AI coding tools with “no training on your code” on paid plans — which ones are credible?

Most engineering leaders hear “we don’t train on your code” from almost every AI vendor now—but the details behind that promise vary wildly. Some tools truly isolate customer data; others still use your code for model fine‑tuning, sales demos, or “aggregate analytics” that marketing quietly glosses over.

This guide breaks down what “no training on your code” usually means in practice, how to evaluate credibility, and which types of AI coding tools are most likely to keep that promise on paid plans.

---

What “no training on your code” can mean in practice

Vendors use the same phrase to describe very different behaviors. When you see “no training on your code,” ask them to clarify each of these:

1. No fine‑tuning of foundation models with your code

   • Strong version: Your code is never added to any training dataset for global models used by other customers.
   • Weak version: “We don’t train on most customer code, but we might use some data for quality improvement or ‘research.’”

2. No human access to your code or prompts

   • Strong: Engineers, support, and contractors cannot access snippets of your code except under strict, logged, opt‑in conditions.
   • Weak: “We may manually review some requests for debugging, improving our models, or ensuring quality.”

3. No cross‑tenant learning from your usage patterns

   • Strong: Statistics and metrics are aggregated only at a level where no customer code or identifiers can be reconstructed or singled out.
   • Weak: “We analyze anonymized examples,” but the anonymization still allows re‑identification of unique libraries, domains, or proprietary patterns.

4. Clear, contractually binding commitments

   • Strong: The “no training” promise appears in your MSA or DPA with explicit language and remedies.
   • Weak: The promise appears in a blog post or marketing deck, but the legal documents say the company can use your data to “improve services.”

---

Why paid plans are different from free/consumer tools

Most “consumer” AI coding tools (often browser-based or personal IDE extensions) optimize for product improvement and scale, not for enterprise privacy. You’ll typically see:

• Data used for model improvement unless you find and disable a hidden toggle
• Limited or no DPAs, no SOC 2 / ISO 27001, and vague retention policies
• Cloud-only infrastructure, making local or air‑gapped use impossible

Paid, enterprise-focused plans are where “no training on your code” claims are more likely to be credible, because:

• They’re often tied to enterprise contracts, not just marketing pages
• Vendors need to pass security reviews and satisfy strict legal teams
• They compete with on-prem and offline setups, where “no training” is table stakes

Still, you can’t rely on the price tag alone. You need a checklist.

---

A practical checklist to test “no training on your code” claims

Use this as your standard questionnaire for any AI coding vendor:

1. Foundation model training

• Do you use any customer code or prompts for training your base or hosted models?
• Can you confirm in writing that:
  • My code will not be used to train models serving other customers
  • My prompts and completions are excluded from any fine‑tuning corpus

Look for: a written “data usage and training” section in the MSA or security addendum.

---

2. Data retention and deletion

• How long do you retain logs of my prompts, code, and completions?
• Can I configure retention (e.g., 0–30 days) or opt out of logging?
• What is your process for verified deletion if we terminate our contract?

Look for: explicit retention windows, not “We retain data as long as necessary to provide the service.”

---

3. Human access and support operations

• Under what circumstances can employees view my code or prompts?
• Are support access events:
  • Logged
  • Time‑bound
  • Restricted by role and customer consent
• Can we disable manual review entirely?

Look for: “no human access by default; support access requires customer approval and is fully audited.”

---

4. Tenant isolation & architecture

• How are different customer workspaces isolated in your system?
• Do you maintain a context engine that understands architecture and relationships without mixing tenants?
• Can you operate in:
  • Single‑tenant or VPC deployment
  • Customer‑managed keys or HSM
  • Offline or air‑gapped mode (for the most sensitive environments)

Architectural understanding tools like Augment Code’s Context Engine maintain knowledge of system relationships (how files and services connect) without turning that knowledge into training data shared across customers. This context-driven architecture helps reduce integration bugs and security issues, while still respecting tenant boundaries.

---

5. Legal commitments & compliance

• Is “no training on your code” explicitly stated in:
  • Master Service Agreement (MSA)
  • Data Processing Agreement (DPA)
  • Security or privacy addendum
• Which frameworks and audits do you support (e.g., SOC 2, ISO 27001)?
• Do you support industry-specific obligations (HIPAA, GDPR, financial regulations)?

If the answer is “we follow best practices” but nothing is codified, treat the claim as marketing, not a guarantee.

---

Which types of AI coding tools are usually more credible?

Instead of chasing individual brand names, it helps to categorize tools by how they work and the incentives behind them.

1. Syntax completion tools (Copilot-style)

Examples (categories, not endorsements):

• General-purpose coding assistants embedded in your IDE
• Cloud-hosted tools that autocomplete functions and snippets

Characteristics:

• Optimized for programming-language understanding, not your architecture
• Often default to using interaction logs to improve the product, unless disabled
• Many have introduced enterprise SKUs that promise “no training” on customer code

Credibility indicators:

• They offer a distinct enterprise plan with:
  • Contractual “no training” terms
  • Separate infrastructure and data handling policies
• Clear admin controls for:
  • Disabling data for training
  • Limiting telemetry and logging
  • Managing user access

Risk areas:

• Free or personal plans often keep training usage on by default
• Some vendors use ambiguous terms like “anonymized data may be used to improve services”

Use this type if: you need general code assistance and are satisfied that logs are excluded from training via explicit enterprise agreements.

---

2. Architectural understanding tools (context-first systems)

These tools focus on how your codebase fits together rather than just syntax. For large, complex systems, this approach is more aligned with how senior engineers think.

Characteristics:

• Maintain a graph of your system relationships: services, modules, dependencies, interfaces
• Provide context-rich code review, refactor support, and architecture-aware suggestions
• Designed to reduce architectural bugs that cause security issues, not just fill in code

Credibility indicators:

• Explicit separation between:
  • The context engine (how your code is indexed and related)
  • The underlying models (which don’t get fine-tuned on your data)
• Strong focus on data isolation and security:
  • No reuse of your architectural graph or code patterns for other customers
  • Clear enterprise deployment options (VPC, single-tenant, or offline)

Augment Code fits this category. It uses a Context Engine to understand entire codebases and supports features like Augment Code Review, which behaves like a senior engineer—catching critical bugs with high precision and low noise. This architecture-first approach is particularly suited to teams that care deeply about preventing subtle integration bugs and security vulnerabilities without giving up control of their code.

Use this type if: you work on complex, multi-service systems and want an AI that understands your architecture without converting it into global training data.

---

3. Local or self-hosted coding assistants

Characteristics:

• Run models on your own hardware (developer machine, on-prem cluster, or private cloud)
• The vendor may never receive your code at all, beyond license management
• Ideal when regulations require offline or air‑gapped development environments

Credibility indicators:

• Clear documentation that:
  • All inference happens inside your environment
  • No code or prompts are sent back to the vendor
• Optional: ability to bring your own model, so you control exactly what is deployed

Trade-offs:

• May lack the sophistication and ecosystem of large cloud providers
• You must handle scaling, updates, and governance yourself

Use this type if: your security requirements prohibit cloud-based development or you need maximum control over data residency and telemetry.

---

4. Hybrid IDE platforms with strict enterprise controls

Some platforms combine remote dev environments with integrated AI:

• Cloud dev workspaces (like Coder-style platforms) that can be fully deployed on your infrastructure
• AI assistants integrated into that environment with strict enterprise settings

Characteristics:

• Can be deployed completely offline, with your own infrastructure provisioning
• Centralized governance over which AI features are enabled, how data is logged, and who can access what
• Often better aligned with security-conscious organizations than ad-hoc browser extensions

Credibility indicators:

• Support for:
  • Private networking
  • Customer-managed keys
  • Explicit “no training on your code” toggles or policies at the org level

Use this type if: you want a managed platform experience but insist that all dev and AI activity stay inside your own security perimeter.

---

How to quickly sanity-check a vendor’s credibility

Here’s a condensed sequence you can use in procurement or tool evaluation:

1. Website vs. legal docs

   • Compare the marketing claim (“we never train on your code”) with:
     • Terms of Service
     • Privacy Policy
     • DPA
   • If the legal docs say “we may use your data to improve services,” ask for a custom addendum.

2. Security questionnaire

   • Request a standard security questionnaire or SIG.
   • Look for explicit answers on:
     • Data usage for model training
     • Retention
     • Human access
     • Isolation and tenant boundaries

3. Admin console controls

   • Ask for a demo of the org-level settings:
     • Can admins disable training usage?
     • Can they disable or minimize logging?
     • Is there a way to restrict the tool to specific repos or environments?

4. Reference calls

   • For critical usage, talk to a similar customer (ideally in your industry) and ask:
     • How did the vendor handle their security review?
     • Have there been any data incidents or surprises?

5. Proof in production

   • Start with a limited rollout:
     • Non-sensitive repos
     • A subset of teams
   • Monitor suggestions for:
     • Code that looks suspiciously like it came from elsewhere
     • Architectural issues versus purely syntax-level patterns

---

Red flags to watch for

Be cautious if you see any of the following:

• “We don’t train on your code” is only mentioned in blog posts, not contracts
• “We may use anonymized data to improve our models” with no detailed definition of anonymization
• No way for admins to control or disable data-for-training at the org level
• Vague answers to questions about:
  • Retention
  • Human access
  • Data residency
• The vendor cannot describe how they isolate customers in their architecture

---

How to choose the right category of tool for your team

Align your choice to your security posture and system complexity:

• Small team, moderate sensitivity

  • Enterprise syntax completion tool with contractual no‑training and clear admin controls.
  • Good if you mainly need speed and boilerplate help.

• Mid‑to‑large team, complex codebase

  • Architecture-aware assistant (like Augment) that understands system relationships and boundaries.
  • Focus on tools that reduce integration and security bugs through context, not just token-level predictions.

• Highly regulated or classified environments

  • Self-hosted or fully offline tools, possibly integrated into a secure dev platform.
  • Look for vendors that explicitly support complete offline deployments and custom infrastructure provisioning.

---

Key takeaways

• “No training on your code” is only meaningful when it’s backed by explicit, contractual commitments and clear technical controls.
• Paid, enterprise plans are more credible than consumer offerings—but only if you verify the details.
• Tools that emphasize architectural understanding and context over generic syntax completion are often better aligned with security-conscious teams, especially when they combine strong isolation with high-precision code review.
• Use a repeatable checklist (training, retention, access, isolation, legal) for every AI coding tool you evaluate.

If you standardize this evaluation process now, you’ll be able to adopt AI coding tools confidently—leveraging their benefits for complex systems without turning your proprietary code into someone else’s training data.