Fleak – Serverless Data API Builder with No-Code IDE

TL;DR

TL;DR: Fleak is a browser-based, no-code API backend builder that chains SQL transformations, LLM model calls, and AWS Lambda steps into deployable endpoints — handling up to 5,000 QPS without an LLM node, no storage layer required.

Source and Accuracy Notes

⚠️ This section is MANDATORY. All links must be verified from actual source, not guessed.

• Project page: fleak.ai
• Source repository: None (closed-source, not open source at time of writing)
• License: Proprietary (no open-source license verified)
• HN launch thread: news.ycombinator.com/item?id=41172036

What Is Fleak?

Fleak (fleak.ai) is a serverless data API backend builder targeted at data practitioners who need production-grade API endpoints without managing infrastructure. The core product is a browser-based IDE where you build workflows by chaining together pre-built step types:

• Native SQL Transformation — write SQL directly against your data sources without an intermediate storage layer
• LLM Node — call LLM models (OpenAI, Anthropic, etc.) as a workflow step
• AWS Lambda — invoke Lambda functions as part of the pipeline
• HTTP Request — call external APIs

Once a workflow is built and tested, Fleak deploys it to a production endpoint with a single click. The company’s own benchmarks claim 5,000 QPS handling capacity for workflows that do not include an LLM node.

What Problem Does It Solve?

From the founder’s launch post:

“The inspiration for Fleak came from my frustration with maintaining production data pipelines, especially with the added complexity brought by LLMs. Fleak aims to simplify life for data practitioners.”

Typical alternatives — writing Flask/FastAPI services, configuring dbt + Airflow pipelines, or wiring together Retool / Pathway — all require significant DevOps overhead. Fleak’s pitch is that you get a typed, versioned, production API without touching cluster config, Docker, or CI/CD.

Setup Workflow

Fleak requires no local installation. Everything runs in the browser.

Step 1: Create an Account

Navigate to fleak.ai and sign up. The free tier is available but comes with rate limits due to hosting costs (per the founders).

Step 2: Create a New Workflow

From the dashboard, click New Workflow. You start with a blank canvas.

Step 3: Add Steps

The step palette includes:

| Step Type | Description | |---|---| | SQL | Write raw SQL against connected databases | | LLM | Prompt an LLM model and use its output in the next step | | Lambda | Invoke an existing AWS Lambda function | | HTTP | Call any external REST endpoint | | Transform | Manipulate/reshape data between steps |

Chain steps by connecting their inputs and outputs visually.

Step 4: Test Locally

Click Run to execute the workflow in Fleak’s sandbox environment. You see real output at each step.

Step 5: Deploy to Production

Click Deploy. Fleak provisions a public endpoint you can call immediately. No Docker image, no Kubernetes YAML, no git push.

Known Restrictions

Per the founders on HN:

1. SQL syntax support is partial — not all SQL dialects are fully supported; new syntax is added daily
2. Free tier has rate limits — the hosted service throttles free accounts due to compute cost
3. LLM node latency — LLM call latency is not optimized due to cost constraints; however, base throughput (non-LLM) is higher than out-of-the-box managed services and scales elastically

Practical Evaluation Checklist

• [ ] Browser-based IDE, no local install
• [ ] SQL step — verify it works with your target database
• [ ] LLM node — confirm model availability for your use case
• [ ] Deploy test — call the produced endpoint with curl
• [ ] Rate limits — check current free tier quotas before committing

Security Notes

• Closed source — the codebase is not publicly auditable. If code transparency is a hard requirement, Fleak is not suitable.
• Data passes through Fleak’s infrastructure — since there is no self-hosted option, any data in workflows is processed on their servers. Evaluate against your data classification policy.
• API key management — confirm how Fleak handles credentials for connected databases and external APIs.

FAQ

Q: Is Fleak open source? A: No. The founders indicated they are considering which parts to open source in the future, but as of the launch (August 2024) the codebase is proprietary.

Q: Can I self-host Fleak? A: No. Fleak is a fully hosted SaaS product with no self-hosted or on-premises option.

Q: What databases does the SQL step support? A: Fleak connects to standard SQL databases. The specifics of supported engines (PostgreSQL, MySQL, etc.) should be confirmed on their documentation site.

Q: How does pricing work? A: A free tier exists with rate limits. Paid plans remove or increase these limits. Exact pricing details are on the fleak.ai pricing page.

Q: What’s the difference between Fleak and Retool or Pathway? A: Retool focuses on building internal UI-facing tools. Pathway is oriented toward data pipeline orchestration. Fleak’s niche is a no-code API backend builder — you design a workflow in the IDE and get a public REST endpoint with no infrastructure work.

Conclusion

Fleak fills a specific gap: data practitioners who need a production REST API without DevOps overhead. The browser-based IDE, native SQL support, and one-click deploy are genuinely useful for teams that want to ship a data transformation or LLM-wrapped endpoint quickly.

The trade-offs to evaluate honestly: it is closed source, hosted-only, and the SQL/LLM performance characteristics under heavy production load are not independently verified. If those constraints are acceptable for your use case, Fleak is worth a try — the free tier is available to experiment with at fleak.ai.