The engineering excellence platform
Lizrd is built for engineering leaders and engineers — where FinOps, cloud monitoring, and engineering meet. From your real usage and billing data, it shows you exactly where your infrastructure money goes and why — then guides you, step by step with tailored diffs, through quick, low-risk optimizations.
Read-only. No changes to your infrastructure without your approval.
Recommendations Lizrd generates
eks · prod-workers
- instance_type = "m6g.2xlarge"+ instance_type = "m6g.xlarge"ecs · checkout-api
- cpu = 1024- memory = 2048+ cpu = 512+ memory = 1024ec2 · 14 × gp3
- resource "aws_ebs_volume" "cache_scratch" {- size = 200- }+ # removed — no instance references itrds · analytics-staging
- instance_class = "db.r6g.large"+ instance_class = "db.t4g.medium"Up to 50% of cloud spend is wasted. Finding it isn’t the hard part — fixing it, under real engineering priorities, is.
Where Three Worlds Meet
Lizrd sits where FinOps, cloud monitoring, and engineering meet — and turns that into one clear path for the people who build the infrastructure.
Every dollar, mapped — across clouds, accounts, resources, and the teams that own them.
The drivers behind each cost, with the evidence behind every opportunity — no black boxes.
Tailored, step-by-step diffs for quick, low-risk fixes — reduce cost without hurting your customers.
Simple enough for anyone to act. Every opportunity becomes a clear, copy-paste-ready change — effort, risk, and rollback spelled out — so even a less-experienced engineer can ship it with confidence.
New AI & GPU waste
GPU and inference spend is the fastest-growing line on most bills — and the easiest to overpay on. Lizrd reads real GPU utilization, endpoint traffic, and accelerator sizing to find idle and over-provisioned AI infrastructure, then hands you the exact fix. Same evidence, same confidence, same one-step guidance as the rest of your cloud.
Examples across AWS, GCP & Azure
sagemaker · fraud-scoring-v2
ec2 · model-training
- instance_type = "g5.12xlarge"+ instance_type = "g5.4xlarge"vertex-ai · recommender-v3
azure-ml · nightly-finetune
◆ Idle & underused GPU instances and managed inference endpoints
◆ Over-provisioned accelerators — right accelerator, right size
◆ Endpoints that should scale to zero between bursts of traffic
◆ Batch & fine-tuning jobs that belong on Spot GPUs
Across AWS, GCP, and Azure — SageMaker & Bedrock, Vertex AI, and Azure ML & OpenAI.
Transparency into cost
One clear picture of your spend across clouds, accounts, and resources — explore it as a breakdown, a tree map, an architecture view, or a trend over time. The visibility your finance and engineering teams have been missing.
Sized by monthly cost — the biggest tile is your biggest spend.
Total Cost Trend
Currently $19,800/mo across all connected clouds
Understanding of cost
Lizrd turns raw spend into ranked opportunities — the highest-value fixes first, each with the evidence behind it and a clear reason to act. No black-box guesses; you see exactly why every dollar is on the table.
eks · prod-workers
$4,800/mo
High
rds · analytics-staging
$1,180/mo
Medium
ecs · checkout-api
$640/mo
High
ec2 · 14 × gp3
$312/mo
High
Step-by-step guidance
Every opportunity comes with tailored, step-by-step guidance for your setup — copy-paste-ready code, an effort estimate, a risk read, and a rollback. Like a principal engineer looking over your shoulder, so any engineer can ship it quickly and with confidence.
eks · prod-workers
- instance_types = ["m6g.2xlarge"]- scaling_config {- desired_size = 8- min_size = 6- max_size = 16- }+ instance_types = ["m6g.xlarge"]+ scaling_config {+ desired_size = 5+ min_size = 3+ max_size = 12+ }Works With Your Stack
Point Lizrd at your clouds and code. It's strongest when your infrastructure is defined as code (Terraform and more): every recommendation ties to the exact file to change, so acting is a copy-paste diff. No infrastructure-as-code? Lizrd still finds the savings and guides you through them, step by step.
🔒 Lizrd reads only your infrastructure data — never your customers' data.
Trust by design
Lizrd connects read-only. We observe your environment — we never touch it.
Nothing changes without your explicit approval. No autonomous actions, ever.
Fully isolated per company, encrypted, and never used to train shared models.
Work with the founder
Book a founder-led infrastructure review — a prioritized, explained plan to cut your cloud bill. Starts with a free 30-minute consultation.
Start free in minutes, or take the self-guided sandbox for a spin first.