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← Back to Guidance Overview

Guidance Pricing & Plans 2026

Complete pricing guide for Guidance. Compare all plans, analyze costs, and find the perfect tier for your needs.

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Still deciding? Read our full verdict on whether Guidance is worth it →

🆓Free Tier Available
💎2 Paid Plans
⚡No Setup Fees

Choose Your Plan

Open Source

Free

mo

    Start Free →

    Azure OpenAI (Cloud Backend)

    Pay-per-use via Azure

    mo

      Start Free Trial →
      Most Popular

      OpenAI API (Cloud Backend)

      Pay-per-use via OpenAI

      mo

        Start Free Trial →

        Local Model (Self-Hosted)

        Free (hardware costs only)

        mo

          Start Free →

          Pricing sourced from Guidance · Last verified March 2026

          Feature Comparison

          Detailed feature comparison coming soon. Visit Guidance's website for complete plan details.

          View Full Features →

          Is Guidance Worth It?

          ✅ Why Choose Guidance

          • • Guarantees output validity against JSON schemas, regex patterns, and context-free grammars at the token level
          • • Rust-based grammar engine processes constraints efficiently during generation
          • • Composable primitives (`gen`, `select`, `@guidance`) enable complex multi-step generation pipelines
          • • Reduces token usage and latency by avoiding retry loops for structured output
          • • Backed by Microsoft Research with an active open-source community
          • • Fully open source under a permissive license with no usage fees or API keys required

          ⚠️ Consider This

          • • Strongest guarantees require local model backends — cloud APIs have more limited constraint support
          • • Learning curve is steeper than simple prompt engineering or wrapper libraries like Instructor
          • • Documentation and examples lag behind the pace of library development
          • • Performance on very large grammars or deeply nested schemas may degrade
          • • Fewer ready-made integrations with agent frameworks compared to simpler structured output tools

          What Users Say About Guidance

          👍 What Users Love

          • ✓Guarantees output validity against JSON schemas, regex patterns, and context-free grammars at the token level
          • ✓Rust-based grammar engine processes constraints efficiently during generation
          • ✓Composable primitives (`gen`, `select`, `@guidance`) enable complex multi-step generation pipelines
          • ✓Reduces token usage and latency by avoiding retry loops for structured output
          • ✓Backed by Microsoft Research with an active open-source community
          • ✓Fully open source under a permissive license with no usage fees or API keys required

          👎 Common Concerns

          • ⚠Strongest guarantees require local model backends — cloud APIs have more limited constraint support
          • ⚠Learning curve is steeper than simple prompt engineering or wrapper libraries like Instructor
          • ⚠Documentation and examples lag behind the pace of library development
          • ⚠Performance on very large grammars or deeply nested schemas may degrade
          • ⚠Fewer ready-made integrations with agent frameworks compared to simpler structured output tools

          Pricing FAQ

          How does Guidance differ from regular prompting?

          Regular prompting sends text and hopes the model responds in the right format. Guidance interleaves fixed template text with constrained generation steps, enforcing structure at the token level so invalid outputs are impossible rather than merely unlikely.

          What changed in the 2026 release?

          The Rust-based grammar engine (llguidance) was further optimized for performance, with improved JSON schema support including oneOf/allOf/anyOf, better error messages, and expanded model backend compatibility.

          Can I use Guidance with OpenAI or Azure models?

          Yes. Guidance supports OpenAI GPT models and Azure OpenAI deployments. Constrained generation works through provider-supported structured output features, though the strongest token-level guarantees are available with local models.

          How does Guidance compare to Instructor?

          Instructor validates structured output after generation and retries on failure. Guidance enforces constraints during generation at the token level, preventing invalid output from being produced in the first place — eliminating retry overhead.

          Is Guidance suitable for production use?

          Yes, for applications requiring guaranteed structured output. It is used in production systems where output validity is critical, particularly with local model deployments.

          Does Guidance work with local models?

          Yes, and local models get the strongest guarantees because Guidance can directly control token sampling. Supported via Transformers and llama.cpp backends.

          Ready to Get Started?

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          More about Guidance

          ReviewAlternativesFree vs PaidPros & ConsWorth It?Tutorial

          Compare Guidance Pricing with Alternatives

          Outlines Pricing

          Grammar-constrained generation for deterministic model outputs.

          Compare Pricing →

          Instructor Pricing

          Extract structured, validated data from any LLM using Pydantic models with automatic retries and multi-provider support. Most popular Python library with 3M+ monthly downloads and 11K+ GitHub stars.

          Compare Pricing →