Groq vs Laguna by Poolside: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Groq and Laguna by Poolside — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Groq
Groq
High-performance inference platform delivering fast, low-cost model inference via the Groq LPU and developer tooling.
Key features
- Low-Latency Inference: Groq LPU hardware is engineered to deliver very low-latency model inference, reducing response times for production LLM and ML workloads compared with general-purpose processors.
- Cost-Efficient Throughput: Platform design and tooling emphasize lowering inference cost per request by maximizing utilization and deterministic execution across Groq chips.
- GroqFlow Compiler Workflow: GroqFlow automates compilation of machine learning and linear-algebra workloads into Groq programs, handling build, optimization, and execution steps for running models on Groq processors.
- Developer SDKs and REST API: Official client libraries (e.g., groq Python package) and a documented REST API enable synchronous and asynchronous calls, configurable timeouts, and easy integration into applications and pipelines.
- Gradio Integration (groq-gradio): A packaged integration to rapidly create web demos and deployable UI frontends that leverage Groq inference speed for multimodal and text-generation models.
- Production Runtime & Tooling (GroqWare): Runtime packages and developer tools (groq-devtools, groq-runtime) facilitate building, running, and managing compiled models on Groq hardware with recommended system requirements and deployment guidance.
- High-Performance & Deterministic Execution: Targeted support for ML, AI, and HPC workloads with optimizations for linear algebra and deterministic behavior to simplify debugging and production reliability.
- Groq Language Processing Unit (LPU) hardware for low-latency, high-throughput inference
- GroqFlow: automated compilation workflow to convert ML/linear-algebra workloads into Groq programs
- GroqWare Suite (groq-devtools, groq-runtime) for building/compiling and executing models on Groq hardware
- REST API for inference with official SDKs (groq Python library with sync/async clients, PHP SDK, Go tooling)
- Official Python library (pip install groq) with configurable httpx-based timeouts and full REST surface
- Integrations and examples: groq-gradio for Gradio apps, community projects using Groq API for search/summarization
- Support for major model families (examples in ecosystem: DeepSeek r1, Llama 3.3, Mixtral, Gemma)
- Command-line and developer tooling for model compilation, deployment, and formatting (GroqFlow, groq-devtools)
- Configurable runtime and client-level timeouts; type definitions for request/response fields in SDKs
- Generated SDKs (Stainless) and support for both synchronous and asynchronous workflows
Best for
- Low-Latency LLM Serving: Deploy production language models with sub-second inference latency for chatbots, assistants, or real-time content generation where response speed and cost matter.
- Compile-and-Run ML Workloads: Use GroqFlow to compile neural network or linear-algebra workloads into Groq programs and execute them efficiently on GroqChip processors for inference and HPC tasks.
- Rapid Prototype Web Apps: Build and deploy Gradio-powered web demos that call Groq-hosted models to showcase multimodal or generative AI capabilities with fast response times.
- Integrate Into Python Applications: Embed Groq inference into backend services or data pipelines using the official groq Python SDK for synchronous/asynchronous request handling and timeout control.
- On-Prem or Appliance Inference: Leverage Groq hardware and runtime packages for organizations requiring on-prem inference acceleration with deterministic performance and controlled operational costs.
- High-Performance Scientific Computing: Accelerate linear-algebra-heavy simulations or analytics workloads by compiling them for Groq LPUs to gain throughput and predictable execution characteristics.
- Production LLM inference requiring minimal latency and high request throughput
- Compiling and running machine learning or HPC linear-algebra workloads on specialized hardware
- Rapid prototyping and deployment of ML-powered web apps via Gradio integration and Groq API
- Embedding Groq inference into backend services using Python, PHP, or Go SDKs and REST APIs
- On-prem or cloud deployments that need a full toolchain (compile -> runtime) for optimized model execution
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.