Ecrett Music vs Laguna by Poolside: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Ecrett Music and Laguna by Poolside — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Ecrett Music
Ecrett Music
Web-based tool that generates royalty-free music with mood/scene controls and downloadable licensed tracks for creators.
Key features
- Royalty-Free Track Generation: Instantly generates original background music from user inputs, producing tracks intended for royalty-free and commercial use.
- Customizable Moods and Scenes: Preset-driven controls let users select mood, scene, genre, and instrumentation to shape the emotional and stylistic character of each track.
- Adjustable Length and Structure: Users can specify track length and basic arrangement elements (intro, loop, outro) to fit video, podcast, or game timing requirements.
- Fast Preview and Export: Browser-based previewing of generated tracks with quick export options for immediate download and integration into projects.
- High-Quality Audio Downloads: Provides downloadable high-quality audio files suitable for editing and publishing across platforms and media.
- License-Focused Delivery: Supplies a simple licensing approach for generated music so creators can use tracks in monetized content with reduced licensing complexity.
- Web-based music generation with customizable parameters (genre, mood, length, instrumentation)
- Composer-grade API for programmatic music creation and retrieval
- Digital license suite to search, activate, and apply Ecrett Music permissions
- Responsive UI optimized for desktop and tablet workflows
- Bindings / integration examples for conversational models (e.g., Claude API) for score suggestion and troubleshooting
- Downloadable audio assets with royalty-free usage assurances
- Security-minded distribution and zero-hassle installation for on-prem/local utilities
- Workflow tooling for streamlined rights management and license issuance
Best for
- YouTube Video Backgrounds: Generate licensed background music matched to a video's mood and exact duration for quick publishing.
- Podcast Intros, Outros and Bed Tracks: Create consistent intros, stingers, and bed music tailored to episode tone without hiring composers.
- Game Prototyping and Loopable Ambience: Produce loopable ambient tracks and level music for prototypes or indie game projects.
- Short-form Ads and Social Content: Produce punchy, licensed tracks optimized for 15–60 second social and advertising spots.
- Corporate and Presentation Videos: Quickly score internal or external presentations and promotional videos with context-appropriate music.
- Indie Film and Video Production: Create mood-specific cues and background tracks for scenes when budget or time prevents custom scoring.
- Content creators generating background or theme music for videos and streams
- Game developers creating adaptive or placeholder tracks during development
- Filmmakers and editors sourcing royalty-free scores for projects
- Podcasters and broadcasters needing licensed beds and transitions
- Agencies producing licensed music for ads and marketing assets
- Tooling integrations where programmatic music generation is required (e.g., automated video pipelines)
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.