identity, binding human, agentic, and non-human identities to specific actions and access permissions, ensuring every invocation, tool call, or workflow can be traced and governed through least-privilege access principles. Through continuous runtime governance, JetStream compares live AI behavior against approved blueprints, using immutable logging and real-time observability to detect drift.

Onyx is a secure AI control plane for discovering, protecting, governing, optimizing, and measuring AI agents and models across the enterprise. It gives security, governance, and AI teams visibility into sanctioned and shadow AI across SaaS, cloud, endpoints, and code, including prompts, responses, and agent actions. AI Security helps strengthen posture, identify vulnerabilities, and enforce real-time safeguards against threats and misuse, while AI Governance supports security standards and regulatory requirements with opt-in coverage and policy controls defined in natural language. AI Orchestration reduces friction when setting up agents and MCPs and helps optimize for cost, accuracy, and latency. AI ROI measures adoption, sets goals, and tracks outcomes across departments. The Onyx Guardian Agent acts as a supervisory AI that continuously identifies risks and remediates issues across the platform, helping organizations manage large numbers of agents at scale.

Ensure AI use adheres to corporate policy with our simple-to-implement AI governance control plane. Remove complexity, gain visibility, and securely increase AI adoption, with SurePath AI. Native integrations to your existing security solutions, private models, and enterprise data sources. SSO, SCIM, and SIEM are natively supported. Detect AI use at a network level. Control access and inspect requests for sensitive data leaks. Redact sensitive data found in requests to public models. In-line modification of requests enables productivity while mitigating risk. Redirect traffic to your private AI models. Leverage SurePath AI's private model access controls as your own internally branded enterprise AI portal. Policy-based controls enrich requests with only the enterprise data users are granted access to, giving meaningful responses based on relevant business context. Users' prompts are automatically enhanced to align output to enterprise objectives.

LLMetrics is LLM cost tracking software for teams shipping AI products, bringing model spend, token usage, feature attribution, and usage alerts into one live dashboard. It supports more than 100 models across OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Together AI, Groq, and other providers, with pricing data synchronized daily. Teams tag each model call with a feature name, provider, model, input tokens, and output tokens, allowing them to see exactly whether a chatbot, summarizer, search feature, lesson generator, or other workflow is driving spend. Real-time updates and daily trend charts reveal how costs change after releases, prompt edits, traffic growth, or model swaps. Spend thresholds and spike-detection rules can alert teams through email or Slack when usage patterns look wrong, helping them catch runaway loops and unexpected cost increases before the provider invoice arrives.

Barndoor is a data and access management layer designed to secure how artificial intelligence systems interact with enterprise data and infrastructure. It acts as a centralized control plane that governs AI agents and applications, allowing organizations to define policies, enforce access rules automatically, and maintain full visibility over how AI tools operate across business systems. Instead of relying only on traditional identity-based permissions, Barndoor introduces context-aware governance, enabling administrators to control what actions an AI agent can perform based on factors such as the user operating the agent, the system being accessed, the type of data involved, and the specific task being attempted. It evaluates every AI request in real time and enforces policies before an action is executed, preventing unsafe or unauthorized operations from reaching internal systems or modifying sensitive information.

SuperBased is a local-first control plane for AI coding agents that lets developers see, control, and right-size agent activity from one binary running on their own machine. It reads native session data from 40 coding tools without requiring a proxy, SDK rewrite, or special configuration, supporting agents such as Claude Code, Codex, Cursor, GitHub Copilot, OpenCode, Gemini CLI, Kilo Code, Qwen Code, Aider, Devin, and others. The dashboard tracks provider-reported token usage, cache reads and writes, costs, sessions, and projected next-message spend across tools that normally keep their data separate. Developers can also launch more than 20 CLI agents as terminal sessions, monitor several repositories from one screen, attach to a running agent, take over the keyboard, and hand control back when needed. Model routing helps teams match tasks to appropriate models, while egress gates can hold commands before execution so users can stop or redirect costly or risky actions.

Maetra is an AI governance and compliance control plane for teams operating tool-using AI agents. Discover inventories agents and capabilities; Comply maps systems to applicable frameworks and keeps reusable evidence current; Govern evaluates consequential actions against versioned policies and routes human approval when required. Secure scans prompts, messages, model outputs, and tool calls for prompt injection, data exposure, unsafe actions, and policy violations. Task Guard detects task drift, scope changes, and mismatched effects. Interaction Guard protects supported browser-AI prompts and files, while Audit preserves linked decision, approval, runtime, and change evidence. Teams can adopt modules separately or together through the web app, REST APIs, SDKs, and MCP. A 14-day no-card trial is available, with paid plans from $20/month.

Domino is an enterprise AI platform designed to help organizations build, deploy, and scale AI systems that deliver real business outcomes. It provides end-to-end support for the AI lifecycle, from data science experimentation to production deployment and governance. The platform enables teams to access data, tools, and compute resources through a self-service environment with built-in IT controls. Domino supports the development of machine learning models, generative AI applications, and AI agents using preferred tools and frameworks. It also includes governance features such as model tracking, audit trails, and policy enforcement to ensure compliance and transparency. With hybrid and multi-cloud capabilities, organizations can run AI workloads across on-premises and cloud environments. Overall, Domino helps enterprises operationalize AI at scale while maintaining control, security, and efficiency.

AICostGuardian is an enterprise AI cost management platform that helps organizations track, optimize, and control spending across 25+ AI providers from one unified dashboard. It monitors every API call with millisecond precision, calculates cost instantly, and combines provider usage into cross-platform analytics, automated reports, forecasts, and dashboards. Teams can analyze spending trends, compare usage, identify optimization opportunities, and use machine-learning insights and smart recommendations to reduce unnecessary AI expenses. Predictive alerts and anomaly detection warn users about unusual usage spikes and approaching budget overruns, while configurable spending limits help keep consumption under control. Department-level cost allocation, team analytics, granular permissions, and role-based access make it easier to understand ownership and govern AI use across an organization.

Burnwise is an AI cost copilot that shows where an organization’s AI budget goes, why spending changes, and what actions can reduce it without sacrificing product quality. It tracks usage across LLMs, image generation, video, and audio from major providers through a single SDK and unified dashboard. Instead of stopping at aggregate token charts, Burnwise attributes costs to individual product features, users, sessions, teams, and agent workflows, helping teams understand the true cost of functions such as chat support, document analysis, summaries, or translation. Usage intelligence highlights cost-to-value mismatches, while anomaly alerts identify sudden spikes and runaway prompts in real time. Burnwise delivers a small set of prioritized decision cards with estimated savings, risk, and quality impact, covering actions such as switching models, enabling semantic caching, setting limits, or changing how a feature runs.

Amnic is a FinOps tool powered by context-aware AI agents that helps organizations gain clarity and control over their cloud spending. It automates cloud cost management by deploying role-specific agents that analyze usage, detect anomalies, and generate insights tailored to different stakeholders. Through its cloud cost observability capabilities, Amnic enables teams to visualize, analyze, and optimize infrastructure expenses, turning complex cloud bills into actionable intelligence. It provides fast cloud financial health checks, natural-language insights, and automated reporting that reduce the manual effort typically required for FinOps workflows. Built-in governance tools monitor budget drift, enforce tagging hygiene, and assign ownership, helping organizations maintain accountability across engineering and finance teams.

Launch production-ready apps with the LMOps stack for monitoring, model management, and more. Replace your OpenAI or other provider APIs with the Portkey endpoint. Manage prompts, engines, parameters, and versions in Portkey. Switch, test, and upgrade models with confidence! View your app performance & user level aggregate metics to optimise usage and API costs Keep your user data secure from attacks and inadvertent exposure. Get proactive alerts when things go bad. A/B test your models in the real world and deploy the best performers. We built apps on top of LLM APIs for the past 2 and a half years and realised that while building a PoC took a weekend, taking it to production & managing it was a pain! We're building Portkey to help you succeed in deploying large language models APIs in your applications. Regardless of you trying Portkey, we're always happy to help!

Cloudgov.ai is an agentic AI FinOps platform for continuous cost and policy governance across cloud, multicloud, data, container, and AI environments. It brings AWS, Azure, Google Cloud, Oracle Cloud, Snowflake, Databricks, Kubernetes, OpenAI, Anthropic, and Gemini into one control plane, giving teams a live view of cost, allocation, policy, and risk. Continuous Multicloud Observability connects accounts, analyzes historical spending, filters costs by region, account, and service, and forecasts future spend from history. AI-driven insights identify waste and optimization opportunities, while anomaly detection highlights unexpected spending surges and their financial impact. Ready-to-use Infrastructure as Code remediation snippets help engineering teams apply recommended changes, and Jira integration turns insights and anomalies into assignable work.

AI Cost Board is an AI API observability and cost control platform that brings costs, requests, tokens, latency, errors, and usage from multiple model providers into one real-time dashboard. Applications route LLM traffic through a single proxy endpoint, while requests are forwarded to the connected provider and logged with model, token, status, timing, costs, input, output, and raw JSON context. In most cases, teams only replace the provider base URL and use an AI Cost Board project key, keeping the original request structure intact. It supports providers including OpenAI, Anthropic, and Google Gemini, with a consistent setup that standardizes usage data across integrations. Cost analytics break spending down by project, provider, model, and timeframe, showing trends, cost per request, success rates, and operational performance. Searchable request logs help developers inspect payloads, troubleshoot failures, compare models, and investigate slow or expensive calls.

Obot is an open-source AI infrastructure platform and Model Context Protocol (MCP) gateway that gives organizations a centralized control plane for discovering, onboarding, managing, securing, and scaling MCP servers, services that connect large language models and AI agents to enterprise systems, tools, and data. It bundles an MCP gateway, catalog, admin console, and optional built-in chat interface into a modern interface that integrates with identity providers (e.g., Okta, Google, GitHub) to enforce access control, authentication, and governance policies across MCP endpoints, ensuring secure, compliant AI interactions. Obot lets IT teams host local or remote MCP servers, proxy access through a secure gateway, define fine-grained user permissions, log and audit usage, and generate connection URLs for LLM clients such as Claude Desktop, Cursor, VS Code, or custom agents.

Peta is an enterprise-grade control plane for the Model Context Protocol (MCP) that centralizes, secures, governs, and monitors how AI clients and agents access external tools, data, and APIs. It combines a zero-trust MCP gateway, secure vault, managed runtime, policy engine, human-in-the-loop approvals, and full audit logging into a single platform so organizations can enforce fine-grained access control, hide raw credentials, and track every tool call made by AI systems. Peta Core acts as a secure vault and gateway that encrypts credentials, issues short-lived service tokens, validates identity and policies on each request, orchestrates MCP server lifecycle with lazy loading and auto-recovery, and injects credentials at runtime without exposing them to agents. The Peta Console lets teams define who or which agents can access specific MCP tools in specific environments, set approval requirements, manage tokens, and analyze usage and costs.

SatGate is an agent authority and accountability Layer that governs what AI agents can access, spend, delegate, and execute before a request reaches an API, model, MCP tool, or paid external service. Deployed as an HTTP reverse proxy and MCP proxy, it applies scoped authority, per-agent budgets, route policies, and next-request revocation directly in the request path. Agents badge in once through existing Kubernetes, AWS, or OIDC identity, and SatGate Mint exchanges that identity for a cryptographically signed Macaroon containing limits for scope, budget, expiration, and delegation depth. Capabilities can only become more restrictive as they move through agent chains, preventing sub-agents from escalating beyond the authority they receive. Observe mode measures requests and attributes usage by agent, team, tool, route, and cost center without changing workflows; Control mode enforces hard budget caps before expensive or unauthorized work executes.

Unity AI Gateway provides centralized governance, observability, and spend controls across enterprise AI systems, helping organizations manage agents, tools, models, MCPs, and AI frameworks from a single governed layer. It applies consistent governance across Databricks-hosted AI, external models, coding agents, agent harnesses, and other AI services without locking teams into a single provider or stack. Identity-aware policies control what agents can access, which actions they can take, and which tools they can use, while built-in, custom, and third-party guardrails enforce safety and compliance across prompts, responses, and interactions. It captures prompts, traces, tool calls, payload logs, audit logs, token usage, and policy decisions to monitor behavior, investigate incidents, and support compliance. Centralized cost controls track consumption across users, teams, applications, agents, and providers, with budgets, rate limits, and hard spend caps.

Keep track of your OpenAI usage and costs with AI Spend and never be surprised again. AI Spend offers user-friendly cost tracking with a dashboard and notifications that passively monitor your usage and costs. The analytics and charts provide insights that help you optimize your OpenAI usage and avoid billing surprises. Get daily, weekly, and monthly notifications with your spending. Discover which models and how many tokens you're using. Get clear insights into how much OpenAI is costing you.

We give engineering, finance, and FinOps teams a single platform to track, attribute, and optimise LLM API spend across every provider. Costs are broken down by dimensions you define, matching how your business already reports its financials. Engineering teams get frictionless cost tracking without slowing anything down. CTOs get a single pane of glass to enforce model governance and prevent shadow usage. CFOs get finance-grade reporting for forecasting, budgeting, and chargebacks, attributed using their own reporting structure. FinOps teams get real-time, multi-provider cost data that slots straight into the workflows they already run for cloud. If your organisation uses LLM APIs and the board is asking "what are we spending and why?" we're the answer.

Mavvrik is an AI and hybrid infrastructure cost management platform that gives finance, FinOps, IT, and engineering teams one control center for GenAI, autonomous agents, GPUs, cloud, on-premises systems, Kubernetes, data platforms, and SaaS. It unifies cost, usage, and telemetry signals from AWS, Azure, Google Cloud, Oracle, VMware, NVIDIA, OpenAI, Anthropic, Gemini, Snowflake, Databricks, and LiteLLM, creating a single source of truth across the technology stack. Teams can track every model call, agent interaction, GPU hour, workload, service, and resource, then allocate spending by customer, product, feature, project, application, environment, team, or cost center. Cost-to-serve and unit-economics analysis reveal margin drains, expensive workloads, and the true cost of delivering each offering. Real-time anomaly detection and alerts identify usage before it becomes a budget surprise, while predictive forecasting helps organizations model cloud, GPU, and AI expenses.

Requesty is a cutting-edge platform designed to optimize AI workloads by intelligently routing requests to the most appropriate model based on the task at hand. With advanced features like automatic fallback mechanisms and queuing, Requesty ensures uninterrupted service delivery, even during model downtimes. The platform supports a wide range of models such as GPT-4, Claude 3.5, and DeepSeek, and offers AI application observability, allowing users to track model performance and optimize their usage. By reducing API costs and improving efficiency, Requesty empowers developers to build smarter, more reliable AI applications.

AICosts.ai is a unified AI cost management platform that brings billing and usage data from more than 50 providers into one dashboard. Teams upload provider invoices and exports in PDF, CSV, or JSON format, or push usage events through the developer API, and the platform parses them into a normalized structure without requiring a proxy or changes to production requests. It supports services including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Vertex AI, Cohere, Groq, Hugging Face, Pinecone, RunwayML, Make, Zapier, and n8n. Daily views break spending down by platform, model, and billed unit, including tokens, operations, characters, and other provider-specific measures, helping users compare services and see where each bill comes from. Budgets can cover the full AI stack or a specific platform or feature, with email alerts when rolling 30-day spending crosses configured thresholds.

Cloudflare AI Gateway is an intelligent control plane for AI applications, built to connect to any model, dynamically route requests, and manage usage, billing, and logs from one unified gateway. It gives teams visibility and control over AI apps by connecting applications to AI Gateway, gathering insights on how people are using the application through analytics and logging, and controlling how the application scales with caching, rate limiting, request retries, model fallback, and more. AI Gateway helps reduce cost and latency by caching responses and reducing redundant API calls, so frequent requests can be served directly from Cloudflare’s cache instead of the original model provider. It improves reliability with dynamic controls that configure how and when model provider APIs are called based on attributes, fallbacks, latency, cost, or availability, with routing rules that can be adjusted from the dashboard or API without redeployments or downtime.

Track costs, usage, and latency for GPT applications with one line of code. Trusted by leading companies building with OpenAI. We will support Anthropic, Cohere, Google AI, and more coming soon. Stay on top of your costs, usage, and latency. Integrate models like GPT-4 with Helicone to track API requests and visualize results. Get an overview of your application with an in-built dashboard, tailor made for generative AI applications. View all of your requests in one place. Filter by time, users, and custom properties. Track spending on each model, user, or conversation. Use this data to optimize your API usage and reduce costs. Cache requests to save on latency and money, proactively track errors in your application, handle rate limits and reliability concerns with Helicone.

Toolspend is an AI-powered spend management platform designed to give organizations complete visibility into their AI and SaaS costs through a unified, automated dashboard. It connects directly to AI providers and financial data sources to reveal real usage patterns, show which teams drive consumption, and reconcile token metrics with actual billing. It goes beyond simple subscription tracking by analyzing usage behavior to identify underutilized licenses, duplicate tools across departments, and potential overpayments. It provides real-time monitoring, anomaly alerts for unusual spikes, and month-end forecasting so teams can anticipate costs before invoices arrive. It also delivers AI-driven recommendations such as switching to cheaper models or pausing idle resources, helping companies reduce waste and control budget growth.

Paperclip.inc is a control plane for hiring and managing AI agents that can support engineering, growth, operations, research, and other company workflows. It lets users run multiple AI agents from a single inbox instead of juggling separate model tabs and scattered conversations. The platform supports agents such as Claude, Codex, Cursor, Gemini, DeepSeek, Qwen, GLM, Kimi, MiniMax, and others. Paperclip.inc includes approvals, permissions, budgets, audit logs, goals, routines, and scheduled heartbeats to help companies manage AI work with structure and accountability. Teams can install pre-built AI companies with defined org charts, agent configurations, and skill sets for areas like engineering, agencies, research labs, and digital studios. With EU-hosted managed infrastructure, open-source foundations, and per-company pricing, Paperclip.inc helps organizations scale AI work while maintaining control over cost, direction, and governance.

Trase is a governed AI platform for healthcare, government, and enterprise environments where trust, security, sovereignty, and predictability are non-negotiable. It provides a powerful foundation for deploying AI agents across real workflows, with hundreds of specialized agents ready to run in production and the infrastructure needed to keep every workflow, decision, and escalation under control. Trase Origin is the operating system agents run on, built to orchestrate, secure, and govern agents across cloud, on-premises, VPC, and edge environments while keeping data where it lives. Trase and third-party agents operate under one control plane with shared policy enforcement, monitoring, cost controls, escalation paths, and a full, immutable audit trail. It supports HIPAA- and SOC2-compliant deployment, data residency, privacy, model flexibility, and no vendor lock-in.

Agent Control is the open source control plane for AI agents, built to establish a new standard for governing agent behavior at scale. It solves the problem of scattered, hardcoded checks by giving teams a centralized governance layer with step-level enforcement that can be managed from a single control plane and updated in real time without touching agent code. Developers can make any function governable by adding the control() decorator, turning meaningful decision points inside an agent into independently governed control points with their own policies. When a decorated function executes, Agent Control evaluates the input or output against the active policy and returns a decision: deny, steer, warn, log, or allow. If the decision is denied, the SDK raises a ControlViolationError before the unsafe action can proceed. Policies are decoupled from code, so developers decide where to place control hooks while policy teams decide what those hooks enforce.

Notenic is a runtime orchestration and governance platform designed to control and secure autonomous AI agents (“digital labor”) in real time, particularly in environments where failure carries regulatory, legal, or operational consequences. It operates as an infrastructure layer that sits directly in the execution path of AI systems, enforcing deterministic governance before any action reaches systems of record, rather than relying on post-output filters or prompt-level controls. It introduces a zero-trust runtime architecture built on core principles such as zero-persistence (no data retained after each session), execution-path control (policy enforcement at the moment of action), and independence from model context, ensuring that adversarial inputs cannot override governed behavior. Notenic provides a unified control plane that includes agent workforce management (treating AI agents as operational units with defined roles and supervision).

MintMCP is an enterprise-grade Model Context Protocol (MCP) gateway and governance platform that provides centralized security, observability, authentication, and compliance controls for AI tools and agents connecting to internal data, systems, and services. It lets organizations deploy, monitor, and govern MCP infrastructure at scale, giving real-time visibility into every MCP tool call, enforcing role-based access control and enterprise authentication, and maintaining complete audit trails that meet regulatory and compliance needs. Built as a proxy gateway, MintMCP consolidates connections from AI assistants like ChatGPT, Claude, Cursor, and others to MCP servers and tools, enabling unified monitoring, blocking of risky behavior, secure credential management, and fine-grained policy enforcement without requiring each tool to implement security individually.

LLMeter is an open source AI cost monitoring platform that gives developers one dashboard for tracking spend across OpenAI, Anthropic, DeepSeek, OpenRouter, Mistral, and Azure OpenAI. Teams connect read-only provider keys and can see real costs, daily trends, model-level breakdowns, and optimization opportunities in about 30 seconds without installing an SDK, changing endpoints, or routing production traffic through a proxy. Because requests continue going directly to the model provider, LLMeter adds no latency, does not become a point of failure, and never sees prompts or completions. Budget alerts warn teams before spending crosses daily or monthly limits, while anomaly detection identifies unexpected usage spikes before they grow. The dashboard shows which providers, models, endpoints, customers, and environments are driving costs, and OpenRouter support extends visibility across more than 500 models.

Dapple is an Enterprise OS Cloud built for regulated enterprises and AI-native companies that need dedicated AI infrastructure without compromising on isolation, data residency, governance, or performance. It sits between the public cloud and the private data center, combining dedicated, single-tenant GPU infrastructure with orchestration, compliance, connectivity, observability, and operations through one control plane. Topology-aware placement, multi-GPU scheduling, fault-domain isolation, and reserved clusters provide predictable performance without noisy neighbors. Private connectivity extends existing cloud environments directly to dedicated compute, while identity, container orchestration, threat protection, and governance policies continue working across the deployment. Compliance is enforced at the architecture level before workloads execute, supporting in-country data residency, audit requirements, and regulatory frameworks.

dstack is an orchestration layer designed for modern ML teams, providing a unified control plane for development, training, and inference on GPUs across cloud, Kubernetes, or on-prem environments. By simplifying cluster management and workload scheduling, it eliminates the complexity of Helm charts and Kubernetes operators. The platform supports both cloud-native and on-prem clusters, with quick connections via Kubernetes or SSH fleets. Developers can spin up containerized environments that link directly to their IDEs, streamlining the machine learning workflow from prototyping to deployment. dstack also enables seamless scaling from single-node experiments to distributed training while optimizing GPU usage and costs. With secure, auto-scaling endpoints compatible with OpenAI standards, it empowers teams to deploy models quickly and reliably.

ZenLLM is an AI cost optimization platform for engineering teams running LLM applications in production. It connects provider invoices to the application behavior behind them, showing which prompts, workflows, models, customers, retries, and request paths are driving spend. Teams send request-level telemetry through the ZenLLM SDK and can attach business context such as workflow, owner, customer, team, or product feature without storing prompt or response content. It monitors token usage, model selection, latency, errors, retries, and cost, then surfaces the waste patterns hidden by aggregate provider dashboards. It detects context accumulation when conversations or agents resend growing histories, premium-model overuse on low-risk work, retry loops that repeat expensive context, stale system prompts, routing mistakes, anomalies, and weak cost ownership.

Microsoft MCP Gateway is an open source reverse proxy and management layer for Model Context Protocol (MCP) servers that enables scalable, session-aware routing, lifecycle management, and centralized control of MCP services, especially in Kubernetes environments. It functions as a control plane that routes AI agent (MCP client) requests to the appropriate backend MCP servers with session affinity, dynamically handling multiple tools and endpoints under one unified gateway while ensuring authorization and observability. It lets teams deploy, update, and delete MCP servers and tools via RESTful APIs, register tool definitions, and manage these resources with access control layers such as bearer tokens and RBAC. Its architecture separates control plane management (CRUD operations on adapters/tools and metadata) from data plane routing (streamable HTTP connections and dynamic tool routing), offering features like session-aware stateful routing.

PaletteAI is an enterprise AI infrastructure management platform designed to accelerate the deployment, scaling, governance, and operationalization of AI workloads across data centers, cloud, and edge environments. It provides a turnkey yet flexible solution that lets platform, DevOps, and AI/data science teams design repeatable, governance-approved AI stacks with all needed components, from storage to machine learning frameworks, without manual, brittle configuration work, helping teams get new AI environments up and running with a click. It serves as a unified control plane that streamlines the entire lifecycle of AI infrastructure: users can build, deploy, and manage AI environments while optimizing hardware utilization, enforcing security and policy guardrails, and supporting “day two” operations such as resource governance and monitoring.

Tuning Engines is a unified AI control and governance layer for teams building production intelligence across models, agents, tools, and fine-tuned systems. It brings together the full AI lifecycle in one governed platform: inference, model routing, fallback policies, fine-tuning jobs, datasets, evaluations, model imports and exports, custom models, agents, MCP servers, reusable skills, guardrails, AGT YAML policies, data capture, runtime traces, usage analytics, API keys, billing, team roles, and integrations. Developers get OpenAI-compatible APIs, Anthropic-compatible routes, CLI workflows, MCP access, coding-agent integrations, and resource catalogs for models, agents, tools, and skills. Teams can connect Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and other AI workflows through a single governed platform.

Tokonomics is an AI cost metering proxy that sits between your app and any LLM provider. One URL change gives you real-time cost tracking, budget alerts, and hard spending caps across OpenAI, Anthropic, DeepSeek, Google Gemini, Mistral, Groq, and more. How it works: Replace your LLM base URL with Tokonomics, keep your existing code. Every API call is logged with token counts, cost (8-decimal USD precision), latency, and custom tags for per-team or per-feature attribution. Key features: - Budget alerts via email, Slack, or Teams at configurable thresholds - Hard spending caps that block requests when monthly budget is exceeded - Analytics dashboard with spend-by-model, daily trends, and cost optimization reports - BYOK (Bring Your Own Keys) with AES-256 encryption - Rate limiting per API key - Works with any language or HTTP client (PHP, Python, Node.js, Go, Ruby)

Humatron is an enterprise AI workforce platform that turns AI agents into secure, trusted, vendor-neutral AI workers that operate across teams, tools, and business workflows. Organizations can build, hire, customize, onboard, train, monitor, and govern AI workers from a single control plane, managing them more like digital employees than standalone assistants. Each worker can be powered by modern core agents such as Claude, OpenAI, Manus, OpenClaw, Hermes, Cursor, or custom agentic systems, allowing companies to mix or replace underlying models without changing the workforce layer. AI workers can act autonomously, initiate and complete tasks, collaborate across teams, learn new skills, and interact through existing channels such as email, Slack, Teams, Zoom, and phone. Builds define reusable tools, rules, behaviors, and agent integrations, while each hired worker becomes an isolated instance customized for a specific company, team, and job.

AICtrlNet enforces AI governance instead of only observing it. Unlike tools that score or monitor AI risk, it runs the work — orchestrating AI agents, humans (as first-class agents, not just approval gates), and enterprise systems under a unified governance model. Model-independent across OpenAI, Claude, Gemini, and local runtimes (Ollama, vLLM). A 6-phase Control Spectrum sets autonomy per workflow and agent. Ships 43 role-configured agent templates and 177+ workflow templates across 41 industry packs. Runs n8n, Zapier, and Make as nodes rather than replacing them. Supports HIPAA, GDPR, SOC2, and EU AI Act via design-time governance and audit-grade accountability. Open-core editions: Community (MIT, free, self-hostable) ships the core platform; Business adds ML-enhanced governance and risk scoring; Enterprise adds multi-tenancy and federation. Access via the HitLai visual no-code interface, REST API, or MCP.