Nightfall AI alternative for developers and LLM apps

Nightfall usually comes up in two different conversations. A security team wants DLP across Slack, Google Drive, Jira and laptops, and AI is one item on the list. Or a developer is building an LLM feature and wants an API that keeps customer data out of the model. Nightfall serves the first conversation well. This page is mostly about the second.

Maskflare wrote this comparison. We've stuck to what Nightfall publishes about itself as of October 2026.

What is Nightfall?

Nightfall AI was founded in 2018 in San Francisco and has raised about $60 million, including a $40 million Series B in 2022. Its website now describes one platform across AI security, data security and investigation: DLP for SaaS, endpoints and email, integrations with Slack, Google Drive, Gmail, Jira, Confluence, Salesforce, Microsoft Teams, SharePoint, Notion and Zendesk, shadow AI controls and an MCP gateway that enforces policy on tool calls.

Two parts matter for AI traffic. Nightfall DLP for Browsers is a Chrome extension that, per its store listing, redacts sensitive data before it's submitted to ChatGPT, Claude, Gemini, Copilot and other AI apps, or lets the employee decide. Firewall for AI is a set of APIs and SDKs (Python, Java, Go, Node.js): you send prompt text to a scan endpoint, get back findings and a redacted payload, then pass the cleaned prompt to your model. Nightfall says its detectors are ML-trained and cover 100+ data types, and lists prompt-injection detection as early access.

When is Nightfall the better choice?

• You need DLP beyond AI. Scanning Slack messages, Drive files, Jira tickets and email is Nightfall's core. Maskflare doesn't touch SaaS apps at rest.
• You want one vendor for many channels. If the goal is one console for SaaS, endpoint, email and AI exfiltration, Nightfall covers more ground.
• Your security team runs investigations. Nightfall offers investigation features; Maskflare records metadata and counts only, never prompt content, so there's less to investigate by design.
• ML detection suits your data. For unstructured entities in prose, a trained model can catch things deterministic rules miss.

When is Maskflare the better choice?

1. You need the real values back. Nightfall's published flows redact or block. We couldn't find documentation of restoring the original values into the model's answer. Maskflare's tokens are reversible: the provider sees mf_mask_170b..., your app or employee sees Ada Lovelace again in the reply.
2. You'd rather not add a scan call to every request. With Firewall for AI, your code calls the scan endpoint and then the model. With Maskflare you change one base URL and the gateway handles requests, streamed responses, embeddings and batch files.
3. You build on several providers. One gateway covers OpenAI, Anthropic, Gemini, Mistral, xAI, DeepSeek, Groq, Together AI and self-hosted Ollama, each in its native API format.
4. You need EU hosting. Maskflare is EU-hosted by default, with on-premises deployment for enterprise. We couldn't find a published EU data residency option for Nightfall; ask them if it matters to you.
5. You want deterministic, explainable results. Same input, same output, with the matching rule shown in the console. That's easier to defend in an audit than a model score.
6. Coding agents are in scope. Hooks for Claude Code, Cursor and Codex CLI mask tool inputs and outputs and block prompts containing secrets.

Nightfall vs Maskflare, feature by feature

What does switching look like for a developer?

With a scan-then-call API, your code does three things for every request: send text to the scanner, take the redacted text, call the model. Streaming, tool calls and multi-part messages each need handling. With the gateway it's one change:

# Before: scan, then call
# findings, redacted = scanner.scan(prompt)
# reply = openai.chat.completions.create(..., messages=[{"role": "user", "content": redacted}])

# After: one base URL, values restored in the reply
from anthropic import Anthropic
client = Anthropic(base_url="https://<your-gateway>/v1/anthropic", api_key="mf_live_...")

Performance is a fair question for anything inline. With default rules Maskflare adds about 2 ms median to a 10 KB chat request (4.3 ms p99), and about 10 ms with all 574 detectors on. The local named-entity layer adds time on long free-text prompts and can be switched off per organization.

The honest objections

“Nightfall covers far more than AI.” It does. If you need SaaS DLP, Maskflare isn't a replacement. Plenty of teams would run Nightfall for SaaS and endpoints and Maskflare for AI traffic.

“Redaction is enough for us.” It might be. If your prompts are questions about data rather than tasks that need the data back, such as summaries for analytics, redaction works. Once the model has to write an email to Ada or fill a form with her IBAN, redacted placeholders reach the user and someone has to fix them by hand.

“ML catches more.” On names and free-text entities, a model helps, which is why Maskflare runs a local named-entity layer next to its rules (100% on our 228-case Swiss benchmark). For IDs, card numbers and secrets, checksums and formats are hard to beat.

“Our security team already chose Nightfall.” Then the useful question is whether its AI coverage reaches your application traffic in the shape you need. A browser extension doesn't see your backend's calls to Anthropic, and a scan API only helps if every service calls it. If the developers end up writing their own restore logic around redacted text, that's the gap Maskflare fills, and it can sit next to Nightfall without overlap.

How should you evaluate the two?

Run a short, specific test rather than comparing feature lists. Here's the one we'd use if we were in your seat:

1. Collect 200 prompts your product actually sends, or realistic stand-ins: support tickets, CRM notes, code snippets with config files, a few in German or French if you serve Europe.
2. Mark the sensitive values by hand before you run anything. It's tedious and it's the only way to count misses honestly.
3. Run both tools and record three numbers per tool: values missed, values masked that weren't sensitive, and prompts where the answer became unusable.
4. Check the answers, not just the prompts. With redaction, see how many replies contain placeholders a person would have to fix. With masking, check that restored values appear where they should.
5. Time it. Measure added latency at p50 and p99 on your real payload sizes, including streamed responses.
6. Ask about data handling. Where is the text processed, is it logged, and for how long? Maskflare never logs prompts or responses and keeps the values behind tokens encrypted for 30 days only to restore answers.

The third number is the one most comparisons skip. A tool can score well on detection and still make your feature worse if every reply comes back with [REDACTED] in it.

What Maskflare doesn't do

• It doesn't scan SaaS apps, email or file stores at rest.
• Prompt-injection detection is pattern-based and explainable; it has no content-moderation classifier.
• It covers AI traffic only, not email or SaaS file sharing.
• SOC 2 Type II and ISO 27001 audits are in progress.