Audience Loop vs Laguna by Poolside: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Audience Loop and Laguna by Poolside — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Audience Loop
iCustomer.ai
An AI audience team in a spreadsheet that enriches, matches, and syncs audiences to Meta, Google, LinkedIn, and TikTok to boost match rates.
Key features
- Spreadsheet-First Workflow: Operates within a familiar spreadsheet interface to prepare, inspect, and manipulate audience lists without requiring engineering resources.
- Data Enrichment: Appends additional attributes and identifiers to raw contact lists to improve coverage and targeting precision before platform upload.
- Identity Matching: Performs intelligent matching and normalization of identifiers (emails, phones, hashed IDs) to increase platform match rates and reduce lost contacts.
- Platform Syncing: Directly syncs prepared audiences to major ad platforms (Meta, Google, LinkedIn, TikTok) for one-step activation of campaigns.
- Match Rate Optimization: Provides tooling and processes specifically aimed at boosting match rates and thereby reducing CAC for paid campaigns.
- Rapid Launch Capabilities: Streamlines the audience prep-to-sync pipeline so teams can launch campaigns faster without custom engineering or lengthy IT processes.
- Spreadsheet-first interface for audience management and editing
- Record enrichment to append attributes and identifiers
- Identifier matching to improve platform match rates
- Direct sync to advertising platforms: Meta (Facebook), Google, LinkedIn, TikTok
- Rapid audience creation and deployment ('ship audiences in minutes')
- Focus on reducing CAC through better targeting
- Cross-platform audience management and syncing
Best for
- CRM Upload Enhancement: Enrich and normalize a CRM export to maximize match rates before uploading as custom audiences to Meta and Google.
- Remarketing Audience Preparation: Clean and segment website or app user lists in the spreadsheet, then sync segments to ad platforms for tailored remarketing.
- Lookalike Seed Optimization: Improve quality of seed audiences by enriching and deduplicating lists to create higher-performing lookalike audiences.
- Cross-Platform Campaign Activation: Build a single audience definition and push synchronized segments to multiple ad platforms for consistent cross-channel targeting.
- CAC Reduction: Increase match coverage and targeting precision to lower wasted ad spend and reduce customer acquisition cost in paid campaigns.
- Rapid Campaign Testing: Quickly prepare and deploy multiple audience variations from spreadsheet data to A/B test targeting strategies across platforms.
- Enrich CRM lists and sync segments to ad platforms for targeted campaigns
- Improve match rates for paid media to increase delivery and reduce waste
- Rapidly launch lookalike and retargeting audiences across Meta, Google, LinkedIn, and TikTok
- Cleanse and standardize audience data in a spreadsheet before activation
- Coordinate cross-platform audience strategies from a single workflow
Laguna by Poolside
Poolside
Poolside's family of open Mixture-of-Experts foundation models for agentic coding — XS.2 runs locally, M.1 reaches 72.5% on SWE-bench Verified.
Key features
- Two Model Sizes: Laguna XS.2 (33B total / 3B active) and Laguna M.1 (225B total / 23B active) target different latency and capability needs.
- Mixture-of-Experts Architecture: Routes each token through a subset of experts for efficiency at large scale.
- Local Deployment: XS.2 is small enough to run on a Mac with 36 GB of RAM via Ollama under an Apache 2.0 license.
- Strong SWE-bench Results: XS.2 hits 68.2% and M.1 reaches 72.5% on SWE-bench Verified.
- Bundled Coding Agent: Ships 'pool,' a lightweight terminal-based coding agent.
- Agent Client Protocol: Includes a dual ACP client-server used internally for agent RL training and evaluation.
Best for
- Local Agentic Coding: Running XS.2 on a laptop for private, offline code generation and editing.
- High-Capability Code Tasks: Using M.1 for harder, long-horizon software engineering work.
- Self-Hosted Deployments: Building on open weights to avoid third-party API dependencies.
- Research & Fine-Tuning: Adapting permissively licensed weights for custom coding workflows.
- Benchmarking: Evaluating agentic coding performance against SWE-bench Verified and Pro.