Internship
Office Operations Associate
Posted on 7/8/2026
Datadog
5,001-10,000 employees
Cloud monitoring, analytics, and observability platform
Compensation Overview
$51k - $75k/yr
Company Historically Provides H1B Sponsorship
New York, NY, USA
In Person
On-site role in New York City; remote work not offered.
 Facilities Operations (1) |
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- A true people person with an empathetic and friendly demeanor
- A quick learner who loves tackling challenges and streamlining processes
- Exceptional time management skills with the ability to multitask effectively
- A self-starter who takes initiative and drives projects forward
- Calm and even-tempered, able to maintain composure under pressure
- Patient and persistent, with a passion for seeing tasks through to completion
- Highly organized with keen attention to detail and pride in maintaining a welcoming office environment
- Able to bring structure to chaos
- Clear and concise communicator, both verbally and in writing
- Possess good spreadsheet skills
- Ability to partake in scheduled after-hours events on a monthly basis
- Act as the face of Datadog by welcoming visitors and supporting employees in the office
- Act as the primary point of contact for all in-office needs, creating a friendly, helpful, and professional environment
- Own and lead daily floor walkthroughs, identifying and resolving operational issues proactively to maintain a high standard of cleanliness, safety, and functionality
- Cultivate and maintain positive working relationships with building management & vendor partners
- Partner with the Security team to ensure the implementation of all safety protocols
- Serve as a main point of contact for incoming service tickets, ensuring timely updates, proper prioritization, and follow-through
- Work with vendor partners to keep the office stocked with food, snacks, and pantry supplies, ensuring everything meets our Datadog standard for quality and consistency
- Maintain inventory of office supplies and ensure timely restocking to support daily operations
- Arrange fun and engaging events for employees - both on a monthly cadence and an ad hoc basis
- Work closely with teammates to maintain smooth office operations and ensure consistency across all workplace processes
- Where required, work alongside various Operations teams to assist with office moves, build-outs and openings
- Engage in a 9-month rotation with teammates to gain hands-on experience as project coordinators across key areas including event planning, food program management, space planning, and facilities operations
- A true people person with an empathetic and friendly demeanor
- A quick learner who loves tackling challenges and streamlining processes
- Exceptional time management skills with the ability to multitask effectively
- A self-starter who takes initiative and drives projects forward
- Calm and even-tempered, able to maintain composure under pressure
- Patient and persistent, with a passion for seeing tasks through to completion
- Highly organized with keen attention to detail and pride in maintaining a welcoming office environment
- Able to bring structure to chaos
- Clear and concise communicator, both verbally and in writing
- Possess good spreadsheet skills
- Creative thinker who brings fresh ideas that enhance our culture at Datadog
- Ability to partake in scheduled after-hours events on a monthly basis
Datadog provides a platform for monitoring and analyzing IT infrastructure, including servers, databases, and applications. The product works by collecting data from a user's cloud environment and displaying it in a single dashboard where teams can track performance, manage logs, and detect security threats. Unlike many competitors that offer fragmented tools, Datadog integrates monitoring, security, and analytics into one unified interface with a flexible pricing model based on data usage. The company's goal is to provide organizations with real-time visibility into their digital operations to ensure their systems remain reliable and secure.
Company Size
5,001-10,000
Company Stage
IPO
Headquarters
New York City, New York
Founded
2010
Your Connections
People at Datadog who can refer or advise you
Simplify's Take
What believers are saying
- Bits Agent Builder enables custom AI agents for operational workflows and automated remediation.
- Agent Console provides centralized monitoring for AI agents like Claude Code and GitHub Copilot.
- Subscription model with land-and-expand strategy drives 27.7% revenue growth in FY 2025.
What critics are saying
- Widespread autonomous agent misconfiguration may cause outages within 6 to 12 months.
- AI Guard could fail due to inability to detect subtle prompt poisoning in 12 to 18 months.
- AI Credits cost explosion may hit customers via 30-day invoice shock in 3 to 6 months.
What makes Datadog unique
- Bits AI SRE resolves incidents 90% faster by pinpointing root causes in minutes.
- AI Guard blocks prompt injection and agent poisoning attacks using deep telemetry tracing.
- Bring Your Own Cloud lets customers process data in their own storage, cutting costs.
Help us improve and share your feedback! Did you find this helpful?
Benefits
Health Insurance
Dental Insurance
Mental Health Support
401(k) Retirement Plan
401(k) Company Match
Company Equity
Employee Stock Purchase Plan
Professional Development Budget
Hybrid Work Options
Flexible Work Hours
Paid Vacation
Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↑ 4%2 year growth
↑ 6%Datadog Bits AI pricing changed. Don't roll it out blind. Nick Vecellio Co-Founder and Principal Engineer, NoBS Yesterday at DASH, Datadog moved Bits onto the new AI Credits model, and the math changed dramatically. Under the old pricing, a Bits SRE investigation cost $25 at the committed rate, based on $500 per 20 investigations, or $36 on demand. Under AI Credits, Datadog's own telemetry across all accounts shows an average of 6.5 credits per investigation. At the committed rate of $500 for 500 credits, that puts the average investigation at about $6.50 per run. That is a 74-82% cut depending on how you were buying before. That's not just a discount; it's a signal. Datadog wants agentic operations to be something every engineer reaches for, not something teams save for sev-1s. And it's not just Bits SRE. The same AI Credits model powers Bits Chat, Bits Security, and Bits Dev, which means your organization now has a single consumption pool feeding four different agents. When a capability goes from rationed to routine, and from one front door to four, the question changes. It is no longer, "Can we afford to use it?" It is, "Are we using it well, and can we see who's using it?" Teams that have been through this curve before know how it ends. Log ingestion. Synthetics. Custom metrics. Every one of them started cheap and accessible. Every one of them punished organizations that skipped governance. The answer is governance, not hesitation. Get the access model and the visibility right up front, and the new pricing is pure upside. Start with access: Datadog's default roles need a closer look. The natural place to start is access, and this is where Datadog's default roles deserve scrutiny. The stock Standard role is much closer to Admin than it is to Read Only. That's fine when you're handing out trust, but it is overly permissive for anything with a meter on it. Datadog enables all AI Credits products by default for the Standard role. So if you've done nothing, most of your org can already spend from the credit pool. In its engagements, Nobs replace the three stock roles with a tiered framework purpose-built for cost-sensitive capabilities. With the Bits agent family now sharing a credit pool, Nobs has added a new piece to it. The tiered base roles. Read Only. Full visibility, zero write access, and zero cost exposure. This is the right default for most of the org. Limited Standard. Can create dashboards, monitors, and similar resources. Very low risk of incurring cost, but enough capability for day-to-day platform work. This is where most builders should live. SRE. Effectively Standard-level access plus additional controls, for the operators who need real reach into the platform. Admin. Full administrative access, granted sparingly. Very sparingly. Base tiers handle the broad strokes. Additive roles handle the exceptions. Datadog allows multiple role assignments where assigned permissions win, so an additive role grants a specific capability without promoting someone's entire access level. Its framework includes additive roles for User Management and Cost Management. As of this week, it also includes Bits Access. The Bits Access role is the cleanest example of why additive beats monolithic. A Read Only user granted Bits Access can run Bits agents and draw from the shared credit pool while remaining unable to edit any other resource in the account. You get adoption where you want it, scoped exactly as wide as you intend, with no collateral permissions dragged along for the ride. For what it's worth, granting Bits access to a Read Only user probably is not the right move. It is just a clean example of how additive roles work. What IS a good idea though - giving Datadog users with Incident Management seats access to Bits. Let's be realistic here, a real live incident is exactly where you want Bits in the picture. Watch the meter: AI Credits need cost visibility. Roles decide who can spend; cost monitoring tells you what is actually being spent before the invoice does. One honest caveat up front: as of launch, there are no real-time estimated usage metrics for AI Credits that you can alert on directly. That gap will probably close, but you should not wait for it. What you can do today is track AI Credit spend through your Plan & Usage page. The data isn't instant, but it is a world apart from the alternative, where the alternative is finding out about a runaway agent the same way teams find out about runaway log ingestion: Thirty days late. On an invoice. With no way to claw it back. Close the loop: measure adoption, not just spend. Here's where the governance story pays off twice. Bits activity is visible directly in the agent console, broken down by individual user. So alongside the cost data, you can answer the questions leadership will actually ask: Who is adopting these agents? Which teams are getting value? Is that new Bits Access role you granted last month being used, or sitting idle? Paired with cost visibility, you get both halves of the picture. Cost monitoring catches out-of-bounds spend before the invoice arrives. The console shows whether the spend you did incur maps to real adoption. That is the difference between a cost center you tolerate and a capability you can defend in a budget review. The point is not to slow Bits adoption down. The pricing change makes Bits agents accessible to everyone in your org. The framework above makes sure that access is deliberate: the right roles, granted additively; cost monitoring watching the meter; and per-user visibility closing the loop. None of it slows adoption down. It's what lets you say yes to adoption with confidence. If you want help mapping this framework onto your own Datadog account, that's exactly the kind of thing Nobs do. Reach out. FAQ: Datadog Bits AI, AI Credits & governance. Last updated: 2026-06-10 What are Datadog AI Credits? Which Datadog Bits products use AI Credits? Should every Datadog user get Bits access? Why use additive roles for Bits Access? Who should get Bits access first? How should teams monitor AI Credit usage?
Guggenheim has upgraded Datadog to Buy with a $175 price target, viewing the stock's 14.33% year-to-date decline as an attractive entry point. The upgrade centres on Datadog's positioning at the intersection of cloud migration and AI deployment. Datadog reported strong fourth-quarter fiscal 2025 results, with revenue of $953.19 million beating estimates by 3.76%. Full-year revenue reached $3.43 billion, up 28% year-over-year. The company now has 603 customers generating over $1 million in annual recurring revenue, up 31% year-over-year. The cloud monitoring platform provider's shares currently trade near $114, well below the 52-week high of $201.69. Management has guided fiscal 2026 revenue to between $4.06 billion and $4.10 billion. Forty-three analysts rate the stock a Buy.
Datadog has launched Datadog Experiments, an integrated platform for product testing and analytics that lets teams design, launch and measure experiments alongside real-time observability and business metrics. The offering targets enterprises previously relying on separate tools for experimentation, analytics and monitoring. The launch comes as Datadog shares trade around $116.50, down 12.9% year-to-date despite a 19.6% return over the past year. Shares currently trade approximately 36% below the consensus analyst price target of $181.52. By tying experimentation directly to observability and business metrics, Datadog aims to deepen its platform's role in customer workflows. However, profit margins have declined to 3.1% from 6.8% last year, and recent insider selling has been significant. Adoption rates among large customers will be key to watch.
Benchmark has initiated coverage of Datadog with a Buy rating and $150 price target, citing the company's AI-powered observability and security platform as positioned to benefit from digital transformation, cloud migration and agentic AI growth. The firm highlighted Datadog's technological leadership, a total addressable market exceeding $400 billion, and consistent profitable growth with Rule of 45+ performance metrics. On 9 March, Datadog announced the general availability of its MCP Server, enabling developers to integrate real-time observability data into AI-driven development workflows. The platform allows teams to debug and operate systems using live telemetry whilst maintaining governance and security controls. Datadog's cloud observability platform is seeing increased adoption driven by AI applications and large language models, positioning it for sustained growth and market leadership.
Honeycomb vs Datadog: which observability tool in 2026? An honest comparison of Honeycomb and Datadog for observability. Honeycomb excels at event-driven debugging. Datadog is the all-in-one SaaS. Here is when to pick each - and when neither fits. Honeycomb and Datadog are both observability tools, but they approach the problem from fundamentally different directions. Honeycomb was built to answer unknown questions about your systems. Datadog was built to be the single platform for everything operations-related. This comparison covers how they actually differ in 2026, where each one excels, and - importantly - when neither is the right choice. Honeycomb: event-driven debugging. Honeycomb was founded by Charity Majors and Christine Yen, both from Facebook's infrastructure team. The core idea: traditional monitoring tools force you to decide what to measure before you know what questions you will ask. Honeycomb flips this. You send high-cardinality events, and query them later. How it works. Every span, log, or event you send to Honeycomb is stored in a columnar store optimized for ad-hoc queries. You can group by, filter, and break down on any attribute - user ID, shopping cart size, feature flag variant, database query text - without pre-defining indexes. This is the key differentiator. In Datadog, if you want to filter APM data by a custom attribute, you need to index it (and pay for it). In Honeycomb, every attribute is queryable by default. Standout features. * BubbleUp: Select a group of slow or erroring requests, and Honeycomb automatically identifies which attributes are different between the selected group and the baseline. Instead of guessing root causes, the tool shows you. * Query builder: Flexible enough to replace many custom dashboards. Group by multiple dimensions, calculate percentiles, heatmaps, and rates - all in a single query interface. * SLOs: Define service-level objectives tied to your trace data. Honeycomb tracks burn rate and alerts when you are consuming your error budget too fast. * OpenTelemetry native: First-class OTLP support. Honeycomb was an early and active contributor to the OpenTelemetry project. Pricing (2026). * Free: 20M events/month * Pro: $130/month for 100M events * Enterprise: Custom pricing, SSO, advanced roles Limitations. * Not an all-in-one platform. No infrastructure monitoring, no synthetics, no log management (though it can ingest structured logs as events). * Steeper learning curve. Getting value from Honeycomb requires understanding high-cardinality querying, which is a different mental model than dashboards-and-alerts. * Smaller ecosystem. Fewer integrations, fewer pre-built dashboards, fewer community resources than Datadog. Datadog: the all-in-one platform. Datadog started as infrastructure monitoring in 2010 and has expanded into a comprehensive observability and security platform. In 2026, it covers infrastructure, APM, logs, synthetics, real user monitoring (RUM), security, CI/CD visibility, database monitoring, and more. You install the Datadog Agent on your hosts (or use serverless integration). The agent collects metrics, traces, and logs automatically. Datadog's auto-instrumentation libraries handle most popular frameworks, so you get APM data with minimal code changes. Everything lands in a single platform with cross-linking: click on a trace to see related logs, jump from a metric spike to the traces that caused it, or correlate infrastructure metrics with application performance. * Unified platform: Infrastructure, APM, logs, RUM, synthetics, security - all in one UI with cross-linking between signals. * Auto-instrumentation: Datadog's agent auto-instruments most frameworks. Less manual work than OpenTelemetry-based tools. * Service map: Automatically generated dependency graph showing how services communicate, with health indicators on each edge. * Notebooks and dashboards: Rich visualization with team sharing, annotations, and incident timelines built in. * Watchdog AI: Automated anomaly detection that flags unusual patterns without manual threshold configuration. * Infrastructure: $15/host/month * APM: $31/host/month * Logs: $0.10/GB ingested (after plan inclusion) * Indexed Spans: $1.70 per million (after 1M included with APM) * Synthetics: $5/1000 API test runs The complexity of Datadog pricing is itself a feature. Or a bug, depending on your perspective. Many teams report bill shock after scaling up, because each product has separate metering and the costs compound. * Cost unpredictability at scale. The per-host, per-GB, per-million-spans pricing model makes budgeting difficult. * Vendor lock-in. Datadog's proprietary agent and query language make migration expensive. * Jack of all trades. Each individual feature is good but rarely best-in-class. Honeycomb's debugging is deeper. Grafana's dashboards are more flexible. PagerDuty's alerting is more sophisticated. Head-to-Head comparison. | Dimension | Honeycomb | Datadog | | Philosophy | Debug unknown unknowns | Monitor everything in one place | | Query power | Excellent (high-cardinality native) | Good (requires indexing for custom attributes) | | Infrastructure monitoring | No | Yes (core strength) | | Log management | No (events only) | Yes | | Synthetics | No | Yes | | Auto-instrumentation | Via OpenTelemetry | Proprietary agent (more automatic) | | SLO tracking | Yes (built-in) | Yes (built-in) | | Free tier | 20M events/month | 5 hosts, limited features | | Cost at scale | Predictable (event-based) | Unpredictable (multi-axis metering) | | Best for | Debugging distributed systems | Full-stack operations teams | When to choose Honeycomb. * Your primary pain is debugging - finding why specific requests fail or slow down. * You run microservices and need to trace requests across service boundaries. * You already use other tools for infrastructure (Prometheus, Grafana) and logs (Loki, ELK). * You want OpenTelemetry-native tooling without vendor lock-in. * Your team is comfortable with a query-driven workflow (vs. dashboard-driven). When to choose Datadog. * You want one platform for infrastructure, APM, logs, and more. * Your team prefers dashboards and pre-built views over ad-hoc queries. * You need auto-instrumentation with minimal code changes. * You have the budget and want to minimize the number of vendors. * You need compliance features (SOC 2, HIPAA, audit logging) from a single vendor. When neither fits. Both Honeycomb and Datadog are built for teams running distributed systems at meaningful scale. But a large portion of modern applications do not look like that. If you are running a single Next.js application - deployed on Vercel or a VPS - you do not have distributed traces to analyze. You do not have 50 hosts to monitor. You have API routes that need to be fast, reliable, and monitored. For that scenario, both tools are overkill. Honeycomb's high-cardinality debugging is powerful but unnecessary when your "distributed system" is one application. Datadog's all-in-one platform costs more per month than most indie products earn. What you need is focused API monitoring: per-endpoint response times, error rates, status code tracking, and instant alerts when something breaks. Nurbak Watch is built for this exact use case. It runs inside your Next.js server via instrumentation.ts - five lines of code - and monitors every API route automatically. Alerts hit Slack, email, or WhatsApp in under 10 seconds. $29/month flat, free during beta. No per-host pricing, no per-span charges, no bill surprises. If your architecture grows into microservices, you can graduate to Honeycomb or Datadog. But start with what your architecture actually needs today. The Nurbak Team builds developer-first API monitoring tools. Nurbak share insights on uptime, performance, alerting, and best practices for keeping APIs healthy in production. Ready to try it? Nurbak Watch is free during beta. 5 lines of code. First alert in under 5 minutes. Comparisons