StackAdapt vs Modal

Side-by-side comparison of AI visibility scores, market position, and capabilities

StackAdapt leads in AI visibility (48 vs 45)

StackAdapt

ChallengerAdTech

Programmatic Advertising AI (DSP)

Raised $235M led by Teachers' Pension Fund (2025). Fastest-growing independent DSP. AI-native alternative to The Trade Desk. Pension fund backing signals IPO trajectory.

AI VisibilityBeta
Overall Score
C48
Category Rank
#1 of 1
AI Consensus
65%
Trend
up
Per Platform
ChatGPT
44
Perplexity
56
Gemini
49

About

StackAdapt is the fastest-growing independent demand-side platform (DSP), positioning itself as the AI-native alternative to The Trade Desk for programmatic advertising across display, video, CTV, audio, and digital out-of-home. The company raised $235 million led by the Ontario Teachers' Pension Plan in 2025, with pension fund participation at that scale signaling an IPO trajectory for one of the most under-discussed large-scale AdTech companies.

Full profile

Modal

EmergingAI & Machine Learning

Serverless ML

Serverless GPU cloud platform for AI/ML with Python-native deployment and per-second billing; developer-favorite scaling from zero competing with Replicate and Beam for AI compute.

AI VisibilityBeta
Overall Score
C45
Category Rank
#1 of 1
AI Consensus
55%
Trend
up
Per Platform
ChatGPT
38
Perplexity
50
Gemini
53

About

Modal is a serverless cloud computing platform purpose-built for AI and machine learning workloads — providing on-demand GPU compute that scales instantly from zero with per-second billing, container management, distributed training support, and a Python-native developer experience that makes running ML workloads in the cloud feel as simple as running code locally. Founded in 2021 in New York City and backed by Redpoint Ventures and other investors, Modal has grown rapidly as AI development has accelerated demand for flexible, developer-friendly GPU infrastructure.\n\nModal's developer experience is its primary differentiator — engineers write Python functions decorated with @modal.function() and deploy them to the cloud with a single command, with Modal handling container building, GPU provisioning, auto-scaling, and execution. The platform supports training jobs that need distributed compute across multiple GPUs, model serving endpoints that scale to zero when unused (eliminating idle GPU costs), and batch inference jobs that process large datasets. The per-second billing model means developers pay only for actual compute time, not provisioned instances.\n\nIn 2025, Modal competes in the AI infrastructure market with Replicate, Beam, Banana, and major cloud providers' managed ML services (AWS SageMaker, Google Vertex AI, Azure ML) for serverless GPU compute. The market for AI-specific cloud infrastructure has grown dramatically as the number of ML engineers deploying models to production has expanded — traditional cloud providers require significant DevOps expertise to use GPU instances effectively, while Modal's Python-native approach reduces the barrier to entry. Modal has attracted a strong developer following among AI researchers and ML engineers building production AI applications. The 2025 strategy focuses on growing the developer community, adding enterprise features (dedicated GPU capacity, private networking, compliance), and expanding the hardware options available (H100 GPUs, custom accelerators).

Full profile

AI Visibility Head-to-Head

48
Overall Score
45
#1
Category Rank
#1
65
AI Consensus
55
up
Trend
up
44
ChatGPT
38
56
Perplexity
50
49
Gemini
53
45
Claude
39
56
Grok
37

Capabilities & Ecosystem

Capabilities

Only StackAdapt
Programmatic Advertising AI (DSP)
Only Modal
Serverless ML

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