4.7 out of 5 stars
(3 Ratings)
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
Learn more about FinOpsly
5.0 out of 5 stars
(2 Ratings)
Checksum is a continuous quality platform that autonomously generates, runs, and maintains tests so engineering teams can ship AI-generated code without trading speed for reliability.
Unlike copilots that wait for prompts, Checksum works as a background agent, detecting what needs testing, generating production-ready Playwright, and healing broken tests automatically. Seventy percent of failures resolve autonomously, keeping suites green without manual effort.
Built on fine-tuned data from 1.5+ million test runs, Checksum covers every layer of the SDLC: end-to-end, API, and CI testing from a single platform. Tests are delivered as standard Playwright code, submitted as a PR to your repo. No vendor lock-in.
Checksum integrates natively with Cursor, Claude Code, and 100+ coding agents via /checksum slash commands, so code is tested before a human ever reviews it. AI handles generation and healing on Checksum's cloud: no LLM tokens.
The result: ship faster, with confidence.
Learn more about Checksum.ai
Claude Haiku 5.5
5.0 out of 5 stars
(1 Rating)
Claude Haiku 5.5 is an Anthropic AI model designed for high-volume, latency-sensitive workloads such as classification, routing, extraction, and subagent tasks. The model supports adaptive thinking, allowing it to determine when and how much reasoning to perform based on the request. Developers can use the effort parameter to balance response quality against speed and cost, while adaptive thinking is enabled by default. Claude Haiku 5.5 provides a 1 million token context window and supports outputs of up to 128,000 tokens, increasing from the 200,000-token context window and 64,000-token output limit of Claude Haiku 4.5. It also supports browser use through the Claude API and Google Cloud for workflows that require interaction with web-based environments. Claude Haiku 5.5 uses Anthropic's newer tokenizer introduced with Claude 4.7 and later models, which results in the same text using approximately 30% more tokens than with Claude Haiku 4.5.
Learn more about Claude Haiku 5.5