Best tools for Cost Optimization

Intelligent model router that balances cost and quality across LLM providers

RouteLLM by LMSYS routes LLM requests to the most cost-effective model that can handle each query's complexity. It uses learned routing models to classify whether a query needs a powerful expensive model or can be handled by a cheaper alternative, reducing costs by up to 85% while maintaining quality. Supports OpenAI, Anthropic, and other providers through an OpenAI-compatible API.

FOCUS-native multi-cloud cost management and FinOps platform

Holori is a multi-cloud cost management platform built on the FOCUS billing data standard. It provides unified cost visibility across AWS, Azure, GCP, and other cloud providers with automated tagging, budget alerts, and optimization recommendations. Features interactive infrastructure diagrams that link architecture visualization directly to cost data for contextual spending analysis.

AI-powered autonomous cloud cost optimization for AWS

Zesty uses AI to automatically optimize AWS cloud costs by analyzing usage patterns and making real-time resource adjustments. It manages Reserved Instance and Savings Plan portfolios autonomously, right-sizes EC2 instances based on actual utilization, and optimizes EBS volumes and storage costs. Claims average 51% savings on AWS compute spend with no engineering effort required.

Agentic IaC platform with AI-powered Terraform code generation

ControlMonkey is an agentic Infrastructure as Code platform that uses AI to automatically generate Terraform code from existing cloud resources. It detects infrastructure drift, converts ClickOps changes into version-controlled Terraform, and enforces IaC-first governance. Raised $7M seed funding to build AI-powered infrastructure management for cloud-native teams.

Infrastructure as Code orchestration and governance platform

env0 is an IaC orchestration platform that manages Terraform, OpenTofu, Pulumi, and CloudFormation workflows with built-in governance, cost estimation, and drift detection. It provides self-service infrastructure provisioning with policy guardrails, automated plan approvals, and budget controls. Supports custom deployment flows with OPA-based policy enforcement and RBAC.

Kubernetes cost monitoring and optimization platform

Kubecost provides real-time cost monitoring and optimization for Kubernetes clusters. It allocates infrastructure costs to namespaces, deployments, pods, and labels with granular accuracy. Acquired by IBM, it has become the standard for K8s cost visibility. Features include savings recommendations, budget alerts, cluster right-sizing, and multi-cluster cost aggregation across AWS, GCP, and Azure.

Autonomous Kubernetes and GPU infrastructure optimization

ScaleOps provides autonomous real-time management of Kubernetes and GPU infrastructure, reducing cloud costs by up to 80 percent without manual configuration. Backed by 130 million in Series C funding at an 800 million dollar valuation, it serves enterprises including Adobe, Wiz, DocuSign, and Salesforce. The platform continuously rightsizes pods, optimizes replicas, manages nodes, and allocates GPUs based on live workload demand rather than static configurations.

Self-hosted UI and API for Ansible, Terraform, and scripts

Semaphore UI provides a web interface and REST API for running Ansible playbooks, Terraform and OpenTofu configurations, Bash scripts, and PowerShell commands from a centralized self-hosted platform. With over 13,000 GitHub stars and 2 million Docker pulls, it replaces AWX and manual terminal execution with a polished dashboard for scheduling, access control, notifications, and execution history across mixed infrastructure automation environments.

IaC orchestration layer for scaling Terraform and OpenTofu

Terragrunt is an infrastructure-as-code orchestration tool that wraps Terraform and OpenTofu to keep configurations DRY, manage remote state, and coordinate multi-module deployments. The 1.0 release introduced stacks, filters, run reports, and backward compatibility guarantees after 900+ releases and tens of millions of infrastructure deployments. It provides a thin orchestration layer that eliminates duplication across environments without replacing the underlying IaC tools.

Open-source control plane for AI workloads across multi-cloud GPU infrastructure

dstack is an open-source platform that orchestrates AI training and inference workloads across heterogeneous GPU infrastructure spanning multiple clouds, Kubernetes clusters, and bare-metal servers. It abstracts away cloud-specific APIs so teams define GPU requirements declaratively and dstack automatically provisions the cheapest available resources from AWS, GCP, Azure, Lambda, or on-premises hardware.

AI-native API gateway by Alibaba with MCP server hosting and LLM routing

Higress is an open-source AI-native API gateway developed by Alibaba that combines traditional API management with LLM-specific capabilities like token-based rate limiting, model routing, prompt caching, and MCP server hosting. Built on Envoy and Istio, it provides enterprise-grade traffic management while natively understanding AI workload patterns including streaming responses, long-lived connections, and multi-model fallback chains.

Cost-effective AI inference platform with 86+ models from $0.02/M tokens

DeepInfra is an AI inference platform offering 86+ LLM models with pricing starting at $0.02 per million tokens. Backed by $20.6M in funding including an $18M Series A from Felicis Ventures, it provides OpenAI-compatible endpoints for models including DeepSeek, Llama, and Mistral with pay-as-you-go pricing.

2x faster LLM fine-tuning with 70% less VRAM on a single GPU

Unsloth is an open-source framework for fine-tuning large language models up to 2x faster while using 70% less VRAM. Built with custom Triton kernels, it supports 500+ model architectures including Llama 4, Qwen 3, and DeepSeek on consumer NVIDIA GPUs. Unsloth Studio adds a no-code web UI for dataset creation, training observability, model comparison, and GGUF export for Ollama and vLLM deployment.

Run GitHub Actions 2x faster at half the cost on bare-metal gaming CPUs

Blacksmith is a drop-in replacement for GitHub-hosted runners that executes Actions on bare-metal gaming CPUs with higher single-core performance. Migration requires one line change in YAML. Features colocated warm caches, persistent Docker layer caching on NVMe, CI observability with log search, and Firecracker microVM isolation. SOC 2 Type II certified, pay-as-you-go at ~$0.004/min versus GitHub's $0.008/min.

Google's pretrained foundation model for zero-shot time-series forecasting

TimesFM is a pretrained time-series foundation model from Google Research that performs zero-shot forecasting on diverse datasets without task-specific training. It handles univariate and multivariate time series across domains including finance, logistics, energy, and infrastructure monitoring with accuracy competitive against traditional statistical methods like ARIMA and Prophet.

First commercially viable 1-bit LLMs that are 14x smaller and 8x faster

PrismML Bonsai delivers the first commercially viable 1-bit large language models with 8B, 4B, and 1.7B parameter variants. The 8B model runs in just 1GB of RAM versus 16GB for standard FP16 models, achieving 44 tokens per second on iPhone. Backed by $16.25M from Khosla Ventures and released under Apache 2.0, Bonsai makes capable LLMs practical for edge devices and resource-constrained environments.

Find which AI models actually run on your hardware in one command

llmfit is a Rust-based terminal tool that matches over 200 LLM models from 30+ providers against your exact hardware specs. The interactive TUI scores each model on fit, speed, VRAM usage, and context length, helping you avoid downloading models that won't run on your machine. It supports Ollama, llama.cpp, MLX, Docker Model Runner, and LM Studio backends.

Kubernetes-native distributed LLM inference stack

llm-d is an open-source Kubernetes-native stack for distributed LLM inference with cache-aware routing and disaggregated serving. It separates prefill and decode stages across different GPU pools for optimal resource utilization, routes requests to nodes with warm KV caches, and integrates with vLLM as the serving engine. Apache-2.0 licensed with 2,900+ GitHub stars.

Serverless vector and full-text search on object storage

turbopuffer is a serverless vector and full-text search engine built on object storage that delivers 10x lower costs than traditional vector databases. Used by Anthropic, Cursor, Notion, and Atlassian for production search workloads. Manages 2+ trillion vectors across 8+ petabytes with automatic scaling and no infrastructure management. Funded by Thrive Capital.

Open-source LLM gateway with built-in optimization and A/B testing

TensorZero is an open-source LLMOps platform in Rust that unifies an LLM gateway, observability, prompt optimization, and A/B experimentation in a single binary. It routes requests across providers with sub-millisecond P99 latency at 10K+ QPS while capturing structured data for continuous improvement. Supports dynamic in-context learning, fine-tuning workflows, and production feedback loops. Backed by $7.3M seed funding, 11K+ GitHub stars.

Kubernetes-native cloud infrastructure control plane

Crossplane is a CNCF Graduated open-source project that extends Kubernetes to manage cloud infrastructure through declarative APIs. Platform teams compose custom infrastructure abstractions as Compositions and publish them as self-service APIs. It provisions resources across AWS, Azure, GCP, and 200+ providers directly from kubectl. Used by 450+ organizations with 11,000+ GitHub stars.

Cloud cost estimates for Terraform changes in pull requests

Infracost shows cloud cost changes directly in pull requests before Terraform resources are deployed. It calculates the cost impact of infrastructure changes across AWS, Azure, and GCP, displaying diffs in GitHub, GitLab, Bitbucket, and Azure DevOps comments. 12,200+ GitHub stars, Apache 2.0 licensed. Used by GitLab, HelloFresh, JPMorgan Chase, BMW, and Accenture. Integrates with CI/CD pipelines to catch cost surprises before they hit production.

Run AI workloads on any cloud with automatic cost optimization

SkyPilot is an open-source framework for running LLMs, AI, and batch jobs on any cloud with automatic cost optimization. It supports AWS, GCP, Azure, Lambda Cloud, and more, automatically selecting the cheapest available GPUs and managing spot instance preemption. Features include multi-cloud job scheduling, managed spot jobs with automatic recovery, and cluster autoscaling with 6,000+ GitHub stars.

AI group-buying for AWS cost reduction

Pump is a YC-backed platform that uses AI and group-buying power to automate AWS cost reduction, claiming up to 60% savings on compute through collective purchasing of Reserved Instances and Savings Plans. By pooling demand across multiple customers, Pump negotiates volume discounts that individual organizations cannot access, providing enterprise-level pricing to startups and mid-market companies.