AssemblyAI vs Lambda Pricing (2026)

How do these two stack up on price? Here's what each one costs, what you get, and where the value sits.

AssemblyAI Lambda
Starts at Custom Custom
Number of plans 2 22
Free plan —
Free trial —
Pricing model usage-based usage-based

AssemblyAI

Official page

Pay as you go

$0/mo
  • Universal-3.5 Pro ($0.21/hr)
  • Universal-2 ($0.15/hr)
  • Universal-3.5 Pro Realtime ($0.45/hr)
  • Universal-Streaming ($0.15/hr)
  • Universal-Streaming Multilingual ($0.15/hr)
  • Sync API ($0.45/hr)
  • Voice Agent API ($4.50/hr)
  • Speaker Diarization ($0.02/hr pre-recorded, $0.12/hr realtime)
  • Medical Mode ($0.15/hr)
  • Keyterms Prompting
  • Prompting ($0.05/hr)
  • Speaker Identification ($0.02/hr)
  • Translation ($0.06/hr)
  • Custom Formatting ($0.03/hr)
  • Entity Detection ($0.08/hr)
  • Sentiment Analysis ($0.02/hr)
  • Auto Chapters ($0.08/hr)
  • Key Phrases ($0.01/hr)
  • Topic Detection ($0.15/hr)
  • Summarization ($0.03/hr)
  • Profanity Filtering ($0.01/hr)
  • PII Audio Redaction ($0.05/hr)
  • PII Text Redaction ($0.08/hr)
  • Content Moderation ($0.15/hr)
  • LLM Gateway
  • No minimum commitments
  • No credit card required to start

Custom

Custom
  • Custom rate limits
  • Enhanced concurrency
  • Enterprise-grade flexibility
  • Volume-based pricing
  • Custom starting concurrency limits

Lambda

Official page

NVIDIA B200 SXM6 (8x)

Custom
  • VRAM/GPU: 180 GB
  • vCPUs: 208
  • RAM: 2900 GiB
  • Storage: 22 TiB SSD

NVIDIA H100 SXM (8x)

Custom
  • VRAM/GPU: 80 GB
  • vCPUs: 208
  • RAM: 1800 GiB
  • Storage: 22 TiB SSD

NVIDIA A100 SXM 80GB (8x)

Custom
  • VRAM/GPU: 80 GB
  • vCPUs: 240
  • RAM: 1800 GiB
  • Storage: 19.5 TiB SSD

NVIDIA A100 SXM 40GB (8x)

Custom
  • VRAM/GPU: 40 GB
  • vCPUs: 124
  • RAM: 1800 GiB
  • Storage: 5.8 TiB SSD

NVIDIA Tesla V100 (8x)

Custom
  • VRAM/GPU: 16 GB
  • vCPUs: 88
  • RAM: 448 GiB
  • Storage: 5.8 TiB SSD

NVIDIA B200 SXM6 (4x)

Custom
  • VRAM/GPU: 180 GB
  • vCPUs: 104
  • RAM: 1440 GiB
  • Storage: 11 TiB SSD

NVIDIA H100 SXM (4x)

Custom
  • VRAM/GPU: 80 GB
  • vCPUs: 104
  • RAM: 900 GiB
  • Storage: 11 TiB SSD

NVIDIA A100 PCIe (4x)

Custom
  • VRAM/GPU: 40 GB
  • vCPUs: 120
  • RAM: 900 GiB
  • Storage: 1 TiB SSD

NVIDIA A6000 (4x)

Custom
  • VRAM/GPU: 48 GB
  • vCPUs: 56
  • RAM: 400 GiB
  • Storage: 1 TiB SSD

NVIDIA B200 SXM6 (2x)

Custom
  • VRAM/GPU: 180 GB
  • vCPUs: 52
  • RAM: 720 GiB
  • Storage: 5.5 TiB SSD

NVIDIA H100 SXM (2x)

Custom
  • VRAM/GPU: 80 GB
  • vCPUs: 52
  • RAM: 450 GiB
  • Storage: 5.5 TiB SSD

NVIDIA A100 PCIe (2x)

Custom
  • VRAM/GPU: 40 GB
  • vCPUs: 60
  • RAM: 450 GiB
  • Storage: 1 TiB SSD

NVIDIA A6000 (2x)

Custom
  • VRAM/GPU: 48 GB
  • vCPUs: 28
  • RAM: 200 GiB
  • Storage: 1 TiB SSD

NVIDIA B200 SXM6 (1x)

Custom
  • VRAM/GPU: 180 GB
  • vCPUs: 26
  • RAM: 360 GiB
  • Storage: 2.75 TiB SSD

NVIDIA GH200 (1x)

Custom
  • VRAM/GPU: 96 GB
  • vCPUs: 64
  • RAM: 432 GiB
  • Storage: 4 TiB SSD

NVIDIA H100 SXM (1x)

Custom
  • VRAM/GPU: 80 GB
  • vCPUs: 26
  • RAM: 225 GiB
  • Storage: 2.75 TiB SSD

NVIDIA H100 PCIe (1x)

Custom
  • VRAM/GPU: 80 GB
  • vCPUs: 26
  • RAM: 225 GiB
  • Storage: 1 TiB SSD

NVIDIA A100 SXM (1x)

Custom
  • VRAM/GPU: 40 GB
  • vCPUs: 30
  • RAM: 220 GiB
  • Storage: 512 GiB SSD

NVIDIA A100 PCIe (1x)

Custom
  • VRAM/GPU: 40 GB
  • vCPUs: 30
  • RAM: 225 GiB
  • Storage: 512 GiB SSD

NVIDIA A10 (1x)

Custom
  • VRAM/GPU: 24 GB
  • vCPUs: 30
  • RAM: 226 GiB
  • Storage: 1.3 TiB SSD

NVIDIA A6000 (1x)

Custom
  • VRAM/GPU: 48 GB
  • vCPUs: 14
  • RAM: 100 GiB
  • Storage: 512 GiB SSD

NVIDIA Quadro RTX 6000 (1x)

Custom
  • VRAM/GPU: 24 GB
  • vCPUs: 14
  • RAM: 46 GiB
  • Storage: 512 GiB SSD

AssemblyAI vs Lambda FAQ

Which one is cheaper?
One or both use custom pricing, so it depends on your specific needs.
Can I use either one for free?
AssemblyAI has a free plan. Lambda doesn't — you'll need to pay from day one.
How do they charge?
Both use a usage-based model, so the comparison is straightforward — it comes down to features and limits at each price point.
Which one is a better deal?
Depends on what you need. AssemblyAI: They're positioning as the developer-friendly, API-first alternative to Deepgram and Rev AI — competitive on price at scale but differentiated by the breadth of AI features (LLM Gateway, multichannel, etc.). The AWS Marketplace listing signals they're actively chasing enterprise procurement budgets. Lambda: Lambda positions as a serious alternative to hyperscalers (AWS, GCP, Azure) for ML teams who want raw GPU access without cloud markup or complexity. They're not the cheapest bare-metal option, but the no-egress-fees policy and 1-Click Clusters are a direct shot at teams burned by AWS networking costs.

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