Tiger Data

Unternehmensdaten

Offizieller Name

Timescale, Inc.

Einheitentyp

COMPANY

Gegründet

2015

Hauptsitz

United States

Unternehmensgröße

50–200

Marktrolle

B2B SaaS Provider

Offizielle Website

tigerdata.com

Was Tiger Data macht

Tiger Data uses an open-core database infrastructure model. Open-source TimescaleDB drives developer adoption and PostgreSQL ecosystem compatibility, while Tiger Cloud converts production workloads into recurring consumption revenue. The company also sells commercially licenced, self-managed TimescaleDB Enterprise deployments, support and operational services to organisations that require on-premises, private-cloud, edge or air-gapped infrastructure. The 2024 PopSQL acquisition expanded its developer workflow and SQL collaboration capabilities around the core PostgreSQL platform.

Strategische Einordnung

KI-gestützte Einordnung aus der bestehenden Unternehmensrecherche; Interpretation und belegte Fakten sind zu unterscheiden.

Tiger Data is the trading name of Timescale, Inc., a private US software company providing PostgreSQL-based database infrastructure for time-series, real-time analytics, operational application data and integrated search. Its portfolio includes the managed Tiger Cloud service, open-source TimescaleDB, commercially licenced TimescaleDB Enterprise for customer-managed deployments, and a PostgreSQL-native keyword, vector and hybrid search stack. The company sells primarily to developers, software companies, data engineering teams, industrial organisations and regulated enterprises. It generates revenue from usage-based managed-cloud consumption, enterprise software licences, paid support and enterprise contractual arrangements. Its architecture enables customers to retain operational, analytical and search workloads within PostgreSQL rather than operating separate database and search systems.

Geschäftsmodell und Monetarisierung

Tiger Data monetises Tiger Cloud through usage-based cloud subscriptions, with hourly billing for configured compute and consumption-based storage charges. Published entry pricing starts at $30 per month for compute, while Enterprise plans are sold through sales engagement. TimescaleDB is distributed as open-source software to support adoption; TimescaleDB Enterprise is commercially licenced for self-managed deployments. Paid support plans, enterprise operational controls and contractual services provide additional recurring enterprise revenue.

Tiger Cloud managed PostgreSQL consumption

Usage-based compute and storage billing

TimescaleDB Enterprise

Commercial software subscription and enterprise licensing

Paid support and enterprise operational services

Service Fee

Open-source TimescaleDB

Product-led acquisition channel supporting cloud and enterprise conversion

Produkte und Fähigkeiten

Für diese Ansicht liegen keine Produkte mit zugeordneten Quellen vor.

Produkte und Marktkategorien

Zuletzt erfasste Signale

Datumsangaben beziehen sich auf die Quellenveröffentlichung. Ältere Einträge sind historischer Kontext, kein Beleg für ein neues Ereignis.

• Configure LiteLLM as Codex Model Provider

  2026-08-28dev.to

  Large Language Models (LLM) & AI · Erfasster Impact-Score: 1/5

  A developer guide demonstrating how to route Codex to use LiteLLM as a custom model provider. The post explains exposing a LiteLLM API key as an OS environment variable, updating Codex's .codex/config.toml to set model_provider to 'litellm' and add provider-specific fields (base_url, env_key, wire_api, streaming options), setting optional custom HTTP headers, and noting that session models are fixed at session creation. The author also advises verifying usage via LiteLLM dashboard logs and links to LiteLLM and Codex documentation for reference.

  • Author Julia Shevchenko published a how-to on dev.to on 2026-08-28 about configuring LiteLLM as a gateway for Codex.
  • LiteLLM exposes an OpenAI-compatible interface and can act as a gateway for LLMs.

• Python 3.15 Release Candidate and Highlights

  2026-08-27dev.to

  Infrastructure · Erfasster Impact-Score: 3/5

  Python 3.15 has reached Release Candidate 1 with the final release expected on October 1, 2026. Key changes include lazy imports for faster startup, UTF-8 as the default encoding, a built-in immutable mapping (frozendict-style), continued JIT compiler improvements with early benchmarks (roughly 8–9% on Linux and larger gains on Apple Silicon), and a new low-overhead sampling profiler called Tachyon. Free-threaded (no-GIL) builds remain opt-in but gain maturity and improved ABI support for C extensions. The release focuses on performance, tooling, and developer experience rather than major language syntax changes.

  • Python 3.15 has reached Release Candidate 1; final release expected on 2026-10-01.
  • Python 3.15 introduces lazy imports to reduce startup time by loading modules only when needed.

• Claude hooks ntfy for iOS Announce Notifications

  2026-08-18dev.to

  Conversational AI & Chatbots · Erfasster Impact-Score: 1/5

  A developer posted an open-source project that connects Claude code hooks to ntfy iOS Announce Notifications so a locked iPhone can speak when an LLM prompt finishes or requires user attention. The project (claude-speaks) is implemented in standard-library Python with no external dependencies and is published on GitHub. The post was published on DEV Community by Shamail Tayyab on 2026-08-18.

  • Shamail Tayyab published a DEV Community post on 2026-08-18 describing the project.
  • The project repository is github.com/pleomax00/claude-speaks.

• Author Tests Grok Bot to Book Flights

  2026-08-13dev.to

  Conversational AI & Chatbots · Erfasster Impact-Score: 1/5

  A developer-author tested whether a Grok AI agent could act as a travel agent and book flights. The experiment showed the bot could perform many tasks (including editing and uploading a video via other bots) but failed to complete the final booking step. The post describes the workflow used, references generated content publishing via Twitter and LinkedIn bots, and links to a video walkthrough and the original blog post.

  • Author Debbie O'Brien tested a Grok AI agent to see if it could book flights for her.
  • The author used AI bots for video editing and to upload the video; the YouTube upload was handled by a YouTube bot.

• Understanding LLM Hallucinations and How to Fix Them

  2026-08-13dev.to

  Large Language Models (LLM) & AI · Erfasster Impact-Score: 2/5

  This Dev.to explainer (posted Aug 13, 2026 by Sangam Shrestha) describes why large language models (LLMs) produce confident but false outputs — known as hallucinations — and gives practical mitigations. The article explains that LLMs operate by predicting the next most likely token rather than verifying facts, which leads to invented answers when training data is missing or when models are optimized to appear confident. Real-world risks highlighted include security vulnerabilities (e.g., fabricated software packages) and damaged credibility from shipping incorrect code or data. Recommended mitigations include grounding outputs with specific source documentation, lowering the model 'temperature' to reduce creativity, and enforcing human-in-the-loop review before production use.

  • Article authored by Sangam Shrestha and published on DEV Community on 2026-08-13.
  • LLM hallucinations occur because models predict the next most likely word/token rather than verifying factual truth.

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