Galileo vs amplication
Side-by-side comparison to help you choose.
| Feature | Galileo | amplication |
|---|---|---|
| Type | Platform | Workflow |
| UnfragileRank | 40/100 | 43/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 1 |
| Ecosystem |
| 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Ingests structured execution traces from deployed LLM applications capturing models, prompts, function calls, context, and metadata in a unified schema. Processes traces through a centralized observability pipeline that correlates signals across the full execution path, enabling step-by-step workflow reconstruction and failure attribution. Supports ingestion via REST API, MCP server, and SDK integrations with configurable sampling and filtering at ingest time.
Unique: Implements unified multi-signal trace ingestion (models + prompts + functions + context + metadata) in a single schema rather than separate telemetry streams, enabling cross-signal correlation for root-cause analysis of agent failures without requiring distributed tracing infrastructure
vs alternatives: Deeper than generic observability platforms (Datadog, New Relic) because it understands LLM-specific signals (prompt changes, function selection, hallucinations) rather than treating them as opaque logs
Analyzes model outputs against provided context and ground truth to identify factual inconsistencies, unsupported claims, and fabricated information. Uses a combination of LLM-as-judge evaluation and Luna distilled models to detect when generated text contradicts source documents or makes claims without supporting evidence. Operates on trace data post-inference, enabling both real-time guardrails and offline batch analysis of historical outputs.
Unique: Combines LLM-as-judge evaluation with Luna distilled models (proprietary cost-optimized evaluators) to achieve 97% cost reduction vs traditional multi-judge evaluation while maintaining detection accuracy, enabling hallucination checking at scale without prohibitive inference costs
vs alternatives: More cost-effective than running multiple GPT-4o judges for hallucination detection; more accurate than simple embedding similarity because it understands semantic contradictions and unsupported claims rather than just surface-level relevance
Enables configurable sampling and filtering of traces at ingest time to reduce trace volume and associated costs. Supports filtering by criteria (e.g., only failures, high-latency requests) and sampling strategies (e.g., 10% of all traces, 100% of failures). Filtered traces are excluded from trace count limits but can still be analyzed if stored.
Unique: Implements ingest-time filtering and sampling to reduce trace volume before storage, enabling cost optimization without requiring application-side changes or losing visibility into important events
vs alternatives: More cost-effective than storing all traces because filtering happens at ingest; more flexible than fixed sampling rates because filtering criteria can be customized for specific use cases
Supports evaluation of outputs from any LLM provider (OpenAI, Anthropic, open-source models, etc.) using the same metric library and guardrails. Metrics are provider-agnostic and can be applied to any model output regardless of source. Enables comparison of outputs from different providers using consistent evaluation criteria.
Unique: Implements provider-agnostic metrics that work across any LLM provider rather than being optimized for specific APIs, enabling consistent evaluation and comparison regardless of which LLM is used
vs alternatives: More flexible than provider-specific evaluation tools because metrics work with any LLM; enables provider migration without pipeline changes
Tracks evaluation metrics over time and automatically detects regressions (quality drops) in model outputs. Compares current metric values against historical baselines and alerts when metrics fall below configured thresholds. Supports trend visualization and statistical significance testing to distinguish real regressions from noise.
Unique: Automatically detects quality regressions by comparing current metrics against historical baselines with statistical significance testing, enabling early warning of degradation without manual threshold tuning
vs alternatives: More proactive than manual quality checks because regressions are detected automatically; more accurate than simple threshold-based alerts because statistical significance testing distinguishes real regressions from noise
Provides 20+ out-of-box evaluation metrics pre-configured for common LLM use cases (RAG, agents, safety, security) that automatically score model outputs against configurable criteria. Metrics are implemented as Luna distilled models that run at 97% lower cost than LLM-as-judge alternatives. Metrics can be applied to historical traces, new inferences, or custom datasets without code changes, with results aggregated into dashboards and reports.
Unique: Implements domain-specific metrics as Luna distilled models rather than rule-based scoring or full LLM evaluation, achieving 97% cost reduction while maintaining accuracy through model distillation from high-quality judges, enabling metric application at production scale
vs alternatives: Cheaper and faster than running GPT-4o or Claude judges for every evaluation; more accurate than rule-based metrics because Luna models understand semantic nuance while remaining cost-effective at scale
Enables users to define custom evaluation metrics using a domain-specific language or configuration interface, then automatically apply them to traces and datasets. Custom metrics integrate into CI/CD pipelines as quality gates that block deployments if metrics fall below configured thresholds. Metrics are versioned and can be tested against historical traces before deployment, with results tracked over time to identify regressions.
Unique: Integrates custom metric definition directly into CI/CD pipelines as quality gates rather than requiring separate evaluation infrastructure, enabling metrics to block deployments before production impact and tracking metric regressions over time
vs alternatives: More integrated than external evaluation frameworks because metrics are defined, tested, and enforced within the same platform; more flexible than pre-built metrics because custom logic can be defined for domain-specific requirements
Analyzes multi-step agent execution traces to identify failure patterns, incorrect tool selection, and suboptimal decision-making. Detects specific failure modes (e.g., 'hallucination caused incorrect tool inputs') by correlating agent actions with outcomes. Provides prescriptive debugging suggestions (e.g., 'Best action: Add few-shot examples') based on pattern analysis. Failure detection is quantified with percentage metrics (e.g., '15% Failure Detected') aggregated across trace populations.
Unique: Correlates agent actions (tool selection, prompts, context) with outcomes to identify causal failure modes rather than just reporting errors, then generates prescriptive suggestions based on pattern analysis across trace populations
vs alternatives: More actionable than generic trace analysis because it understands agent-specific failure modes (tool selection, hallucination in tool inputs) and provides specific remediation suggestions rather than just identifying that failures occurred
+5 more capabilities
Generates complete data models, DTOs, and database schemas from visual entity-relationship diagrams (ERD) composed in the web UI. The system parses entity definitions through the Entity Service, converts them to Prisma schema format via the Prisma Schema Parser, and generates TypeScript/C# type definitions and database migrations. The ERD UI (EntitiesERD.tsx) uses graph layout algorithms to visualize relationships and supports drag-and-drop entity creation with automatic relation edge rendering.
Unique: Combines visual ERD composition (EntitiesERD.tsx with graph layout algorithms) with Prisma Schema Parser to generate multi-language data models in a single workflow, rather than requiring separate schema definition and code generation steps
vs alternatives: Faster than manual Prisma schema writing and more visual than text-based schema editors, with automatic DTO generation across TypeScript and C# eliminating language-specific boilerplate
Generates complete, production-ready microservices (NestJS, Node.js, .NET/C#) from service definitions and entity models using the Data Service Generator. The system applies customizable code templates (stored in data-service-generator-catalog) that embed organizational best practices, generating CRUD endpoints, authentication middleware, validation logic, and API documentation. The generation pipeline is orchestrated through the Build Manager, which coordinates template selection, code synthesis, and artifact packaging for multiple target languages.
Unique: Generates complete microservices with embedded organizational patterns through a template catalog system (data-service-generator-catalog) that allows teams to define golden paths once and apply them across all generated services, rather than requiring manual pattern enforcement
vs alternatives: More comprehensive than Swagger/OpenAPI code generators because it produces entire service scaffolding with authentication, validation, and CI/CD, not just API stubs; more flexible than monolithic frameworks because templates are customizable per organization
amplication scores higher at 43/100 vs Galileo at 40/100. Galileo leads on adoption, while amplication is stronger on quality and ecosystem.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Manages service versioning and release workflows, tracking changes across service versions and enabling rollback to previous versions. The system maintains version history in Git, generates release notes from commit messages, and supports semantic versioning (major.minor.patch). Teams can tag releases, create release branches, and manage version-specific configurations without manually editing version numbers across multiple files.
Unique: Integrates semantic versioning and release management into the service generation workflow, automatically tracking versions in Git and generating release notes from commits, rather than requiring manual version management
vs alternatives: More automated than manual version management because it tracks versions in Git automatically; more practical than external release tools because it's integrated with the service definition
Generates database migration files from entity definition changes, tracking schema evolution over time. The system detects changes to entities (new fields, type changes, relationship modifications) and generates Prisma migration files or SQL migration scripts. Migrations are versioned, can be previewed before execution, and include rollback logic. The system integrates with the Git workflow, committing migrations alongside generated code.
Unique: Generates database migrations automatically from entity definition changes and commits them to Git alongside generated code, enabling teams to track schema evolution as part of the service version history
vs alternatives: More integrated than manual migration writing because it generates migrations from entity changes; more reliable than ORM auto-migration because migrations are explicit and reviewable before execution
Provides intelligent code completion and refactoring suggestions within the Amplication UI based on the current service definition and generated code patterns. The system analyzes the codebase structure, understands entity relationships, and suggests completions for entity fields, endpoint implementations, and configuration options. Refactoring suggestions identify common patterns (unused fields, missing validations) and propose fixes that align with organizational standards.
Unique: Provides codebase-aware completion and refactoring suggestions within the Amplication UI based on entity definitions and organizational patterns, rather than generic code completion
vs alternatives: More contextual than generic code completion because it understands Amplication's entity model; more practical than external linters because suggestions are integrated into the definition workflow
Manages bidirectional synchronization between Amplication's internal data model and Git repositories through the Git Integration system and ee/packages/git-sync-manager. Changes made in the Amplication UI are committed to Git with automatic diff detection (diff.service.ts), while external Git changes can be pulled back into Amplication. The system maintains a commit history, supports branching workflows, and enables teams to use standard Git workflows (pull requests, code review) alongside Amplication's visual interface.
Unique: Implements bidirectional Git synchronization with diff detection (diff.service.ts) that tracks changes at the file level and commits only modified artifacts, enabling Amplication to act as a Git-native code generator rather than a code island
vs alternatives: More integrated with Git workflows than code generators that only export code once; enables teams to use standard PR review processes for generated code, unlike platforms that require accepting all generated code at once
Manages multi-tenant workspaces where teams collaborate on service definitions with granular role-based access control (RBAC). The Workspace Management system (amplication-client) enforces permissions at the resource level (entities, services, plugins), allowing organizations to control who can view, edit, or deploy services. The GraphQL API enforces authorization checks through middleware, and the system supports inviting team members with specific roles and managing their access across multiple workspaces.
Unique: Implements workspace-level isolation with resource-level RBAC enforced at the GraphQL API layer, allowing teams to collaborate within Amplication while maintaining strict access boundaries, rather than requiring separate Amplication instances per team
vs alternatives: More granular than simple admin/user roles because it supports resource-level permissions; more practical than row-level security because it focuses on infrastructure resources rather than data rows
Provides a plugin architecture (amplication-plugin-api) that allows developers to extend the code generation pipeline with custom logic without modifying core Amplication code. Plugins hook into the generation lifecycle (before/after entity generation, before/after service generation) and can modify generated code, add new files, or inject custom logic. The plugin system uses a standardized interface exposed through the Plugin API service, and plugins are packaged as Docker containers for isolation and versioning.
Unique: Implements a Docker-containerized plugin system (amplication-plugin-api) that allows custom code generation logic to be injected into the pipeline without modifying core Amplication, enabling organizations to build custom internal developer platforms on top of Amplication
vs alternatives: More extensible than monolithic code generators because plugins can hook into multiple generation stages; more isolated than in-process plugins because Docker containers prevent plugin crashes from affecting the platform
+5 more capabilities