ML Junction
ML Junction gives developers a consistent API for working with multiple AI model providers. It supports OpenAI and Anthropic SDK compatibility, capability-aware routing, retries and fallback, and request-level cost and privacy controls.
Its per-request receipts explain which route was selected, the alternatives considered, the policies enforced and the final cost. ML Junction also supports model evaluations, agent observability, and a native LangChain integration.
Pricing is usage-based on prepaid credits: provider rates at cost plus one routing fee, or bring your own provider keys. It is designed for teams building AI applications.
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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.
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AI Cost Board
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.
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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.
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