βΈ» Serra Labs Platform βΈ»
Start free. Scale when you're ready. Every plan includes all three optimization modes β cost, value, and performance β across AI and traditional workloads.
π° Maximize Savings
βοΈ Maximize Value
β‘οΈ Maximize Speed
Simple plans. No surprises.
What's Included
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1 workload β full capabilities on a single workload of your choice.
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All three optimization modes β Maximize Savings, Value, and Speed
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Workload classification β AI/GPU or traditional/CPU
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Performance-guaranteed right-sizing
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Resource health analysis
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Lifecycle mode management
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Optimal parking & cleanup
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Dashboards & reports
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APIs
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Free initial consultation
Limited to one workload. Upgrade to Standard for unlimited workloads across your full environment.
Everything in free, Plus
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Unlimited workloads β full optimization across your entire cloud environment.
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AI and traditional workloads β both workload types, full lifecycle management
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AWS and Microsoft Azure β multi-cloud across both platforms
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NVIDIA GPU support β full GPU-aware configuration for AI workloads
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Advanced dashboards with cost-performance efficiency tracking
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Full API access for integration into your workflows
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Dedicated onboarding and support
Pricing scales with workload volume. Reach out and we'll scope the right plan for your environment.
MSPs, Neo Cloud providers, and hyperscalers
Serra Labs can be embedded into your platform or advisory service β giving your customers workload-aware optimization across AI and traditional workloads, at every lifecycle stage. If you're building a cloud optimization service, let's talk.
Common questions
What's the difference between cost optimization and cost-cutting?
Cost optimization means finding the lowest cost configuration that still delivers a defined level of performance. The performance floor is a hard constraint. Cost-cutting ignores performance effects and trades a lower bill for degraded applications, missed SLAs, and engineering overhead β costs that don't appear on an invoice but are real. Serra Labs always holds performance as a constraint while minimizing spend within it.
Does Serra Labs handle both AI and traditional workloads?
Yes. AI and traditional workloads have fundamentally different economics β GPU compute scales near-linearly with throughput while CPU compute delivers diminishing returns β and they require different optimization strategies. Serra Labs classifies each workload by type and lifecycle stage, then applies the right mode for each. Both workload types are supported on every plan.
What does "lifecycle mode management" mean in practice?
For AI workloads, the right optimization mode shifts at each lifecycle stage: cost optimization in prototyping (architecture is exploratory, budget should be lean), value optimization in testing and validation (results need to reflect production conditions without full production spend), and performance optimization in production (where throughput and latency directly drive business outcomes). Serra Labs applies these transitions automatically as workloads mature.
How does GPU-aware optimization work?
For AI workloads, Serra Labs evaluates GPU cores, VRAM, and memory bandwidth together β not just core count or hourly rate. A configuration that appears cheaper but stalls on VRAM constraints or memory bandwidth delivers more cost per result, not less. The platform searches potentially millions of configurations to find the genuine optimum for the workload's actual requirements.
How long does it take to get started?
The Free Plan is available immediately β no credit card required. Connect your AWS or Azure account, select a workload, and the platform begins collecting utilization and health data. Initial recommendations are typically available within minutes of data collection starting.