Cloptima
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.
Learn more
Edgee
Edgee is an AI gateway that sits between your application and large language model providers, acting as an edge intelligence layer that compresses prompts before they reach the model to reduce token usage, lower costs, and improve latency without changing your existing code. Applications call Edgee through a single OpenAI-compatible API, and Edgee applies edge-level policies such as intelligent token compression, routing, privacy controls, retries, caching, and cost governance before forwarding requests to the selected provider, including OpenAI, Anthropic, Gemini, xAI, and Mistral. Its token compression engine removes redundant input tokens while preserving semantic intent and context, achieving up to 50% input token reduction, which is especially valuable for long contexts, RAG pipelines, and multi-turn agents. Edgee enables tagging requests with custom metadata to track usage and spending by feature, team, project, or environment, and provides cost alerts when spending spikes.
Learn more
FinOps LLM
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.
Learn more
AWS Step Functions
AWS Step Functions is a serverless function orchestrator that makes it easy to sequence AWS Lambda functions and multiple AWS services into business-critical applications. Through its visual interface, you can create and run a series of checkpointed and event-driven workflows that maintain the application state. The output of one step acts as an input to the next. Each step in your application executes in order, as defined by your business logic. Orchestrating a series of individual serverless applications, managing retries, and debugging failures can be challenging. As your distributed applications become more complex, the complexity of managing them also grows. With its built-in operational controls, Step Functions manages sequencing, error handling, retry logic, and state, removing a significant operational burden from your team. AWS Step Functions lets you build visual workflows that enable fast translation of business requirements into technical requirements.
Learn more