Top WrangleAI Alternatives

FinOpsly helps enterprises regain control of cloud, data, and AI spend—and turn it into measurable business value. As organizations scale across AWS, Azure, GCP, and modern data platforms like Snowflake, Databricks, and BigQuery, technology costs become harder to predict, explain, and control. FinOpsly addresses this challenge by connecting technology spend directly to business outcomes—and enabling teams to act on it in real time. FinOpsly unifies cloud infrastructure, data platforms, and AI workloads into a single operating model where spend is planned upfront, monitored continuously, and optimized automatically. Using explainable, policy-driven AI, the platform helps organizations reduce waste, prevent overruns, and align technology investments with business priorities—without slowing down innovation. With FinOpsly, organizations can: Understand exactly where money is going across AWS, Azure, GCP, Snowflake, Databricks, and BigQuery Plan and forecast costs earlier, before new cloud, data, or AI initiatives are deployed Automate optimization safely, using governance rules aligned to business risk and performance needs Deliver measurable financial impact quickly, often within weeks rather than quarters FinOpsly enables IT, finance, and business leaders to operate from a shared view of spend and value—bringing Value-Control™ to cloud, data, and AI investments at enterprise scale.

FinOps LLM is a sophisticated solution for managing AI costs and achieving observability, specifically tailored for engineering teams working with GenAI in production environments. It provides clarity on token spending across multiple providers, including OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Azure, and Groq, while also reconciling internal usage metrics with the corresponding invoices from these services. Users have the ability to filter expenses at the token level based on a variety of criteria such as provider, model, feature, team, customer, and environment, ensuring that every dollar has a responsible owner. The platform also features attribution and chargeback capabilities that link usage to product interfaces and customer segments, facilitating showback processes and enabling data exports to platforms like NetSuite, QuickBooks, CSV, or via APIs. Moreover, it includes real-time anomaly detection functionalities that monitor spending, latency, and quality against dynamically established baselines, sending alerts through Slack, PagerDuty, email, or webhooks when significant variations occur. To bolster cost management, optional budget enforcement and auto-throttling tools are available to curb overspending caused by runaway agents, excessive retries, or unforeseen shifts in model performance. By integrating these various functions, the platform empowers engineering teams to effectively oversee their AI resources while ensuring robust financial accountability, ultimately leading to more informed decision-making and strategic resource allocation.

Cloptima represents a groundbreaking solution that merges artificial intelligence with cloud-based financial operations, delivering a framework for managing large language model expenses while providing insights into costs across multiple cloud environments. The platform empowers teams to securely leverage their credentials from major AI providers such as OpenAI, Anthropic, Gemini, Vertex AI, and Amazon Bedrock through an AI gateway that incorporates robust security measures like encrypted controls, virtual keys, model policies, token limits, budgets, guardrails, and attribution before any requests reach the providers. Its spend analytics feature offers a detailed overview of usage, organized by various factors including provider, model, team, application, environment, user, agent session, tool, workflow, and additional metrics, while the agent controls track retries, loops, tool interactions, and the risk of excessive costs. Furthermore, precise and semantic response caching works to reduce unnecessary usage, and intelligent routing functions enable traffic to be directed to more economical or faster models, with the flexibility for canary rollout and rollback in case of declines in quality, latency, or error rates. This comprehensive strategy guarantees that organizations can proficiently oversee their expenditures related to AI while enhancing efficiency and performance in all operational areas, thus promoting sustainable growth and innovation in a rapidly evolving technological landscape.

Domino is a powerful enterprise AI platform built to help organizations develop, deploy, and manage AI systems at scale while delivering measurable business value. It provides a unified environment that supports the entire AI lifecycle, from data exploration and experimentation to deployment and monitoring. The platform enables self-service data science by giving users secure access to datasets, development tools, and scalable compute resources such as CPUs and GPUs. Domino supports a wide range of AI applications, including machine learning models, generative AI solutions, and agent-based systems. Its orchestration capabilities allow organizations to run workloads across hybrid, multi-cloud, and on-premises environments with flexibility and efficiency. The platform includes robust governance features, such as model registries, audit trails, and automated policy enforcement, ensuring transparency and compliance. It also tracks experiments and model lineage, providing a complete system of record for AI development. Domino enhances collaboration by enabling teams to share insights, tools, and workflows across the enterprise. Cost optimization tools help manage infrastructure spending through autoscaling and resource monitoring. The platform integrates seamlessly with existing enterprise systems and supports industry-standard tools and frameworks. With strong security certifications and compliance support, it meets the needs of regulated industries. Overall, Domino enables organizations to industrialize AI, reduce risk, and accelerate innovation while maintaining full control over their AI operations.

LLMetrics is a robust solution designed for tracking costs associated with AI product development, seamlessly combining model expenses, token usage, feature attribution, and usage alerts into an engaging and user-friendly dashboard. This versatile tool supports over 100 models from a range of providers, such as OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Together AI, and Groq, ensuring that pricing data is refreshed daily. Teams have the capability to tag each model interaction with essential information, including feature names, providers, model types, input tokens, and output tokens, which helps them identify the specific functionalities—like chatbots, summarizers, search tools, or lesson creators—that are driving their costs. The platform provides real-time updates alongside daily trend visualizations, showcasing how expenses change in response to software releases, adjustments to prompts, spikes in traffic, or shifts between models. Furthermore, LLMetrics is equipped with spend thresholds and spike-detection mechanisms that can notify teams through email or Slack when unusual usage patterns are detected, effectively assisting them in averting runaway loops and unexpected cost increases before they receive their provider invoices. By utilizing these valuable insights, teams can strategically navigate their AI product initiatives and manage their budgets more effectively, ensuring a well-informed approach to financial planning. Ultimately, this enhances the overall efficiency of their AI development process.

Burnwise operates as an AI-driven financial assistant that delivers valuable insights regarding an organization's spending on AI technologies, elucidating the causes of expenditure variations and offering tactics for reducing costs while maintaining product quality. The platform tracks usage metrics from prominent providers of large language models, image generation, video, and audio services through an integrated SDK and a unified dashboard. Unlike typical platforms that simply aggregate token data, Burnwise dissects costs by specific features of products, individual users, sessions, teams, and agent workflows, enabling teams to better understand their spending related to services like chat support, document evaluation, summaries, or translation tasks. Its usage intelligence reveals gaps between incurred costs and derived value, while it also provides real-time alerts for any unusual spikes in costs or excessive prompt usage. Furthermore, Burnwise includes an organized set of prioritized decision cards that highlight potential savings opportunities, associated risks, and impacts on quality, recommending actions such as modifying models, initiating semantic caching, setting limits, or changing operational features. By delivering these comprehensive insights, Burnwise equips organizations with the tools necessary to make strategic choices that boost operational efficiency and enhance resource management, ultimately leading to better financial outcomes. Through these capabilities, Burnwise stands as a vital resource for companies aiming to strike a balance between innovation and cost-effectiveness in their AI investments.

AICostGuardian is an all-encompassing platform designed to oversee AI-related expenses, allowing businesses to effectively track, optimize, and manage their expenditures across more than 25 AI service providers via a unified interface. The platform diligently monitors every API interaction with millisecond precision, delivering real-time cost assessments while integrating provider data into extensive analytics, automated reporting, forecasting, and interactive visual dashboards. Teams can analyze spending behaviors, compare usage against industry peers, identify opportunities for cost reduction, and utilize machine-learning insights along with smart recommendations to reduce unnecessary AI expenditures. With features for predictive alerts and anomaly detection, users receive prompt notifications regarding any abnormal usage patterns and potential budget overruns, while adjustable spending limits help maintain control over consumption. Furthermore, it offers department-specific cost tracking, team performance metrics, granular permission settings, and role-based access, promoting clear accountability and governance of AI resource usage across the organization, which aids in making informed decisions and strategic planning. As the adoption of AI technologies continues to rise, AICostGuardian proves to be an essential resource for promoting fiscal responsibility and enhancing operational productivity, ultimately contributing to a more sustainable AI integration.

Amnic stands out as a cutting-edge FinOps solution that leverages intelligent AI agents to enhance organizations' ability to monitor and manage their cloud spending. By automating the cloud cost management processes, it deploys agents tailored to specific roles, which analyze usage trends, spot irregularities, and provide insights tailored to diverse stakeholders. Featuring powerful cloud cost observability tools, Amnic empowers teams to visualize, scrutinize, and optimize their infrastructure expenditures, converting complex cloud billing data into clear, actionable insights. The system facilitates quicker assessments of cloud financial health, presents findings in natural language, and simplifies reporting tasks, significantly reducing the manual workload typically linked to FinOps activities. Moreover, its built-in governance features allow for monitoring budget discrepancies, ensuring adherence to tagging standards, and defining ownership responsibilities, which cultivates accountability across engineering and finance teams. Consequently, Amnic not only streamlines financial monitoring but also strengthens teamwork within organizations, making it an essential ally for efficient cloud cost management. Ultimately, this innovative platform positions companies to better navigate the complexities of cloud expenditures while fostering a collaborative environment that supports financial discipline.

LMOps is a comprehensive stack designed for launching production-ready applications that facilitate monitoring, model management, and additional features. Portkey serves as an alternative to OpenAI and similar API providers. With Portkey, you can efficiently oversee engines, parameters, and versions, enabling you to switch, upgrade, and test models with ease and assurance. You can also access aggregated metrics for your application and user activity, allowing for optimization of usage and control over API expenses. To safeguard your user data against malicious threats and accidental leaks, proactive alerts will notify you if any issues arise. You have the opportunity to evaluate your models under real-world scenarios and deploy those that exhibit the best performance. After spending more than two and a half years developing applications that utilize LLM APIs, we found that while creating a proof of concept was manageable in a weekend, the transition to production and ongoing management proved to be cumbersome. To address these challenges, we created Portkey to facilitate the effective deployment of large language model APIs in your applications. Whether or not you decide to give Portkey a try, we are committed to assisting you in your journey! Additionally, our team is here to provide support and share insights that can enhance your experience with LLM technologies.

Cloudgov.ai is an advanced FinOps platform driven by AI, dedicated to the continuous oversight of expenses and compliance with policies across diverse environments such as cloud, multicloud, data systems, containers, and AI technologies. By incorporating major cloud services like AWS, Azure, Google Cloud, Oracle Cloud, Snowflake, Databricks, Kubernetes, OpenAI, Anthropic, and Gemini into a single control panel, it allows teams to monitor costs, resource allocation, policy adherence, and associated risks in real-time. Its Continuous Multicloud Observability feature connects various accounts, analyzes historical spending patterns, sorts expenses by region, account, and service, and forecasts future costs based on past trends. With the help of AI-generated insights, the platform identifies areas of unnecessary expenditure and opportunities for improvement, while its anomaly detection system warns users about unexpected increases in costs and their financial consequences. Additionally, it offers ready-made Infrastructure as Code snippets for quick remediation, enabling engineering teams to implement recommended changes effortlessly, and ties in with Jira to translate insights and anomalies into actionable tasks for team members, thus enhancing the workflow for financial management. Ultimately, Cloudgov.ai not only helps organizations maintain financial oversight but also promotes greater efficiency in their cloud operations, ensuring they can adapt swiftly to changing business needs. By leveraging this platform, companies can achieve a more streamlined financial strategy that aligns with their operational goals.

Lunar.dev functions as an all-encompassing platform for AI gateway and API consumption management, specifically crafted to empower engineering teams with a unified interface for monitoring, regulating, securing, and optimizing all interactions with outbound APIs and AI agents. This encompasses the ability to track communications with large language models, employ Model Context Protocol tools, and connect with external services across a variety of distributed applications and workflows. The platform provides immediate visibility into usage trends, latency problems, errors, and associated costs, enabling teams to oversee every interaction involving models, APIs, and agents in real-time. Moreover, it facilitates the implementation of policies such as role-based access control, rate limiting, quotas, and cost management strategies to maintain security and compliance, while preventing excessive use or unexpected charges. By centralizing the oversight of outbound API traffic through features like identity-aware routing, traffic inspection, data redaction, and governance, Lunar.dev significantly enhances operational efficiency for its users. Its MCPX gateway further simplifies the administration of numerous Model Context Protocol servers by integrating them into a single secure endpoint, thereby providing comprehensive observability and permission management for AI tools. In addition, this platform not only alleviates the challenges associated with API management but also substantially increases the capacity of teams to effectively leverage AI technologies, ultimately driving innovation and productivity within organizations.

Mavvrik functions as an advanced platform designed to oversee expenses related to AI and hybrid infrastructures, offering a centralized location for finance, FinOps, IT, and engineering teams to manage GenAI, autonomous agents, GPUs, cloud environments, on-premises assets, Kubernetes, data platforms, and SaaS offerings. By integrating cost, usage, and telemetry information from leading providers such as AWS, Azure, Google Cloud, Oracle, VMware, NVIDIA, OpenAI, Anthropic, Gemini, Snowflake, Databricks, and LiteLLM, it creates a thorough source of truth for the entire technology landscape. Teams can carefully track model interactions, agent activities, GPU performance, and resource workloads, enabling them to allocate spending accurately across various parameters, including customer, product, feature, project, application, environment, team, or cost center. Mavvrik's detailed analysis of cost-to-serve and unit economics reveals margin declines, pinpoints expensive workloads, and clarifies the true costs associated with delivering each service. Moreover, its real-time anomaly detection and alerting functionality helps to identify unusual usage trends before they lead to unexpected budget overruns, while its predictive forecasting capabilities assist organizations in effectively planning their cloud, GPU, and AI-related expenses. This comprehensive strategy not only empowers teams to make well-informed financial choices but also optimizes resource utilization, paving the way for sustainable growth and enhanced operational efficiency. As a result, Mavvrik stands out as an essential tool for organizations seeking to maximize their investments in technology.

AI Cost Board is an all-encompassing platform designed for tracking AI API utilization and managing related expenses, integrating essential metrics such as costs, request counts, token usage, latency, error rates, and overall consumption from multiple model providers into a cohesive, real-time dashboard. By routing LLM traffic through a singular proxy endpoint, applications can seamlessly transmit requests to the chosen provider while garnering comprehensive logs that detail model specifics, token consumption, status updates, timing, costs, inputs, outputs, and raw JSON context. Generally, teams need merely to modify the base URL of their provider and employ an AI Cost Board project key, which ensures that the original request format remains intact. This platform supports a range of providers including OpenAI, Anthropic, and Google Gemini, providing a uniform setup that aligns usage data across various integrations. Cost analysis features break down expenditures by project, provider, model, and time period, thus allowing users to spot trends, compute costs per request, evaluate success rates, and analyze operational efficiency. Additionally, the searchable logs of requests enable developers to scrutinize payloads, troubleshoot failures, compare different models, and investigate instances of slow or expensive API calls. Through these features, AI Cost Board not only boosts visibility and management of AI API spending but also fosters informed decision-making for teams that leverage AI technology for their projects, ultimately promoting more effective resource allocation.

SatGate serves as a crucial governance and oversight mechanism for AI agents, managing their permissions, spending, delegation, and operational functionalities before they engage with APIs, models, MCP tools, or any external paid services. Acting as both an HTTP reverse proxy and MCP proxy, it enforces scoped authority, individual agent budgets, routing guidelines, and revocation of requests seamlessly within the workflow. To access these capabilities, agents must authenticate through recognized systems such as Kubernetes, AWS, or OIDC, after which SatGate Mint transforms the authenticated identity into a cryptographically secured Macaroon that specifies constraints on scope, budget, expiration, and the depth of delegation. The design of this architecture guarantees that permissions can only become more restrictive as requests move through chains of agents, effectively preventing sub-agents from surpassing their designated authority. Furthermore, the Observe mode meticulously monitors requests and evaluates resource utilization by categorizing data based on agents, teams, tools, routes, and cost centers, all while maintaining existing workflows. On the other hand, the Control mode establishes stringent budgetary constraints to deter unauthorized or expensive actions from being carried out. This integrated dual functionality not only ensures organizations retain comprehensive oversight but also empowers their AI agents with the freedoms they require to operate effectively. Additionally, such a system fosters a balance between innovation and risk management in the deployment of AI technologies.

Unity AI Gateway provides a cohesive platform for managing governance, oversight, and financial management of diverse AI systems within an organization, allowing for the centralized supervision of agents, tools, models, and AI frameworks through a single, regulated interface. This solution promotes uniform governance for AI services, including those hosted on Databricks, external models, coding agents, and agent harnesses, effectively mitigating the risk of vendor lock-in for teams. With policies that consider user identities, it regulates agent access, allowable actions, and tool utilization, while integrated, custom, and third-party protections ensure safety and compliance during prompts, responses, and user interactions. The system meticulously logs prompts, tracks tool interactions, records payloads, maintains audit trails, documents token usage, and captures policy decisions, all of which enhance behavior monitoring, facilitate incident investigations, and support compliance efforts. Additionally, it features centralized financial controls that allow for the tracking of consumption across users, teams, applications, agents, and providers, incorporating budget management, rate limits, and strict spending thresholds to enhance resource allocation efficiency. By simplifying these workflows, Unity AI Gateway not only optimizes the use of AI technologies but also ensures that organizations remain aligned with their governance and financial constraints, making it an essential tool for modern enterprises. Ultimately, this comprehensive approach positions businesses to leverage AI innovations while maintaining accountability and control over their operations.

Keep track of your OpenAI expenses seamlessly with AI Spend, which helps you remain aware of your financial commitments. This innovative tool offers an easy-to-navigate dashboard alongside notifications that consistently monitor both your usage and spending. By providing in-depth analytics and visual representations of data, it equips you with essential insights that contribute to optimizing your OpenAI engagement and avoiding surprise charges. You can opt to receive spending updates daily, weekly, or monthly, while also identifying specific models and token usage trends. This ensures a clear perspective on your OpenAI financials, empowering you to manage your budget more effectively. With AI Spend, you'll always have a thorough grasp of your spending patterns, enabling proactive financial planning and management. Plus, the ability to customize your alerts adds another layer of convenience to your budgeting process.

Requesty is a cutting-edge platform designed to optimize AI workloads by intelligently routing requests to the most appropriate model for each individual task. It features advanced functionalities such as automatic fallback systems and efficient queuing mechanisms, ensuring uninterrupted service availability even when some models may be out of service temporarily. With support for a wide range of models, including GPT-4, Claude 3.5, and DeepSeek, Requesty also offers observability for AI applications, allowing users to track model performance and adjust their application usage for maximum effectiveness. By reducing API costs and enhancing operational efficiency, Requesty empowers developers with the necessary tools to build more intelligent and reliable AI solutions. This platform not only fine-tunes performance but also encourages innovation within the AI landscape, creating opportunities for the development of transformative applications. As a result, developers can push the boundaries of what AI can achieve, leading to more sophisticated and impactful technologies.

Preloop is an open-source control plane tailored for AI agents that can execute real-world tasks, featuring a robust multi-layered security system. This includes an MCP firewall for tool access management, an AI model gateway that promotes cost efficiency, safety, and accountability, along with policy-as-code that emphasizes human oversight, all while ensuring runtime session visibility and maintaining audit trails in a self-hosted environment. As AI agents rapidly gain the ability to deploy code, alter infrastructure, manage financial transactions, access production data, and generate model costs nearly instantaneously, Preloop equips teams with the tools to oversee agent activities, track spending, and identify which actions require human approval. It supports an array of tools such as OpenClaw, Hermes, Claude Code, Codex CLI, Cursor, Gemini CLI, Windsurf, Cline, OpenCode, and any agents compliant with MCP standards. Moreover, access rules can assess not just tool names but also their arguments and context, utilizing CEL expressions to set specific conditions. Teams are also given the option to start with observability features and gradually implement approval and denial processes without needing SDKs or significant changes to current applications, facilitating a more efficient rollout. This comprehensive strategy not only ensures that organizations retain control over the functionalities of their AI agents but also allows them to adapt to evolving needs and challenges in the AI landscape. Such flexibility is crucial in a rapidly changing technological environment where the implications of AI actions can be profound.

Our platform offers an all-in-one solution for engineering, finance, and FinOps teams to effectively monitor, allocate, and improve expenditures related to LLM APIs from a variety of providers. Spending is organized according to customizable metrics that correspond with your organization's financial reporting requirements. Engineering teams can enjoy smooth cost tracking without disrupting their daily operations. CTOs gain a comprehensive perspective that aids in model governance and reduces the risk of unauthorized usage. CFOs are provided with detailed financial reports that support accurate forecasting, budgeting, and chargebacks, all customized to fit their specific reporting needs. FinOps teams benefit from immediate access to cost data across different providers, seamlessly integrating into their current cloud management workflows. When your organization engages with LLM APIs and board members seek clarity on spending and its rationale, we become the ultimate answer to those inquiries. Moreover, our platform not only facilitates informed financial decision-making but also enhances accountability while optimizing resource distribution. This comprehensive approach ensures that every team is equipped with the insights necessary to manage costs effectively.

MintMCP acts as a comprehensive Model Context Protocol (MCP) gateway and governance solution tailored for enterprises, presenting a unified strategy for security, observability, authentication, and compliance related to AI tools and agents that interact with internal information, systems, and services. This innovative platform enables organizations to deploy, supervise, and manage their MCP infrastructure on a vast scale, delivering real-time insights into every interaction with MCP tools while incorporating role-based access control and enterprise-level authentication, all while ensuring thorough audit trails that comply with regulatory requirements. Serving as a proxy gateway, MintMCP efficiently consolidates connections from multiple AI assistants, such as ChatGPT, Claude, and Cursor, thereby simplifying monitoring tasks, reducing risky behaviors, securely managing credentials, and enforcing specific policy measures without the need for separate security setups for each individual tool. By centralizing these essential functions, MintMCP not only boosts operational efficiency but also strengthens the security framework of organizations utilizing AI technologies. This integrated approach ultimately allows businesses to focus on their core objectives, knowing their AI interactions are managed securely and effectively.

AICosts.ai is an all-encompassing solution for overseeing expenses related to artificial intelligence, bringing together billing and usage data from more than 50 different service providers into one unified dashboard. Users have the convenience of uploading their invoices and data exports in formats like PDF, CSV, or JSON, or they can employ the developer API to send usage events, with the platform skillfully converting this data into a standardized format without requiring any proxy configurations or modifications to live requests. It supports a diverse range of services, including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Vertex AI, Cohere, Groq, Hugging Face, Pinecone, RunwayML, Make, Zapier, and n8n. Daily analytics provide a detailed breakdown of expenses by platform, model, and billed units, which include tokens, operations, characters, and other specific metrics from the providers, allowing users to evaluate different services and gain insight into their charges. Furthermore, users have the option to establish budgets that can either encompass the entire AI ecosystem or concentrate on particular platforms or features, and they are promptly notified via email when their rolling 30-day expenses exceed set limits, ensuring they remain aware of their expenditures and within budget. This comprehensive approach not only aids in tracking costs effectively but also fosters strategic financial planning for AI initiatives within organizations.

Toolspend is an advanced spend management platform driven by artificial intelligence, designed to give businesses a thorough understanding of their expenses linked to AI and SaaS services through an integrated, automated dashboard. By establishing seamless connections with AI service providers and financial systems, it reveals genuine usage patterns, identifies which teams are incurring costs, and correlates token metrics with billing information. This platform goes beyond mere subscription tracking by analyzing usage habits, enabling it to detect underutilized licenses, redundant tools across various departments, and opportunities for reducing overpayments. Equipped with capabilities like real-time monitoring, alerts for unexpected spikes in usage, and monthly forecasting, teams can proactively manage expenses before invoices arrive. Moreover, it provides AI-driven recommendations, such as shifting to less expensive models or discontinuing unused resources, which supports organizations in reducing waste and effectively managing budgetary increases. Additionally, by utilizing its insights, businesses are empowered to make strategic decisions that significantly improve their operational effectiveness and drive cost efficiency. This holistic approach not only streamlines expense management but also fosters a culture of financial awareness within the organization.

Effortlessly track expenses, usage, and latency for your GPT applications using just a single line of code. Esteemed companies that utilize OpenAI place their confidence in our service, and we are excited to announce our upcoming support for Anthropic, Cohere, Google AI, and more platforms in the near future. Stay updated on your spending, usage trends, and latency statistics. With Helicone, integrating models such as GPT-4 allows you to manage API requests and effectively visualize results. Experience a holistic overview of your application through a tailored dashboard designed specifically for generative AI solutions. All your requests can be accessed in one centralized location, where you can sort them by time, users, and various attributes. Monitor costs linked to each model, user, or conversation to make educated choices. Utilize this valuable data to improve your API usage and reduce expenses. Additionally, by caching requests, you can lower latency and costs while keeping track of potential errors in your application, addressing rate limits, and reliability concerns with Helicone’s advanced features. This proactive approach ensures that your applications not only operate efficiently but also adapt to your evolving needs.

Singulr serves as a holistic platform tailored for enterprise AI governance and security, offering a unified control structure that supports organizations in discovering, securing, and optimizing their extensive AI deployments. By addressing the growing disparity between the swift adoption of AI technologies and the limitations of governance, it provides unmatched insight into all AI systems employed within the organization, encompassing bespoke applications, integrated AI solutions, publicly available tools, and shadow AI, which frequently bypass security detection. The platform meticulously identifies and inventories AI resources across the enterprise, establishing a dynamic record of agents, models, and services, while assessing their respective risks through comprehensive evaluations of data management, model lineage, vulnerabilities, and compliance standards. Furthermore, Singulr Pulse, the platform’s intelligence layer, analyzes millions of AI systems, assigns risk classifications, and streamlines automated onboarding processes, dramatically reducing approval timelines from weeks to just hours, all while maintaining stringent security protocols. This forward-thinking methodology not only improves the efficiency of AI adoption but also enables organizations to uphold a robust governance structure as they navigate the intricate landscape of AI integration. In doing so, it positions organizations to better respond to the challenges and opportunities presented by the evolving AI landscape.

LLMeter is an all-encompassing open-source solution aimed at tracking AI expenses, enabling developers to oversee their costs across multiple providers such as OpenAI, Anthropic, DeepSeek, OpenRouter, Mistral, and Azure OpenAI through a unified interface. By connecting read-only keys from these providers, teams can swiftly obtain in-depth information on actual spending, daily consumption patterns, model-specific data, and areas ripe for cost reductions, all accomplished in about 30 seconds without the necessity for installing SDKs, altering endpoints, or rerouting production traffic through intermediaries. As it allows direct interactions with model providers, LLMeter does not introduce extra latency, avoids being a single point of failure, and ensures that user prompts or completions remain unaccessed and unrecorded. Furthermore, the platform features budget alerts that inform teams before they surpass their daily or monthly budget limits, alongside anomaly detection tools that identify unexpected spikes in usage before they can escalate into larger issues. The user-friendly dashboard presents a comprehensive view of costs related to different providers, models, endpoints, customers, and environments, and its partnership with OpenRouter increases transparency by encompassing over 500 models, thus providing users with a powerful tool for effective AI expenditure management. In the end, LLmeter equips teams with the necessary insights to make educated financial choices concerning their AI operations, fostering a culture of mindful spending while leveraging advanced technologies. This approach not only enhances financial stewardship but also encourages strategic planning in resource allocation for future AI ventures.

Tuning Engines is an all-encompassing AI control and governance framework intended for teams focused on creating production intelligence that incorporates a wide range of models, agents, tools, and specialized systems. This platform brings together the entire AI lifecycle within a unified and regulated space, addressing crucial elements such as inference, model routing, fallback strategies, fine-tuning tasks, datasets, evaluations, model imports and exports, custom models, agents, MCP servers, reusable skills, guardrails, AGT YAML policies, data capture, runtime tracing, usage analytics, API management, billing, team roles, and a variety of integrations. Developers can take advantage of APIs that are compatible with OpenAI, routes that are aligned with Anthropic, as well as CLI workflows, MCP access, and smooth coding-agent integrations, supplemented by an extensive resource catalog for models, agents, tools, and skills. In addition, teams are empowered to connect different AI workflows, including Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and more, all facilitated through a single, governed platform that significantly boosts collaboration and operational efficiency. Ultimately, Tuning Engines not only streamlines the development process but also fosters a collaborative environment where diverse AI applications can thrive.

ZenLLM is an AI-powered platform designed to help engineering teams minimize expenses related to the deployment of LLM applications in active settings. It achieves this by connecting provider invoices directly to the specific activities within applications, allowing for the identification of which prompts, workflows, models, customers, retries, and request paths drive financial costs. Through the ZenLLM SDK, teams can send request-level telemetry, integrating essential business context, such as workflow, owner, customer, team, or product feature, while avoiding the storage of prompt or response content. The platform also monitors token usage, model choices, latency, errors, retries, and total expenses, uncovering inefficient patterns that are often concealed in provider dashboards. It is adept at detecting instances of context buildup when conversations or agents repeatedly transmit lengthy histories, unnecessary reliance on premium models for low-risk tasks, retry loops that incur additional costs, outdated system prompts, routing mistakes, anomalies, and a general lack of accountability regarding expenditures. Moreover, ZenLLM provides teams with the insights needed to make strategic decisions that can greatly improve cost-effectiveness in their operations involving LLM applications. By leveraging these capabilities, organizations can foster a culture of financial awareness and efficiency, ultimately leading to better resource allocation and project outcomes.

DueDel is a comprehensive AI-driven due diligence and risk management platform built to help financial institutions research entities faster, detect hidden risks, and produce high-quality intelligence at scale. Using advanced machine learning and NLP techniques, DueDel scans vast volumes of news, filings, sentiment data, litigation history, and stakeholder relationships to surface issues that could impact investment or compliance decisions. The workflow is simple: enter the target entity, apply relevant keywords, and launch an automated assessment that produces a consolidated report within minutes. These reports include sentiment shifts, reputational risk indicators, early warnings, and structured visual summaries crafted for executives and decision-makers. DueDel’s automation reduces manual research from days to minutes while improving accuracy by removing human bias and uncovering non-obvious patterns. Investors, analysts, and compliance teams benefit from consistent, data-backed insights that fit seamlessly into their existing operational systems. Its founders bring years of experience from AI research labs in Singapore, working at the intersection of finance, AI safety, and regulatory compliance. In addition to DueDel Risk Assessment, the team is also developing a unified AI Guardrails Platform aligned with SEBI and RBI guidelines to help fintechs adopt safe and compliant AI systems. Recognized at the Global FinTech Fest 2025, the platform has gained traction among top financial institutions. DueDel is shaping the future of risk intelligence by bringing research-driven AI from the lab into real-world dealmaking, underwriting, and compliance workflows.

AICtrlNet goes beyond simple monitoring of AI governance by actively engaging in its implementation. Unlike other platforms that merely evaluate or supervise AI risks, AICtrlNet takes the lead in orchestrating AI agents, involving human participants as essential contributors rather than just approval checkpoints, and integrating enterprise systems into a unified governance framework. The platform is versatile, accommodating various models such as OpenAI, Claude, Gemini, and local runtimes like Ollama and vLLM. It employs a 6-phase Control Spectrum that delineates autonomy levels for diverse workflows and agents. Additionally, it boasts a library of 43 agent templates designed for specific roles alongside over 177 workflow templates that cover 41 different industry sectors. Rather than replacing existing automation solutions, it collaborates with tools like n8n, Zapier, and Make as integral nodes within its system. AICtrlNet emphasizes compliance by supporting regulatory standards such as HIPAA, GDPR, SOC2, and the EU AI Act, thanks to its built-in governance and accountability features. The software is offered in open-core editions: the Community edition, which is MIT-licensed, free, and self-hostable; the Business edition, which enhances governance and risk evaluation through machine learning capabilities; and the Enterprise edition, which adds multi-tenancy and federation functionalities. Users can easily engage with the platform via the HitLai visual no-code interface, REST API, or MCP, providing flexibility and user-friendliness. With its comprehensive features, AICtrlNet is set to transform the AI governance landscape by fostering a proactive approach while ensuring that all elements work in harmony to achieve compliance and effectiveness.

JetStream Security operates as a governance platform that prioritizes security, enabling businesses to attain thorough visibility, control, and accountability over their AI systems by transforming them from vague, fragmented applications into well-managed and traceable infrastructures. Acting as a centralized control hub, it merges identity management, operational governance, monitoring, and financial oversight into a single, integrated system, which allows enterprises to “track every AI action, link actions to responsible individuals, and ensure that processes remain within authorized boundaries” while enforcing policies in real-time. Additionally, it features agentic identity, which connects human, agentic, and non-human identities to particular actions and access permissions, guaranteeing that every invocation, tool utilization, or workflow can be monitored and regulated in accordance with least-privilege access principles. By ensuring continuous runtime governance, JetStream consistently assesses real AI behavior against established frameworks and employs immutable logging along with real-time monitoring to detect inconsistencies, thereby strengthening security and compliance measures. This comprehensive strategy not only improves accountability but also aids organizations in effectively managing the intricacies associated with AI governance, ultimately fostering a more secure and compliant operational environment. As a result, businesses can confidently optimize their AI usage while adhering to the necessary regulations and best practices.