TextLayer
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What is TextLayer?
TextLayer is an enterprise AI infrastructure company that helps engineering teams build, deploy, and scale production-ready AI systems without rewriting their existing infrastructure. Their flagship product, TextLayer Core, is a modular AI stack that integrates directly into company infrastructure using Terraform scripts and standardized APIs, enabling teams to deploy LLMs, RAG pipelines, and agentic workflows into production environments.
Key features include a production-ready Flask API framework, automated infrastructure provisioning via Terraform, pre-built LLM and tool templates with retrieval and routing setups, technical documentation with an AI assistant, eval kits with datasets and scoring logic, working agent blueprints from past deployments, and custom Devin autonomous coding agent setup. The platform uses eval-driven development (EDD) methodology where teams define desired inputs/outputs and iterate quickly with LLM-as-a-judge scoring.
TextLayer serves enterprises and ambitious teams with engineering departments that need to integrate AI into legacy systems. Their clients include some of the world's most valuable companies with over $50B in enterprise value supported. The company offers flexible engagement models including fixed scope deliverables, ongoing AI expert support, and timeline-based engagements typically lasting 8-10 weeks. Their engineers embed with client teams to teach them how to maintain and extend AI systems after deployment.
TextLayer pricing
Pricing model: Freemium
TextLayer does not publish public pricing on their website. The company operates on an enterprise engagement model requiring businesses to book a call to get started. They offer flexible engagement models including fixed scope deliverables with clear timelines, ongoing AI expert on hand for strategic support and engineering assistance, and timeline-based engagements typically lasting 8-10 weeks. Pricing is customized based on scope, team size, and specific requirements. There is no free tier or self-service option - all engagements require direct contact with the TextLayer team.
TextLayer pros
- Integrates AI into existing infrastructure without costly rewrites
- Delivers working agentic MVP within 1-2 weeks
- Provides production-ready Flask API with built-in best practices
- Automated infrastructure provisioning with Terraform scripts
- Pre-built LLM and tool templates jumpstart AI delivery
- Includes evaluation datasets and LLM-as-a-judge tooling
- Provides working agent blueprints from successful past deployments
- Custom Devin autonomous coding agent setup included
- Teaches your team to maintain and extend systems after deployment
- Standardized APIs ensure consistent architecture across teams
- Built-in observability for monitoring AI components
- Supports retrieval-augmented generation (RAG) pipelines
- Modular stack maintains long-term maintainability
- Engineers embed with your team for hands-on support
- $50B+ enterprise value supported across major companies
TextLayer cons
- No public pricing information disclosed on website
- No free tier available - enterprise-only engagement model
- Requires technical team with engineering capabilities
- Minimum engagement typically 8-10 weeks for full kickoff
- Small company with only 1-10 employees
- No self-service option - requires booking a call
- Custom deployment tailored to each environment increases complexity
- Not designed for individual developers or small startups
Frequently asked questions about TextLayer
What is TextLayer Core?
TextLayer Core is a modular AI stack that integrates into your existing infrastructure. It includes a production-ready Flask API, Terraform scripts for automated infrastructure provisioning, pre-built LLM and tool templates, documentation with AI assistant, eval kits with datasets and scoring logic, and working agent blueprints from successful past deployments. It provides standardized, battle-tested infrastructure to deploy AI workflows fast without adding legacy debt.
How quickly can we deploy AI with TextLayer?
Teams can ship a working agentic MVP within 1-2 weeks after the stack is deployed. The typical full engagement to kickstart AI delivery lasts 8-10 weeks, during which your team gets upskilled to own and extend the system after TextLayer leaves.
Do I need to rewrite my existing infrastructure?
No. TextLayer's engineers embed with your team to integrate production systems and deploy AI features without costly infrastructure rewrites. Their modular stack plugs into your existing systems using Terraform scripts and standardized APIs.
What engagement models does TextLayer offer?
TextLayer offers three flexible engagement models: Fixed scope for teams that know what they want to build quickly with scoped deliverables and clear timeline; AI Expert on Hand for ongoing strategic support, roadmap guidance, and engineering assistance for teams scaling AI across products; and Timeline-based engagements for teams not fully scoped on deliverables, typically 8-10 weeks.
What is eval-driven development (EDD)?
Eval-driven development is TextLayer's methodology where teams first define evals (desired inputs/outputs), then build toward them using iterative development with LLM-as-a-judge scoring. The process follows Prompt → Eval → Refine → Deploy, making outputs measurable from day one.
Who is TextLayer's target customer?
TextLayer serves enterprises and ambitious teams with engineering departments that need to build, deploy, and scale advanced AI systems. Their clients include some of the world's most valuable companies, with over $50B in enterprise value supported. They work with platform teams and technical leaders integrating LLMs, RAG pipelines, and agentic workflows into production.
What monitoring capabilities does TextLayer Core include?
TextLayer Core enables robust monitoring for AI components in production including prompt performance tracking to optimize effectiveness over time, cost management to track LLM usage costs across services, quality assurance with systematic evaluation of AI outputs, and compliance monitoring to ensure AI services adhere to organizational policies and regulations.
Does TextLayer provide training for our team?
Yes. TextLayer provides code best practices and onboarding to launch features quickly and teaches your team to maintain them. Your team gets upskilled during the engagement so they can own and extend the AI systems after TextLayer leaves.
What AI use cases does TextLayer Core support?
TextLayer Core supports building internal AI tools (AI-powered knowledge bases, document processing systems, internal chatbots, data analysis tools) and creating LLM-powered APIs (content generation services, text analysis endpoints, recommendation systems, translation services). It also implements consistent patterns across teams with standardized deployment workflows and unified monitoring.
How do I get started with TextLayer?
To get started with TextLayer, you need to book a call with their team. They will scope your goals, deploy the stack, and guide your team through a clean setup using their Terraform scripts and standardized API. You own everything that's built, and their engineers embed with your team during the process.