SatGate Alternatives

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

Formal is a protocol-aware reverse proxy that secures access to databases, APIs, infrastructure, and AI tools by enforcing least privilege at the wire-protocol level. Deployed as a single stateless binary in a VPC through Terraform, Kubernetes, or Docker, it sits between identities and resources without application changes, SDKs, or agents. Formal parses more than 15 protocols, including PostgreSQL, MySQL, MongoDB, Snowflake, SSH, Kubernetes, HTTP, MCP, S3, Redis, RDP, BigQuery, ClickHouse, and DynamoDB, allowing query-level decisions instead of generic network filtering. Policies can authenticate and authorize users, mask or filter fields, rewrite requests, block actions, require MFA, quarantine sessions, suspend access, or support impersonation across session, request, and response stages. Teams can secure AI agents and MCP servers by stripping PII before it reaches a model, blocking unauthorized tool calls, and auditing every action.

asqav is an AI governance and security platform designed to make AI agents audit-ready by providing real-time monitoring, enforcement, and verifiable proof of every action taken by an agent. It introduces a lightweight SDK that allows developers to integrate governance directly into their agents in just a few lines of code, enabling continuous oversight across the full lifecycle of AI operations. It includes behavioral monitoring to detect issues such as drift, rate limits, and scope violations, along with advanced threat detection that identifies prompt injections, exposure of sensitive data, toxic outputs, and other risks. It enforces policy through configurable “policy gates,” which apply per-agent rules, preflight checks, and dynamic approvals before actions are executed, ensuring that agents operate within defined boundaries. asqav also provides automated incident response capabilities, including the ability to suspend, quarantine, or escalate risky agents.

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.

Scalekit is an authentication platform for AI agents that enables secure, user-delegated access to SaaS applications, APIs, databases, and MCP servers. Rather than relying on shared service accounts, Scalekit allows agents to perform actions on behalf of individual users using their own identities, permissions, and approved scopes. The platform manages OAuth flows, credential storage, authorization, token refresh, and tool execution behind the scenes, simplifying agent development. It includes over 100 connectors, support for custom integrations, and compatibility with popular AI frameworks. Scalekit also provides detailed auditing, encrypted credential storage, and enterprise deployment options for production environments. By handling the authentication and authorization infrastructure, Scalekit helps developers build secure AI agents that integrate with external systems at scale.

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.

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.

Fluq is an AI agent observability and orchestration platform designed to give teams full visibility and control over how their AI agents operate in real time. It acts as a centralized “single pane of glass” where every agent action, LLM calls, tool usage, file operations, token consumption, and associated costs are tracked and visualized through detailed waterfall traces. By routing all agent requests through a lightweight proxy, Fluq requires minimal setup and works with any LLM provider or agent framework, allowing organizations to integrate it into existing systems without modifying code. It enables teams to inspect each decision an agent makes, drill into execution steps, and understand exactly how outcomes are generated, improving transparency and debuggability. It also includes governance features such as policy enforcement, spend limits, approval gates, and access controls, helping prevent issues like runaway costs, misuse of tools, or inaccurate outputs.

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.

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)

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.

Waterfall is a credit infrastructure for platforms building on large language models, designed to turn AI usage into a business model without requiring teams to build their own billing stack. It gives each user, agent, or team a stablecoin-backed credit wallet, then meters every model call by provider, model, token count, and cost. Requests can be routed through the Waterfall Gateway or integrated through TypeScript and Python SDKs, with usage attributed to the correct wallet in real time. Each API call settles atomically against the wallet as it happens, so credits decrease, and revenue is recognized per request instead of through delayed invoices and manual reconciliation. Waterfall supports more than 300 models across providers such as OpenAI, Anthropic, DeepSeek, and xAI, allowing products to use multiple AI services while maintaining one accounting layer.

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.

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.

Navarch is the command layer for teams running AI coding agents. Crews plan, build, review, QA and ship real software across several repos at once, while you set priorities, budgets and approval gates. Every task lands on an append-only ledger with its pull request, before/after screenshots and an independent review attached, so you judge the work from evidence instead of re-reading diffs. Your repos stay in GitHub, your agents run on your machines or ours.

Fleece AI is a delegative AI workspace. A no-code workflow automation platform that deploys autonomous AI agents to automate tasks across 3,000+ app integrations. Describe tasks in plain language — AI agents connect applications, map workflows, and execute automations end-to-end. Build hierarchical agent teams that mirror real organizations: assign a lead agent that delegates to specialized sub-agents, consolidates results, and delivers outputs — fully autonomous, no supervision required. Use cases include email triage, CRM updates, report generation, invoice processing, and cross-app data syncs.

TokenAtlas is an AI FinOps and cost intelligence platform that helps teams understand, forecast, and optimize AI costs before they become expensive. Users describe a workload by entering the model, input and output token volumes, request counts, and growth assumptions, and TokenAtlas prices the scenario against a maintained catalog of published API rates. The cost modelling dashboard brings configured workloads into one view, while model comparison places provider and model options side by side using transparent assumptions. What-if scenario planning shows the cost impact of launching a new prompt, agent, model swap, retrieval pipeline, or traffic increase before it reaches production. Cost risk analysis identifies the workloads most sensitive to changes in volume, prompt size, or model choice, and benchmark comparisons show how a modeled model mix compares with typical AI product and infrastructure profiles.

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.

Mindra is an agentic orchestrator for adaptive AI workflows, built around agent teams you can actually delegate to. Explain your task, and Mindra spins up a specialized agent team that works 24/7, talks to each other, and takes real action across your stack. Each team is a roster of specialists, one per channel, tool, or domain, sharing context, handing off tasks, and finishing the job together through phase-based workflows and real-time orchestration. Every action and retry is on the record, so when a tool fails or an API hits a limit, the agent reasons its way around it while keeping the whole chain auditable, replayable, and debuggable. Mindra agents do more than chat: they can pause campaigns, reduce spend, reinvest budgets, post summaries to Slack and email, and log every action with reversible controls. Teams can describe the work they need, and Mindra sketches a personalized orchestrator with the sub-agents and tools it would lean on.

Kastra is the authorization layer for AI systems, deciding what agents, models, and AI tools are allowed to do before they do it. It sits in the execution path of every prompt, tool call, shell command, database operation, and API request, evaluates each action against deterministic, attribute-based policy, and returns an allow, deny, redact, or escalate decision in under a millisecond. Unlike monitoring products that observe AI after it acts, Kastra blocks unauthorized behavior before it reaches a tool, API, database, or production system. Its unified control plane combines a policy engine, edge decision points, integrations, and a tamper-evident evidence vault that signs every decision for audit and replay. Kastra Edge brings local enforcement to developer machines, protecting Claude Code, Cursor, Codex CLI, and other coding agents from destructive commands, secret exfiltration, unsafe file writes, and unauthorized tool use.

Constellation Gate AI is a drop-in defense layer for AI agents, built to sit between the agent and the model while screening every request for attacks and leaks. Gate acts as an inline gateway for coding agents and model APIs, protecting workflows without requiring major code changes. Users can point existing tools such as Claude Code, Cursor, OpenClaw, Codex, or OpenCode at Gate and inherit prompt-injection defense, secret scanning, PII redaction, token optimization, and a verifiable audit trail. The platform is designed around three real risks: prompt injection, credential and PII leakage, and hijacked tool calls. Instead of relying on the model to defend itself, Gate blocks attacks before they reach the model, redacts secrets before responses return, and stops attacker-controlled tool outputs before an agent acts on them. Gate accepts the same calls an agent already makes, forwards them to the model, scans every call and response in both directions.

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.

The Knox API Gateway is designed as a reverse proxy with consideration for pluggability in the areas of policy enforcement, through providers and the backend services for which it proxies requests. Policy enforcement ranges from authentication/federation, authorization, audit, dispatch, hostmapping and content rewrite rules. Policy is enforced through a chain of providers that are defined within the topology deployment descriptor for each Apache Hadoop cluster gated by Knox. The cluster definition is also defined within the topology deployment descriptor and provides the Knox Gateway with the layout of the cluster for purposes of routing and translation between user facing URLs and cluster internals. Each Apache Hadoop cluster that is protected by Knox has its set of REST APIs represented by a single cluster specific application context path. This allows the Knox Gateway to both protect multiple clusters and present the REST API consumer with a single endpoint.

Pangolin is an open source, identity-aware tunneled reverse-proxy platform that lets you securely expose applications from any location without opening inbound ports or requiring a traditional VPN. It uses a distributed architecture of globally available nodes to route traffic through encrypted WireGuard tunnels, enabling devices behind NATs or firewalls to serve applications publicly via a central dashboard. Through the unified dashboard, you can manage sites and resources across your infrastructure, define granular access-control rules (such as SSO, OIDC, PINs, geolocation, and IP restrictions), and monitor real-time health and usage metrics. The system supports self-hosting (Community or Enterprise editions) or a managed cloud option, and works by installing a lightweight agent on each site while using the central control server to handle ingress, routing, authentication, and failover.

​LiteLLM is a versatile platform designed to streamline interactions with over 100 Large Language Models (LLMs) through a unified interface. It offers both a Proxy Server (LLM Gateway) and a Python SDK, enabling developers to integrate various LLMs seamlessly into their applications. The Proxy Server facilitates centralized management, allowing for load balancing, cost tracking across projects, and consistent input/output formatting compatible with OpenAI standards. This setup supports multiple providers. It ensures robust observability by generating unique call IDs for each request, aiding in precise tracking and logging across systems. Developers can leverage pre-defined callbacks to log data using various tools. For enterprise users, LiteLLM offers advanced features like Single Sign-On (SSO), user management, and professional support through dedicated channels like Discord and Slack.

Snapper is an AI agent security platform designed to provide end-to-end governance and protection for organizations deploying AI agents across applications, networks, and systems. It delivers runtime enforcement by evaluating every agent action, including tool calls, API requests, and data access, before execution through a policy-driven rule engine with multiple enforcement layers. It offers unified visibility into AI usage by monitoring network traffic, browser activity, DNS, and processes to detect unauthorized tools and “shadow AI,” while also intercepting outbound LLM requests through SDK wrappers and a network proxy to evaluate, redact, and log sensitive data in real time. Snapper includes advanced threat detection capabilities that identify prompt injection, exploit chains, anomalous behavior, and multi-step attack patterns using behavioral baselines, kill chain tracking, and composite trust scoring.

NewCore is a next-generation identity provider for humans and AI agents, built around one converged identity core that keeps every human and agentic identity visible, secured, and governed from access to audit. It treats employees, contractors, partners, machines, on-behalf-of actions, delegated agents, and autonomous workloads as first-class identities under one architecture. Identity Security protects the trust behind access with SSO, passkeys, phishing-resistant MFA, secure token signing, authenticator strength scoring, and security-aware policies. Secure Split Key distributes signing authority between NewCore’s cloud and the customer environment so neither side can sign a valid token alone. Identity Discovery maps humans, agents, accounts, applications, authenticators, sessions, and access paths into one live graph, revealing shadow accounts, orphaned credentials, and unmanaged agents.

Keelen is an autonomous coding loop. It turns free-form requests into a prioritized roadmap, breaks roadmap items into dev-ready tasks with acceptance criteria, implements each task in an isolated single-use VM using your own Claude, Codex, GLM, or Kimi credentials, and ships the result as a merged pull request, or holds it for review if that is how you configure it. Every change passes five verification gates before merge: a red-first test proof, an independent adversarial review of the diff by a model that shares no context with the run that wrote it, the project's own test suite on a clean checkout, a CI-green requirement, and a review window before gated auto-merge. The loop may never weaken a check to reach green. Each iteration runs in a single-use, non-root VM that is destroyed when the run ends, with deny-by-default network egress. Per-run GitHub tokens are scoped to one repository. Keelen is not an IDE copilot and not a chat agent.

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.

Meta Model API is a new developer API for building with Muse Spark 1.1, Meta’s multimodal reasoning model built for agentic tasks, coding, tool use, computer use, and multimodal understanding. Now in public preview, it gives developers a way to access Muse Spark 1.1 through an OpenAI-compatible package, making it easier to point existing clients at the API, keep the same code structure, and set the model to muse-spark-1.1. Muse Spark 1.1 is designed for personal agentic tasks that require planning and orchestration across external apps and services, with the ability to generalize to new native tools, MCP servers, and custom skills. As a main agent, it can gather context, make a plan, and delegate execution across parallel subagents; as a subagent, it follows its role, understands available tools, and knows when to escalate back. The model can actively manage a 1 million-token context window, remember actions, retrieve information from much earlier work, and compact context.

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.

CloudQuell is a cost management platform built for teams whose spend no longer sits in one place. It ingests AWS billing data daily through a scoped read-only cross-account IAM role, and connects OpenAI, Anthropic, and Snowflake from the Integrations page. On top of that it supports cost centers, allocation rules, tags, and multi-account cost views, so spend can be attributed to the team or product that caused it. Anomaly detection, budgets, and alert delivery flag problems as they develop, and ranked savings recommendations show where the money is. Every tier receives a weekly accrued-cost recap email.

Reasonix is an open source coding agent designed for long autonomous sessions that remain readable, auditable, and reversible. One local engine powers four interfaces: terminal, desktop app, browser, and ACP-compatible editors, with sessions, permissions, skills, and MCP servers shared across them. Plan mode holds every write until the proposed steps are reviewed and approved, while reads, writes, and shell commands are separately gated and constrained by a workspace sandbox. Each turn creates a checkpoint outside Git, allowing users to rewind a long run without affecting commit history. MCP support over stdio, SSE, and streamable HTTP merges external tools into one registry, while Markdown skills and isolated subagents extend the agent without requiring a fork. Reasonix maps a codebase once and keeps that map throughout the session, letting users queue tasks, review diffs, and resume work without losing context.

Osaurus is a native AI harness for macOS that lets users run open models entirely on their Mac, connect cloud models when greater capability is needed, and carry shared memory across them. Built in Swift for Apple Silicon, it works fully offline with local models through Ollama, MLX, or LM Studio, keeping conversations, code, files, settings, agents, skills, and provider keys on the device unless the user explicitly chooses a cloud provider. A system-wide chat overlay provides quick access from any app, while separate agents can be created for coding, research, file organization, and other jobs, each with its own prompt, history, and memory. Osaurus distills past conversations into relevant facts, loads packaged skills when tasks require them, and gives agents scoped access to working folders, file search, Git, and other tools. Agents can run code in an isolated sandbox, delegate work to subagents, operate on schedules, respond to folder changes, use voice input, and generate images.

Harden AIF is an agent endpoint security platform for AI coding agents. It evaluates supported agent tool calls before execution using the developer’s intent, session context, organisational policy, and the effect of the proposed action. Harden helps protect against destructive commands, unauthorized access, unintended data transfers, secret exposure, privilege misuse, and other unsafe agent actions. Legitimate actions can proceed normally, sensitive data in supported flows can be safely redacted, and actions that fall outside the developer’s intent or authority are blocked before execution. Harden works across popular coding agents and agentic development tools including Claude Code, Codex, Cursor, Antigravity CLI, Kiro, Hermes, and OpenClaw, providing a consistent security layer across the agent ecosystem.

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.

Finout combines Cloud Providers, Data Warehouses, and CDNs into one mega bill, enabling an unparalleled business context view of your cloud spend with no heavy lifting in minutes. Monitor anomalies, view recommendations and forecast cost per growth. While AWS charges you by the instance, you genuinely care about your pod cost. With no-agent integration, utilize your existing Datadog or Prometheus to get a pod-level granularity of your spend in minutes. Forget about absolute cloud cost. See the cost of what you are utilizing and not only what you are paying for. For example, view Kubernetes pods instead of EC2 instances and DynamoDB indexes. Finout can give you one unified language the entire company can talk in, not only DevOps.

AI bug repair from real production traffic, run entirely in your cloud. Speedscale captures HTTP, gRPC, and SQL traffic via eBPF inside your Kubernetes cluster. Production payloads never leave your perimeter. When a failure occurs: the capture agent records the exact request, a triage agent classifies it, a reproduction agent replays it against your codebase, and a validation agent verifies the fix before the PR opens. Fully autonomous or human-gated. You supply the cloud, storage, and AI models (Anthropic, Llama, or your own). Speedscale orchestrates the loop with no cloud, model, or storage lock-in. proxymock: free CLI giving AI coding agents (Claude Code, Cursor, Copilot) real production context via MCP. No signup required. Meets PCI and HIPAA data-residency requirements. Used by Home Depot, IHG, FLYR, Cimpress/Vistaprint, Navitaire, and Ascension Health.

kgateway is a Kubernetes-native gateway platform designed to manage microservices and AI agent traffic at scale. It acts as a unified control plane for API gateways, AI gateways, inference routing, and agent-to-agent communication. Built on Envoy and open standards, kgateway implements the Kubernetes Gateway API for modern cloud-native environments. The platform enables centralized authentication, authorization, rate limiting, and traffic management. Kgateway also secures LLM consumption by controlling access to models, tools, and agents. It supports intelligent routing for AI inference workloads running in Kubernetes. Trusted by enterprises worldwide, kgateway delivers scalable, secure, and flexible connectivity across any cloud.

OpenWeave is execution governance for AI agents and autonomous systems — a server-enforced state machine that controls what AI agents can do and when. You define workflows as states, transitions, and who may trigger them; the backend enforces every transition with a hard 403, and critical states sit behind human approval gates that block bots until a human signs off. Monitoring tells you what agents did; OpenWeave prevents what they shouldn't do, before it happens. Agents discover allowed transitions from the API instead of hardcoding them, every bot has a verifiable identity, and every change is written to an immutable audit trail. Integrates over a REST API and a remote MCP server. Built for AI-agent developers, AgentOps/MLOps and platform teams.

RequestRocket's is a hyper scalable universal API authentication and authorization service. RequestRocket empowers organisations to centralise the management of access to third party systems, while offering the ability to down-scope permissions with fine grained access controls for any platform API. Our API's enable on demand configuration of authentication proxies across 6 continents allowing for low latency and high availability security improvements that comply with data sovereignty requirements.

OpenLegion is a production-grade AI agent framework and platform for building an AI workforce by describing the team you want. Tell OpenLegion “I want a marketing agency,” “I want a sales team,” or “I want a research desk,” and it deploys the agent stack with roles, budgets, permissions, and secure credential controls built in. Instead of stopping at chat, OpenLegion is designed for real workflows; agents can browse websites, fill out forms, write and run code, send emails and messages, manage files and folders, research and summarize, scrape data, qualify sales leads, process spreadsheets, post to social media, monitor for changes, and trigger workflows through Slack, Telegram, or Discord. Each agent runs in its own isolated container with per-agent budgets, tool permissions, persistent memory, MCP-compatible skills, and vault-secured credentials that agents never touch.

KYDE is the behavioral firewall for AI agents. KYDE makes AI agents trustworthy enough to hand them real responsibility. It prevents what an agent must not do, proves what it did, and keeps your knowledge yours. Not on the machine. Not in the agent. KYDE sits outside, in the request path between your agents and every LLM provider — every action intercepted, scoped, and signed before it executes. Outside the agent. Cannot be overridden. Zero code changes. One environment variable. Provider-agnostic, MCP-ready, <100 ms target latency. Three functions. One layer. PROVE — After the action. An audit trail that is architecturally independent of every LLM provider: Ed25519-signed, hash-chained, captured at the boundary, undeletable by any agent. The suspect can't write the police report. RATE — Across the network. The KYDE Trust Score™: agent behavior rated across every provider in the same currency. Vendors grade their own homework. We grade behavior.

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.

AstroFabric is an agentic AI operating system for growth, revenue and digital operations. Specialist AI agents cover SEO and AI visibility, advertising, outbound pipeline, competitive intelligence, creative, lifecycle, reporting and site operations, planning and executing complete missions through metered tools. Every run carries enforced budgets, approval-gated writes and a full transcript, so autonomy stays accountable. Teams can start with pre-built playbooks or write their own objectives, and work runs from the console, REST API, hosted MCP server, scheduled missions, agent email or an embeddable chat widget.

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.

Operator by Planck Proof is an agentic API penetration testing tool. Give it your OpenAPI spec and credentials for two or more roles; it tests every operation across roles and tenants for authorization flaws (BOLA, BFLA, BOPLA), broken authentication, injection, mass assignment and business-logic abuse, chaining findings into attack paths. Coverage maps to the OWASP API Security Top 10 and includes REST, GraphQL and gRPC APIs, plus the APIs behind AI agents, LLM apps and MCP servers. Every finding ships with the exact request and response, a CVSS score and a runnable proof-of-concept your engineers can execute to confirm the issue and verify the fix. Scope, rate and data controls keep runs safe against real environments, and you can steer or pause the agent at any time. Run it on every deploy, retest fixes, and send findings to Jira. Reports are built for engineers and auditors (SOC 2, PCI DSS, HIPAA). First scan is free; Pro and Enterprise are quoted per API by endpoint volume.

QCecuring SSL Certificate Lifecycle Management is an enterprise platform designed to automate the discovery, issuance, renewal, rotation, and monitoring of SSL and TLS certificates across hybrid and cloud environments. The solution centralizes certificate and machine identity visibility, replacing fragmented manual processes with a unified inventory that provides expiry alerts, ownership tracking, and policy enforcement. The platform discovers certificates through network scans, API integrations, and lightweight agents across public and private Certificate Authorities, servers, load balancers, appliances, and cloud services. Automated renewal and rotation workflows help prevent service outages caused by expired or unmanaged certificates. QCecuring integrates with ITSM, SIEM, orchestration tools, and DevSecOps pipelines to streamline operations and strengthen cryptographic governance. Role-based access controls enforce secure issuance, renewal, and revocation processes.

SEAOTTER is a managed control plane for Hermes Agent on Google Cloud. Isolated, always-on agents. No VPS. No SSH. You create an agent in the dashboard. SEAOTTER provisions a per-agent namespace with a gVisor sandbox, typically in a few minutes. Pause, restart, restore, and reprovision from the API or UI. Logs and metrics included. You do not SSH into a box. Secrets go in a write-only tab and are stored in Google Secret Manager. Hermes loads them at startup. Do not paste keys into chat. After signup, connect with an org-scoped so_ MCP key from Cursor, Claude, or Codex. Hermes is the live harness: tools, memory, cron. us-central1 is live. 7-day trial, no credit card, one agent only (1 CPU / 4Gi / 8Gi). Then $99 per always-on agent / month. Extra agents after convert at +$99. Custom is book-a-call.

Zenflow is an AI orchestration platform built to bring discipline and structure to AI-assisted software development by coordinating multiple AI agents in spec-driven workflows, enforcing planning, implementation, testing, and review steps so output stays aligned with defined requirements rather than ad-hoc prompting. It organizes repeatable processes that run on autopilot or with human review, with built-in automated verification and cross-agent quality gates to reduce errors and “AI slop.” Zenflow enables parallel execution of tasks in isolated environments, provides visibility into agent work via project management views, and supports pre-built workflows for features, bug fixes, and refactors that users can extend or customize. It anchors tasks to a single source of truth such as PRDs or architecture documents to prevent drift and scope creep, and coordinates agent diversity to catch blind spots across model families.