Monitor Your Entire Azure Infrastructure More Effectively
- Collect and analyze metrics from your entire Azure stack with 40+ turn-key integrations for Azure services and 850+ other technologies
- Get started monitoring in minutes with auto-discovery for Azure services such as CosmosDB, Service Bus, and AKS, among many others
- Visualize the performance of all your Azure cloud services, on-premise servers, VMs, databases, containers, and .NET apps in real time with customizable, drag-and-drop dashboards
![dg/cs-demo/azuredashboard_screencapture](https://imgix.datadoghq.com/img/dg/cs-demo/azuredashboard_screencapture.jpg?ch=Width,DPR,Save-Data&fm=jpg&auto=format&fit=max&w=730)
Resolve Azure Performance Issues Faster
- Easily identify bottlenecks, errors, heavy traffic issues, slow-running queries, and more with end-to-end application tracing, continuous profiling, and real user monitoring
- Automatically collect, monitor, and visualize high-granularity data and custom metrics in real time, including availability, response times, reliability, error rates, and throughput
- Get deeper visibility into your Azure ecosystem with more than 40 Datadog-generated metrics for Azure in addition to the metrics collected from Azure Monitor
![dg/cs-demo/gif-3-code-level-visibility](https://imgix.datadoghq.com/img/dg/cs-demo/gif-3-code-level-visibility.png?ch=Width,DPR,Save-Data&fm=png&auto=format&fit=max&w=730)
Simplify Your Onboarding Experience
- Simply configure the setup process by enabling single sign-on with Azure Active Directory when you create a new Datadog account
- Easily install the Datadog Agent on multiple Azure hosts at once
- Automatically start sending Azure platform logs and metrics to Datadog within minutes of signing up
![dg/cs-demo/monitored-resources-table](https://imgix.datadoghq.com/img/dg/cs-demo/monitored-resources-table.png?ch=Width,DPR,Save-Data&fm=png&auto=format&fit=max&w=730)
Adopt Azure Services with Confidence
- Simplify migrations to Azure by monitoring services side-by-side; ensure benchmarks are met
- Visualize performance data for legacy and cloud-based systems before, during, and after a migration on Datadog's host mapManagement
- Pivot from a high-level overview of your infrastructure and applications to the health of individual Azure services in seconds
![dg/cs-demo/dashboard-overview-1](https://imgix.datadoghq.com/img/dg/cs-demo/dashboard-overview-1.jpg?ch=Width,DPR,Save-Data&fm=jpg&auto=format&fit=max&w=730)
Ensure Optimal Performance with Alerts Powered by Machine-Learning
- Track all SLIs and SLOs in real time
- Automatically detect unanticipated outliers, anomalies, and errors with Watchdog
- Proactively prevent outages and errors by alerting on metric forecasts or specific data thresholds
- Eliminate false-positives and leverage AI-powered alerts that account for daily, weekly, and seasonal fluctuations or cascading event failures
![dg/cs-demo/watchdog-alerts-2](https://imgix.datadoghq.com/img/dg/cs-demo/watchdog-alerts-2.jpg?ch=Width,DPR,Save-Data&fm=jpg&auto=format&fit=max&w=730)
Eliminate Silos and Improve Efficiency
- Share critical information and context across teams with workflow integrations such as Azure DevOps, Microsoft Teams, Slack, and Jira
- Break down information silos with intuitive collaboration features for real-time graphs, dashboards, logs, traces, and security signals
- Create shareable Datadog notebooks to encourage teams to seamlessly work together on postmortems, runbooks, or other team documentation
![dg/cs-demo/monitor-azure-devops-request-traffic-drops-devops-build-2](https://imgix.datadoghq.com/img/dg/cs-demo/monitor-azure-devops-request-traffic-drops-devops-build-2.jpg?ch=Width,DPR,Save-Data&fm=jpg&auto=format&fit=max&w=730)
Ensure a Successful Migration with Azure’s Cloud Adoption Framework
- Datadog provides critical visibility into your environment at each of the phases recommended in the Azure’s Cloud Adoption Framework
- Ensure your environment is ready to move to the cloud and safely migrate your workloads
- Better plan and track your migration’s progress and immediately identify problems with Datadog - a single source of truth
![dg/cs-demo/azure-caf](https://imgix.datadoghq.com/img/dg/cs-demo/azure-caf.png?ch=Width,DPR,Save-Data&fm=png&auto=format&fit=max&w=730)
Take Control of Your Cloud Costs on Azure
- Migrate to the cloud without any unexpected cost overages using granular, accurate Azure cost data across multiple billing accounts and subscriptions
- Provide each team with cost data scoped to their services and applications to help empower engineers to take action
- Identify cost optimization opportunities with unit economics and combine cost data with telemetry to visualize the full story
![dg/cs-demo/azureccm2](https://imgix.datadoghq.com/img/dg/cs-demo/azureccm2.png?ch=Width,DPR,Save-Data&fm=png&auto=format&fit=max&w=730)
Customer Story
Lifion by ADP previously leveraged multiple monitoring tools for alerting, tracing, and more, totalling $2.1 million in annual maintenance costs. With Datadog, ADP was able to consolidate their monitoring solutions and reduce time spent managing disparate tools. These changes resulted in a net savings of $647,000 annually, representing a 30% decrease in their overall IT costs.
![ADP 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m0mPHg8eKG2tXdeR9eDxk2NaobhvoT5qUIXOiuJdoDP/3Ap/CBbHS8DyuRLo2fGYpisxfRKQ97onE8viUyuN7erYE8jTTWhYep0ldAV5DuO2waK23XUxph2RyqIW7/vg/39uByRVtofBLqydWnAoeGrdkycZEnjqNP5wHYp8+5K71jLx4EWZzJXJJLBT+fSxUUQIuKYoueJJHFh8CD5kKr+npuRLI0wyPw97C7R9vZ+MSRF5tKk6XRAkijWmAk3156kVr4lJracWXvWr1jVsyf2N+ot1KmiTd7PtgJPMzvaw/G0E1tpaGv+/WNTZgh+8G/llt4BF+w06IlVdN7eG5Il3ES2dyVyWdCx5KDaIhXfj/4FkTxB6ULNfcLwKcjqie5JZT05ryiv8HHjhkxaLNJiRPlP3NsxOXTQO4W+F2qxfIjdb58FXzN2qlvwPeQdR0QU9OlECeRgh9fwCP8fjUg8FI/Ssg9kUSkRwE13RKDa3+EAtVroiVzToNXGbNPNZKfYrfqpUgdtPKsK7drLh++Y84iSuO0fiUisvBYWMaG55BD9O48t/6jQ0TZgw72HkSyNMEt2TO44ejwUPWsovBye23gtgnIjJA9BCOBzJeiIWq/tHs8qQ4awKcJsXj9vgBiBTta7d2LMyaHiVucPhJqR9zufmjZ+AsR+/Lgjx1rYfX0sD2/PwvH+wkCeRpYMuUGdaY+M3gMUK4Y8jyN9eB2Cfiji4QvUSn5KO5pCVU+T1wUXG0Nmpo+AIBeZpkJF1g2q0jt80Vx5a1PPluebkfHDJ8yfyNRKZnKaqJ4CsHO0cCeRrYhvlX8UMJeAlhdWBg+2MgDkS61vuEBinAe1tDlf+NheaUgEtGL619FQkvA5ENk932iwPd2SP08N+tLCkpAIfMj459DJFeAw9wDaxy5eEH3uJUArnHYuWzy/md+i54TBFcjh6nSEx7TuZEzwHcHToHINHA3aFXgkuC0bqHFFBa7G6VBrK2IsrB7syCguA/ejKe3BfW5F+dxKu8egH9CfjSgb4uBZPXtGllcMsHD/HF+VxhpO6PIITzCvnfD+NllX9aP2nWUHAB+YdY63HfgRym1fYc6E3COYk8/wvkUMu8aFndPK4weDIRWCn80oEyKkog91C8rOqL/HA4eGunIt/VIA6OULrWbUKEn2/PMxbGXZgIF1g8d5sJ6gwE2gw5Svv8WTXZbX8Isaotv+in4BAT9I/ACwQT4+Uv77d7XQK5h4j07eAxDk0/DETnyVKdHjBQutbtxNfeZI3J+WvLK48Ah42JzF/KhaH3GRM9opKJnLl2CeH8eKjyS+CAusjYF/knNIIHNOnw/r4mBZNH2kLH8DghTgJvvZ/E5P0gesTM2VzrzuEmYlFSQ0OsNHwxOMza35srD7+DHKT0wJxKZkSAj3wwraIMbNaZKEvdAx7wEZ28v69JIPdAfPrM8SZqz9dr85v/LSuBBogeMUjLOnIHWGuC+T8/aw1VXQ9Oy99qZcpaCjmIcmvVxZCOpHp+2SH2z8MIRIqe5Kt2NbjMJDjB2hlzX1+TQO6FDv+d3DIYCN6aVxipmwuix0yUMXIn8Rj2HbFQuBocFFi8eBsqcrz1n25MX7vOwfzCEwYfatg+fImpVrl+HFyGCMMHq1Gf2NfXJJC7bE2o4rM8Nn42eEwnDcfTG2YbQ5afueHm1tKK28BBgcb6//DD85BDqDNFa+7FcgWXtoQqw2A7epyr9a7vR8GNiRP3dVwKJhf9l4c5DFCej0nz3fx48fJX3wDRK5LZzR2I6oZ4uaPd7EQIl/B98CHkiGFZnBDmYPgP/7HdO6YFIw3NXB68Ci7zIRy7r+MSyF1UGgrfwg8TwENcgK020Pg+iF6TKO4e0tzNXhp2bFlkUVPd+0TkaMs/nWzrTNGao0NDOGttWeX/gs00aNczYWqCmTRnjm/v4xLIXRKfGB6tgL4NHlMEt41qejUGovesdpxw052xUOX/gEMCgxP3cwGYMz1TlMMZhrUDqXr9pP7O49bbwUXWOPnK2I7yvY9LIHcJ5dFNBOhI+sCe4jGdtwsjdb8A0Tcy2c1dCAb/57ex8qqp4AArJTERXAU5wEztfoa5XBH9VCx0zByw0aho7YfcqzMPXDYEjVl7H5NA7oLYpNlcg0LPW+NJSp6LsvFH30mL3AsjwKS/du0QaLtAtO4/XD17GXKAXLym7Tn+TcB/g8sSpD+59zEJ5A6r5tcY88xfgtcQHxmz9LW3QfSZLCL3CMLErcr/+7WjygeDAwzAa7l2m9XJfrJ597Oew1PWTJo1BuykjbfAZQrU9I8fE466sLTyO9x9VwHeaufxsbtA9IvMWvcOAp6gRw6/AxzAXaQL+d39O2S/XO+N8ymfcRHYKB/1a/yyJsBFpGDa3hPeJJA7aF1pVREX/p5vSKKB7rNm6YLoFySyvVFOVu8cOfmPsqbywa/Vd+JlVY5UipGUYxttpINBqXXk5PC1tuc/snYKTzMc8M6uPsAuYr1ljZNzDd/VVjkPBQ3b9sGOkd2P+UA4xkR9M7/sQfBWc0FS/wBEvyUBba15W1nGWoIzDnGytMsbMUShGUMThh3K9ZBRGnxFBiZL+GceSYDTuVSYjhk0CYqIfhMvnH5koG3xNrBRIFpb0xYKN/Dr4nXvmSOGLq35gK81Vyfb5o3YobQ5eFSeypvUQfp0fm0/y+XhOPAQ/w4TLpr64uHV74Jtw4waYQHXDGaCe3Cr6bN2DWzbdUACuUNWhGZO4YLyW+AxbpDdMHL5gi0g+s1AtL0Hqzi2yNnlKx8tNLSSn3xsl7v3DztmhH+QDnOr4rNA+jMc0+0dQ7TfFBw++CYuwq4BG1mTQGNA3HWPWdvF7vi1trfOa28rdF53/4oXnnStHv7hN7nieB2/4KPBIx1JZS1ptC2QG0hvuz0PVps0mR8adn0uXesOGQh5PwOPIeFTRdGGnNztSfTMuFXzNwab6l4IRmovDETrDjNUxxHcdfcAdKvtpxtuAV3dMil8EtgsGKl/HpEWgHBEoG3utqJo/f2YhMP5U8+GMgxUJ4KN0DQWgss0Ukn3zyWQOyAeqjyHx4dOAC8hbKd2UzK42Uhn+e5n3FKi0Y2vv8UB/TK9uX0CB7XzuaHaDGkIDbp/ZUlJAdhME3q/wiTLBd6rWxuM1FmTgG3f0KSHZvHwzCCwyfbE1vfR5VUPaq+eMwnkNls2adZQHodxZHZtb2iCe4LNDc0g7JNDCWGsbthAU/0vd+7ML+M/+kr+tw7SCCKW5RcUXwE226j8v7ceQDguGK27iSvHz4D7CpIjB08Gm4xvfmsTN9w+ABdxxeGQ7p9LILfZYL/vSn6Zx4OHuKbbqreSJ5vfZ7UcTAgzvrlmZ1Gk7sf5fvNIRPgVpBEEuiY+fbqtY61TG2u28vP+GoTjrHkJiQEFF3jR62OYcATYCpvBRfzaFXf/XAK5jWKhihIgsD17UG8p0j8cu6Z+BwhbGTmcHGvk4gVrAk113zAJjk+j7vahZvug74HNDFJPgXDF+LdqNinAC8BtisrATgiuLu/VRNIidwqB+h232mwbe+kThNcCSxvuAyEcMMZKaerHWTx08wdIAzxWeL7d6Vs7E8TA6yBcURipm8ulZw24iHstJ4KNtIYN4CKFao+lhBLIbdJWFv4qN9cc2MC+56zdjTRoWzMXiY9wx3pWp/HsqcDiurXF0bqz+cPqNJg0MGIbFnwHbGat+ADhIneHM5TN+T0UurvKg0v6Id0/l0BuE014A3iML87HipsacmZbRtfl8j6Q+xCM1N0CpC9CK5OXl5Au2TDB3mQnSex4EYRrEnm+v7mZhZBv5JFgIw1ka4Kig8P87p9JILdBa3nVhXxpTAEPEeE6rqXdCcIx3H0mCZT2UhRteMRKnQreGrHTX2Dr7oLFh497jwDTaqZ+Nhu3ZP5GQmwB99ga+7j8dTfZzl57OEkg7ydrghsSeR5ACZLXBCOy3MxJhpKdIPclGK17SGn4Gni6KYe+oiU4YyDYBJ991kSg34JwjQG4Etxja6WcCwbXJxcvmzRpd6tcAnk/IarbuPgaAd5aWBxd8DgIR5mI3nYhp7HCpXVPcBS3fQZ5TyHgSGN4wWfBRsrEf4JwkZv1QHs3dFEepIpK+P27/wYJ5P0QC83+FF8OXwGvId4MwgW5kxCmL6z15gTk2YoJ0vRNsNH2jrxXCcHtLtOcxbXkALjG3kq51u6vVpra2Lh7EycJ5P2AkHwMPMaRZW6wqVYm5riC5H45iGBy+HX8Oi0CDyDCnLXlR9sWDKxkOIqgFoTjosEZh6LNS8IORNvfFW57uuCD2KMiIgVTH7WGKq8jQNvS/PUNbUbwu59MIXdJi/wgcPk/2pOozwEPxgz5zTGSlHc+2Ih73P4NwnHDhvlP5JaRax3URueubLZRGmybn9Ez1N79MwnkfWBNcFOI14PHkPDeYKSmGYRbZLJbD4xtWrCMA6A3E0ApNenONknD3UQluUoDXghusnndt0Z0cVgg5cPun0gg7xN1KRdUg8FDRLTS3NIuGdxcpKRF3mPbzGH38sM74DKuaU1aO2qObffmFtj8VupBOGZ9qDLM79ux4CKT9HtgIwNpOLhrj0xyEsh7aW155SR++C54zCDfFdYOVSBcw5U3yezWQ5O5i517rWzfnawHUI9MfhpsYk0oQg8qJLnC2oo2AfgEuEyRehdsxMOsheCqPXMcSCDvJZPwAQDwel/q5wuXvvoXEK5ClIQwvVHYVPsvt3NoWwjMk8BG3A3jyeS9bMevKw4sKLK2fJ4ALkOFTWArcvVv4B7ZPeagSCDvhVhZ+GK++k4Dz+lLQbhOmzJrvdcIrybXhyTwDLCXpD12QDxUWa0BvOi1aV+PG217T6m83M/X3Bhwk4LmPT8VPbLSWtZCcB14jeBhyeDmDWUoSQjTS8Fo3etIUAPuCraOD48Dm+gkvQ3CNutnzRoaD4V/zsHvJvAAD5G93n0Ndn+tMYeEANzurdszna0E8h4q0D5rUxR3a117I1jbAXg7CI+QzFrvAwS8H1xm+GE22CSR3LGSuxRkfkQ/WV3p8fLw/7ZvMt7mj78FHjEMeBlsxNd3CFzGXet7TNaTMb8eaCk9qpQAL/a8FFdwy2FNta0gRAb50Gz752Df6PVg845TB2IiHMEPT4INxje/tamlrOIiRTDA5IIgD1FrrtQpQOqgzuEW61jqY37kUlYZaGUP4q9DZ2uJSBvK+pr1MSoT+fNdz2UldU99QZOx67kSaMyHLEDBGQM3jCgYn9D61DbAr/Af+gn0uCBNapoLNvKBb7q1gbSbuC+/dc/fQRyUQv+PwOM1xPzD65sK/Y+CzVM0duGurie5vfmZ1FadaE393dVbQ9YaySR3j+bxEc2PKtU6ITD489T1mzod+WudTVZfKouxsnb263wua5Go9WyoCXc/F0Iyde6u58LUWamfaz1X16+ld/3c3VuI8s+3fq71XFwrHQJulgpeb9eZoSYvX97eGhr1ArdcbF3jfSAcG48EGxU3NfwCMljLlBmHKpW/PHVH8v1k1RxQpe6pVN5hpVP3cPd70yAre5h1v+Hu+9i6T617wLrFade9uWvzUesUq1pjPRchdvC3+WMAA/hezUu9I+A9LkaWFUfq68FOqCvBZQNUMtr9cwnkB9EammVNnPkMeEyh79xP19Q40r3XEqo8h2+yL+++06h7JzJ21mCw6/hHhzs/wd0Pu771o6/BR+d0Pme354K9nqvbz90jNHd/Ltjzudym0fPVChmLa311XBtzLZDzJTKdzjrLsHYxA8EB1rCC7zDsun+w232V+nDXfXeAe7P7jdm9TOh+K+LusoP2/pZ08TzYOPmSn0jFECpc3YWBYPOgxtfaur+6MkZ+ACtL5hTwS3QveIyrxD8b3TRvGTjA2vqRL4I7QBwUF4USyPtoB6q/gYu4ynjIh2+v8HpXwrQxhAN5GgZV15kd8BDYqGVS1UwO4i6nZ4UluNcGThLIDyC/oP1yfsHGg7di29qTt4FD1FD/teD93yiyXEnTq9zLCivARTt9/rEgUrYnfDlfCeWRhH+Pfa9+OdiIfMnp4DKukEX2PiaBfD9ay8LjOIhfBd770YSVC2zNC7xL6m9EvByEcAW9Ci4ik8pBiC4dQD8Bm+WB71PgOmrY+4gE8v0hsLYo9bZrjmhlIOB/AByCRL/m/hlPc8ZnEkIlS5D6gQAXg4uSoIpBdJcO8808wa3Y18dEGv4ONnq3vNxPQCeDyzSYH8trIIF8H+KhynP4jT8RPKaBLkOHJrjFQpVck8Q5IHoMScvk0H5AoKXgIkRye0eqtJX0J61Jfzk7TJ4ksn2u08jkMCtXgWtLKlOINj8afe1jWekkkO+ls5bl/eQvrun9uTja4OAEIfwpiF4xu9YBi77J8+XZuuPUQREOAZGSWq2eq4iaiqP1fwSbkVKurcLYDfGtavj4onUJ5HsZmRzxNa8nf3FFYqeivCvBIa2hSiur0uEgekURyVKmftihO6y5Hq517yqE0SBSjGR+7pb1Sn0Pwd6MLcsmnZrPFYRTwG2KXtnnYRC7bSivOowU3QoeQ4LqQHTeSnCANcGNWyqS5rUvEOV+6YctsHkrR3HbclwfDBH6QexGOThGTkj3BZtqXwSbDfJvPgu8qChq+M++DkvB1E2CtJXEPwheIlgRiNbdAw4xiG5BhFEgek3n8BijHayNKrjAaQe3oJZA3kXn4jpywpX5HboaHKAILgb3bQ0E8mv39QUJ5F3WllcewTX4/wOPGUDfRodqzqumho/WLqbJzDZKRsj7jVxNSu11Vu/0oX3t1uueS69HkgvTz45cvmAL2Gx9edVUvo5dT8vKb+Ar+5v8LIG8i9b4G/D4QufovXR0tP4lcIjPTG0iIYVbHyHKfuT9xUNXrrWS3ezGT3cqmcipa5f/2GuCjbXvggM6TPJkO2sF9NL+vyYgHqq4lLye/EVgciF3NjiktbTyuxzBp4DoM1Pn7jpcO6RSHrs4bo2yyc1uSg/MmQo8t5b/Whip+zE4YFPpseN5jMKxcvpAfKD3u9lLzgfyWKiiRIO6BbyG9HBxY/2b4IAtoZkjEfEKEP2iDFl+1i8Dtw1HFzdq4v6TDhC75cJkNx67fjU4uP0scMhOTKXLdj3dLRE1jYosWLS/r+d8IEdQ16PXGdwQVu9UScfWrm+H1FK2w0D0C8qmKf3iJ6MI3ETgSGrjTDSIx8hzoEm+cPQA/+m4aJEjFbiWaceO5bL68+ABA/GAM+9zOpDHDz9mAjexPJ/8xbWt28c3vh4HB7RNC0/UoNMhZ3zGk+Z4/6DpGwcuQoSNIFI2KyNNdxW1Cy5MKjoZ36rZBA4xkh1Xk/s7naUo1E8d8OuQw8wOfRdf2fngrXeCkfrHwCFmEn6MgJJaVHiOCyNXNzHRGjaASFHZnBAGcX5BQd6JYxvrHXu/V5fNmsw1oQvBE/TOqKaGNw50Rs4G8rWh8Hn8xzs2ltJDSeWDzzm13MzK4MbPfToIW8jM0P7hizwMLjJQN4FIUZ0pWrNujJx7XR4IdAz9zAgHW+KWPPI9B141iBAfPtgpOdlSW11eeYipwfsJbgS/L3ynzpH80y1T5hwK1H6jLDazj5nDu0fZAvFIN19C0ihj5N1kV986bePr6epAU91D4LCWUPhSAnJ93/FOuM2kjj8c7KycDOQ+jdeD95O/dpCCG8EhqDqu5y71MSBsY8ga/D5bO6nyCBPc3Y2sIJHv6m5r6cz0tWsFvqy4fonw7XwTvzxyuTPrxLuzVjXxbX+TV3V4DfDcmMhr6w92Xs71FlqTv/jhEvAYXxb3FTXVvQ8OaC07bhwSfQeESBOmD44HNyHFhzfXbAaRMiQ7UrQmCel+2rIz7EYQ76Qe5NL6UPAKmj/pyWk51yI3k/Cw9xPcaOW2tfpucArt/D63HWWplP2ka72PuGXxKVdbDQSNKO/XbtsTPsNK7ZiJyGqDW8uvkG4taqx/DVxiJdHih8+Ad+YVH2SS2y45FchbQ5Un8819IniMS5eLJm+wPwewJR6q+Cw//zdB2I5QSaawPlgxbVahSqKrLXIEtQDE3jJumJx/4blA5veDkdcWgos2l1dO2qHxNm9rgnRvT8/MmUBOcJYRgzU/Ba8R/KEoWv9PcMBCmJFHpO6TkVxnIGlZxtcH+abvs3zhDwA3EUkgz1w7EOGvSPCTwkhdPbjMmii8zWyfy7/DEPDOimCk/vmenpwzBVM8tOYiBJwMnsKdpOgacEhRaf7VHMQngnCESSgt8j5QRK4nJKJ2/TaI3ZL+pOnT6bsbXKr7nLCWf8FnOwx60sk14QfjU4lHNOB48BQddMlZdzkRyNdPmjMmQR03WrsoeIvudmqCW8uEqsMQ9dUysdo5iJKitbfaQhVf0G5v1kPQFGxuaAaxG6bWkadRcU/EsRJX8QevEGCtqeHFsUvrWsBjLaWVt/L1egZ4iBuc8YLt6pe9+Z6cCOQdvoQ11jAaPMQ1zpaCxKD7wCFYQMeChr/I/B7nIOJiED3GV6IRB/wRuIwQXgWxB59/505KDPotuIgjtZXfvQO5f4TvnvVcBsYM0Cu3oREdH6lbhd63rPbQVlZ1oyZybElwT/GrcsvwVfN7lV4465tvLWWzP6fI/DN4jFtz5wea6ntVyxIik8VLw9/hoPoAuIyHr04taqx/CYTooday8Fc5gP4KPG/c4spIJG/Kp6Em2Zvvyup15DRpUr6hzR+C916XIC5yyarSqiJuJjg2H2R/rCaeb13+fBCih9qmVH4eiX4BadBDTZpu7m0Qt2R1IG/NG/UV8nzyFxGBugiEyCF5SltLd9zdthRSE+sWj15XsxWE6IGWsoovmgqe5pawHzzGQxCLipbWPQF9kLWBfF3Z7KAiuAE8hoT3FEXmLwIhcsTa8spTeKD6/8ADWsHjIEQPWEM/itSTmAZBnCUoqfu8pXbWBvKENm/kMFoCXkJYld/uvwuEyBHW6omkib8Ab9CAnej5fBiR/mKhcHXX/I20mPDN/bZPBJcvaIQ+yspZ62tCM6cooAs9n8unoXpEs7Pb6wmRLpZNmpTvy9N/1oTF4AECennEynpHlneK7PBuebl/BA17gC8Wj/YW3weCtYj5t0M/ZGUgN8D3G/A4ivMPXxKI1kk3n8gZQ/JGP6gJjgSP8DDWz0GI/WiZdvRYI5n3NAGEIY2gojsDTTXN0A9Z17UeKw1/nR8qwGMasBqEyBHxUOWdRHA+eIbejwT9fwEh9qEtFK7EZF5DugVx7kV6o7Cpvkc7nB1IVrXI10+fNSaRgB+A1wieKIrW/gmEyH4YmxJ+gAvIS8FLiHd/uqb3y3ZEdnt3VPngkYeMuE2T/g4iplXDle+ZVmjHL9ixS19WBfKOhGFl5SkET9EWwPybQIgsR1Ct1pbO/alGuBg8xK2a9cEO/TsQopuWKbOOQWX8gq+P0nTMfaYQrgystCdld9YE8liookQDnYcev2Go1N2Bxv6NdwiR7mKTZo+KG3Of5tvtOPCYAepxXF7vyLbAIvNQcMbA+LD8u/ijSyBNs5ciwgOBprqnwSZZE8gJ8A6v1wNyza85sL31xyBEFlsXOmaGCeZz3B9YAt5b/2FHot9jjCI7xMtmnd1GyhpeLUnbDOQIqwyNtuZ0z4pAvrZ09plcsHwJPIZJOA+bm3eCEFloIbd0iobmX9EB+iYuIvMgPTw16b3XV4PIWStLSgoG5Y/5kkZ9PhFUQhrjxt72PDK+MCo6/0OwUcYH8hYuXEw07wXP0W+Cy+tfASGyULw0fBwh/Yw/DEGa0ASbyDTvAZGTaM4cX2us4zSFdIcJehpkAI141agm+zN9Znwgx6F510EadPGZYNwJQmSZttLZ0zXouzmIn5JuXZU+BT8ojC5YAyKn0KRTh8aNTRfE4+0XKcTxkCGQ4O4xkbqHwAEZHcitCW6A6grPt+AmeHhMdP5SECJLxCdVVYBBl2k0z4B0LCeIVn6gNt8PImfESsNHc13yq22w+escFgdDRqGawnNOvh6q68AJmd0iR+MuvqEHgqco7gPVr/R6QniN68L4QdnsgKnNswjxSxpoVppOFQIriiOoy6Y2NiZAZK144fRBNGzQVDTwdL4+z+aLNLWTpdfttt5CpOXbd+qzsbpag0MyNpC3Tan8nCb6InhMEY95RGtbQYgMs+nwY0Zsazc/ZaD6VBzpRCBzamfvOaXrfN9OBD8MRGv/BiKrUElJQSy/eBoCHcc1tROgM0PnEMq0yN0dwmq9E0+YsHJBGzgoYwO5NvB2r6tmSFRbGK1/EoToG4yXVRyPYHSFT5NMUsqHJqVSlJGB1g1KikxTo5G6Wbl6n+TzfHwedJ3n4/+1k1YGau3ruqWT/D/rY22a1qkDeXxuKH/vSL5oJ3CDdgwCztiRMANKoaKMauPQyg0bNt8KImNZm+sMpKLRSaN9XIFhTDeJPqEApsYBj+AgnuphtW6ITI7fKQSbfYQnjlpZ6/hGPhkZyOOhiku5ljYVPKZU3tfBIa1l4XFKw+Ug0oapzBeKmxb8C2wSnz59ICXUPziQdt2HKrX5gSZj9yYIqb44jZ3HrY/5wrfO6vzY6DpOkGcVffy57ir+FHR9bMV760BXVaHzy9j1kNbt7n0yFJwxdV3jVrBZut1v/P78Lhitex1sEisLX4MaguAirnP6uG8nj/+awfw4jP+o0VyRPBRQjQZKDvJb16g1prPreswifJtuVYY6eVTj/Ci4IOMC+dryOQGtE9eC5/Bno5vmLQOnaLqNxyq/CiJtGKSs7jHbArnoHS7sfzK6sf5NcAD3WFzJ3bne5ovvhnv7FvODbYGcA8v/43jp6s50Hw3QYLcHTP0y2cxaK64UfT7QWLcAXJJxgTypE7fypVAE3mqG5PZbwCFrSsNncmNJgniacWymiuiJ/wZ3+q8BB1jpZonMMzKwg0KkGSLaYSCeUNhUXw8uyqhtTNeUzjyO77Vvgsc06UeCy99cBw5YWfLJ4QZSGiS4EXvLuj1/M0dznqavYnONM1kTDfNKDuJeNw5E5tuoCD5bGKlzNYhbMqZsSo32ofEIeI1oZdHgDsfyqfsLBl7G3U8lIISw7rfNeaY+7dCl9S3gACsXBXepfxeE6A+kNh/qEwJL618GD2RMIF8bCl+GgJPBY1zjuhIXLeoAB7RNm2VtwZoG4/9iXwjRBOEaHmskDXjuocsamsAhBOp67uUrACH6ylpiZsKcUU0Nb4BHMiKQb5gwY5gG8nwiClcknipcWv9ncErSdyO/Ifkg0pWMorqIAG8tjtb9FRwSmxo+GoHOByH6bgn3Fs8uXlofAQ9lRCBP5Ptv9rq7WRNsMBNwHTjE2pSCKyvfBpG2kEiGyV3C98LNxZG6anCINVRHJjydkWvwRFrgSuCf/UnzmKKmOsfXiR9M2hdMq6bNmEiE3wGP+dC8s3hF7SpwgLWUkpB+ASKtcde6TFx3A+FtxZF6R5O+cMX5exzBJ4AQvUSp/+Fdj5x98pkjly/YAmkg7Zef+ZMFj5CV6cJLBCtGRxc4NsGtNVR5pYLM2cUnV0mL3A10azBadzM4aEN51WHtmr4PQvTeRi4JvlkUqf0jVNdCukjrQN5aPusrpOkE8JhGuhEdyhiY2sEN0LEue2EfQpUE4RjulbqpqKn+NnBYO+nrrA2QQYheoWYD9UmjmxY4lwisj9K2hRGfGB4NpvED8N487uZ7ChyjrDXjh4BIe0g6s3cLTFNEsJNry+e5EcRbQ+Fvco3hQhCih6zVE4hweyA5/BPpGMQt6Vsw5cEN3AoeAx7iN1CThgvAIWtLK08xAc4EkRFkgNwBCG18n32hKFrnzEbN3Vj51LnCcIeVj1WInqH3EYzzAk3zayCNpWWLfHX5kZP4VrsEPMYViYedXFZgIsouThlEJrvZ7k3UyUru8XI8iBNUK0XwNCKNAiF6guiZgWrokcFIegdxS1q2yH26wJqI4vGyEFpjqOTt4JDW8soLuIl3NIiMIZPdbETwh8CWkf+Hsb9vBxfEQ3Nvgs79rYU4CIorNC4ujMx3LmeIzdIukLeFKsPc7DkPPGYloxjd+HocHGB18XHb7gYQGUWiuC3WcpXotkC09mdOTSDdW0tpxen8o26SfD7iQPjq2EwIjwzwGXcPXzJ/I2SQtArk1obzGvAJ8BgR/qMoWvsYOAQJq/mneDr+L4TbiOBvear9wlFNi2Lgks5VIepBkCgu9sOqTfLF8U8CfWGwqaEZMlBaBfJBxqHfA++TNJCpko5tohAvrzyeNJ0HIhPJLKm+2czDElcEo/W/AhdZOwkCGC/w2zYWhNgXhNeQ9DXBSEMNZLC0CeRWzVmh+p72uqhEfHasQ0sMCM4yWs2WnytpG2QkmezWW2SlwHrWzNdXjlm8YA24yMqWGC8Y+Av+qByE+Lgogr6qsKn+ecyCZQxpE8gVGLdoouHgIX43O5DMa8Ah8dCayxXgRBAZicjjDIOZ5RU0jWsCy+YvAA+0hSrv5IczQIg9YCOgvnsDbn56amNjIltGXNIikLeGwmdooK+Bx7hedmMw6swYSdu04wt1cscVIDIWSdd6D2ANId1c1FQ3DzzC5ckdJNsBi10ITL4u/6hRP1AccT5fgRfSpUV+B3iMb/ylwehJ9wI48z7rjh03c+UvCCJj8dCP7Ee+L1xQkqK5YKofFC2tnQ8eVnjiofCl/MOvByEAPkBUPwd/4peBxa+thCzmeSC3UiZy50YpeIxv/ksRqh0ZA11dOns6oHkRiIymSUvX+h5oCxD+DpEeDjbVLwGPtZTO+jqPyt8n89NzmTUvA+fzjfqT9m30wtg183dADvA0kG8JzRy5DeBG8BgSPVMcrZ8LDkhNulH6UemUzXyGLGHqzDttjX9reipP0x/SZRvHNaGKcxUoV2fFizRBpBFwMRexT+cRPHno0roWyDGeBvLt4LOyLXm6NITrb+tI4dXgkNQWpUSzQGQ8azIk5CYrH/rLGnCRasc/BlfWvQ9pJBYKX8mVi7ukrpwrrMqksq7JlwhxMYL+cyBS3ww5zLNAHisNH211Z4Pn8KdFTbWOFEwt5VWHKU2yRWmW0IQ50bXO9+VG7qWaT4ALtMKXiwvzFmJNTVpu4RoPVV3PBfodEsSzGze4NnHX6QLudXkFyXxh9Dknv4PV1bIctIt3LXJFv7EW5oKnqIWo/WFwCJp0O3fGyhalWUKp7Bkg6ewix038b7UGWqoQolrrpnaE2pJI/ft7rK1tgrTELfFq/jtuBpE1uq7LDfzhMv5sEceId5PK/PeYSMPyPa7Jak9WNaYtTwI5dzd/i9+gMvAYoqouXrroA3BAy6TKI7ia8lXwCsFDqOgNrkhYSTk6K0yEytpU+6Op19zE5HEF61jqUz5XazIUpob2yTrP6Po+kwdFjV0JUazn6/ZcBn9mEqldz7XrWPfn2v177HquvY7xm0FWmxe6ngv5GH+veaDn4tNu41/GtZUAWtubOKIwLy8RT8A3NWrfvt8HI/Wa9Od9SJ1B2OEDTJimuUUpvakD/OsBEuuLAwM377ulnd5TAWjOHF9bPPEzfnG+BSKtUVcCVLSyMKSOYLsC2MDHN/HnqxRBK1/pa0jTGu4iX5aHedFDv3h828db2zKD8UBcD+TrSquGdKRBLZrLwEWBptpfgkOUD24D76wOGJsux1TCg+zFLbLL+MG1QG5wwAUb4aJF1pj7r8Erjm3Q65z4xPDoeDzxNH/4aRBpjUP3qUlQi7XaST4yEvHN5o6jYosOvttd9asgesf1QM4tiuu4flYEXkJIIuWd6dTuS9aSOn74DHiEu0qvzfYg7gVTUrR6akNZeFo7wYvg8QRZ0VPqg8Oita0gHOfqzozx0pnjuSS8Erz342Ckphkc0DZ+ViF6uKSO+69eLI7UPwXCdobsR+6Z1vKqC9sBrIFRCeJC7MXVFrlG3/0c5PzgJYRV3By/G5ySr6wg7lVhkzSV7zIQIku8N2HGsEH+/Pt4DPXrIITYJ9cCeaw0fCY/nA4eQ6RbxjbWbwAHrAnNnMI9DheDR5Dg7rGReY7s3CaE21pKq0oNpOd5/GsSCCH2y5VAbu0LTEj3otczD5GWBxqd2RN54YwZecZW31/Buz9yRfsO9SMQjjFl0xRXWBNikwjfI9TfI8KBIIQ4IFcCeUHBoO9zGVgCHupcrwTng0PGbcu/lAufEHhF6UvGrarbCEJksFioYk4S9S+AcCKSLDkSoiccD+Srps2YiB10udf3JIL6bTBS+wo4IGK1IEBf6tVaRy7wfhdobPgHCEdJrnXnbJhwwrD2/G13gbbWhqNMKhSiFxy/YfKS/ps4iHuc2pI+5I5Rx9auD1d0DRc+JeAJ7nzMS94DwnHStW6/aqhWreXhr+z0b3+XW+EXWlmaQAjRK462yFunVZRBEr3LbtaFm1F3BiINzeCAeOmx4zV1XOXd0Dg+WPjOgsUgRAaxsrPFYx2zEefeSRoqIO1Zicm8TimdYTApFV+XOFb7/S/M8XEQ/yt43h1JywOR+rvAIYTJ5ziIe7Wk7v2tSfMGEK6QzcjtsZrHwWPxjhoeE/oPl/RpH8S7UoveB6JXfEpJAiWXONYiLw8lvqsBJ4PHdAecCw6Jh8Lf5Jv8SPAIAl0/OU32g84R0iLrh7ayqhO1JivPwuxMGqVAwqs5Iv1NIV0BoseSppK6r0scaZG3TDt2LI8nfh88xh1hfyl+r74OHLB6ynHF5OnfSP8MSAY3V8kYee/RWWcZbZOrToyXVS7QRHO5KjQbMsstwWjtvSBEGnOkRW4kk7dwiTcCPEYdhmOB1qd2XMtVhXHgAX5tE6aCS0C4Smat99z7hx0zwjfQ/EbrkjUXKAMnQWYuJbslGKmrBiHSnO2BPF5WcQIRpEE6RfphcPmrjeCA1aHKw/lv/LZXU194TP6OsY11y0G4ihBNEPtlJXLRYJ7Or9P/atCn83WaDxmoc+NNCeIic9geyEmrn6dBu6V55878O8AhPsA7PczgtiZweNEdmbgFZaZDIhnz2wuVzCloLUgcy6/NRR2KTkJSqUxsmdp1YQVxRXBDIFrnWPkhhN1sDeStpVVf5tJuAniMb8QbxzfXbAIHdOWM/x/wCBHegc8+Ky1D4Zk1k2aN8fnzTgKtT4hD4jMKYGhqZVYWzCBQRJcHovX3gxAZxLZAvmzSrKGI+nav6+LcMqgtjNY/CQ6IF04fxEN9Xk58ebcoWvsICOGileVHBwZqf1gDVSHB8YQ0nbTOqvkCPFTWroAu5iDuyF4MuSgPUSaHusS2QD7Ep64jwBLwFCVMUN8Ap4wY4mXO+GSSjC+B8Ew2z1q3MqxdXP7C6PYOY5xP4WEcrCfzWPcnFOFRYFKJxs692DvnrGXbnD/aggZ9M9DY8AwI25AyZR25S2wJ5LHyWeWk4RrwGqm/kEED1pVVHJmwPkWl8zBJHeTjj0zDSlCQgCRZNUUOigq1VsTHqOsY8rEkH0vwBeiztliBPMjnQX8u0IgvyWICfa13+dThR2Ojr0oGNw8h4Kg15Ud/MslXlnV9KK0M/j/H9w6wjuXx9WKNfRB+VIBZ5+y67pKpMJgH/m7XnXWOH3xoXXe7niv1s/hatB4/eq486M01vOuYMv0+AjOfe8sGJ7UxwPDBIaDVwDykQq0hwN8Q4D9sIsDcgKnzRviM3X8r7G5PZXNCM4RV/EKeUhRpaII0kgQsai6vPMK/1/u793tunWtdP9wTiQnrv6ljneWW9bV2vn6s7zNNcH0XOes6BOEKW+7QWFn4DW6rHAHCKc35iYGfPGTFvzeD2C0WCi/hh2kgRB9w1FtoFtDnxr5d33Kwc6290RWSTDHtBa5bHM1DgQtBOK7fNaZYafjrEsSdxaOR10kQF8I+PN7/m40b/J/uSRAXIt31q2t9dXkld9Px8JpwEM0rbqp/GoQQNrA2P4HbiiP1ju2GKITb+hXIlYbv8sNhIBxCXOpoye8shA000RruHv9qMNJQA0JkkT53rW8MVZQYiBJknPVoUWTBIhBC9A/Bi748fZQEcZGN+twibwd1OzcXB4NwCK3xkboVhBB9RmCl48NbR0drb+k2D1+IrNKnQN4WCp+kAb4MwjEm4Z3BaG0rCCH6BmG10nheYbTuP7Lfjft8hnY8AyVNOnVoq3/TmTzMO5MrauM5LhUohA1JDQuTRuLFksaFb0IO6HUgXz2mcgBXa38GwkkNxdE6yeAmRF8QcXmOP9+i228qXbroAxCecHI/8vXTjh2bMJOXtNHmC7myNtQ61rXZTWo6o4HwOUP7b4+Fwq/zF+4JRuuegyzW60CuhsK1fJtMAuEMtHKL+M/JjszVQriLC/Fm7s86qyj6mqxf9phTLfLW8oovdyQ7uDGJw7qC9zIeQvmr9agJtyNQEBA/xV87no8dzSc821pa+fONxubvTG1sTEAW6lUgj4UqSkjj1SAcowB+XBipaQYhRI9ZY+HctXpf0Bx2Cy7/xxYQnusA+8XLw/9LJv2WELmopJXcpX7F6KL857GmJrnXqfeuKq0q8im6Ggm+i4gXHELDxy6cMeNzRy1a5MSv5qletsjVPVzDKQDhENqyfUf+D0AI0WNcJtWhoisCjfULQKQNStqba31NaOYU7g3+Lbe2FVfc/kQ6cQGpgiva4h2z4qEqbe2Ylfq5mjaTYSzZloj/97Do8svioYqXCdWTXNs7rXhr/gN8ysWQZXocyFeXV04CDWeBcAxfnHc7tf2qENmGm+CbFOhbC794ygNYXS0bdKQbn8/WGYaKjL9yrW0oR+t/BQclzl6+ebMapPzX0q7NALrtDYBaw2DfqJWtoZFnBSINf4+VVlwLqB7i778oFqp8MRipfx6ySI/WkfPro3wa/gTCMfwavxkNFNwDQoiDoC08Dnp9+w7/+EBTw30SxNOVfT3YsbLwxdw9HuJycqNOmv+H3D0+ye+3hlO6Kgv4W/7ao3wh/JobRCu7jo1HUM9HDq0aEow2PMwnPtb1dA9ScIbrm8g4qUct8nhZ5UVc/T0chGMMMs779MfHeYQQXbig7uAA/nOtE7cUy2z0tGfrfuQaLrXGUPj/T45ZvmDNrsNdcyPQhzuvHdW0KJY6dtZZRmxxy/sc4ov5OwKDRycmwgfwFvrMG3VSncvnj1s3vOCzEIM/QJY4aIt8ZfnRASD1fRDOIXykULYoFWKfKLWejJ7XWh3NXaKXShDPDNb20WCD1skVZRzBQ9bHHJwf3XW8EVLb7aZ+hpnI/2iG/LNTubcddvfS7NoSuPCdBW188n9S55M+FbLIQVvkBWbejfxKBEE4gqusrXlKMrgJ8TFk5UWCP5OCO4sb63MisUc2IU32LD9DXyi1Xx3AB4Gmund3HfYnEshdmTo1+c0P1bFQeAf/1Pw4zA1zwB6b+lbAfxU3Nbzx0e+k5vLZp/CHWbVj5wEDeUt55RH8+l0EwjmEt42KvBoDIUQnhI3W7GQD6ZnCSH0diIykVP825dr9PKjHWn30RBD7WH4N3D3RrStO7Z73Zg3DPFqQp27c83fSbdR5zhDIIvt9oRfOmJGntuHvQXIbOoa7DGuLJIObEJ0IonxPPLyF1K9C0doPQQimkdrRalsD5XU/bk12i+vO+KQRf4aakhzXOejTaZ3LpNUXEqZ5P3954+5vIox3fgdm1Vry/Qby4u0DzuGXpxSEY9BEWTMucl2SA/g8LmEfSW6D58euqd8BIiu0m/ZkdlOkYoTELXMMWhPZ8Nlndz8vpVYhAhp5H94aWLx4rXXs/fKqqX5N7/CXgh0azuRDd+06nysFeakqAdE6yCL7DOQ0pnJAjExrtyAQDiF4Iris7gUQIgdxqynOpcvDKom/K1xe9x6IrDPYsCfXOuYlF1DSSHLQHhZb3GqNbafS71qT3Q7p6jE2Oobt/lnjGmvfjZWGl/BXDjcQ99yhk2Cm9R1cKciq7aH3GchbB+PVCqAEhEPoQ0C6CYTIJQib0MrIlcQ/tXfEXh7f3LwTRNZKoD2Z3azZ5hyY5/H1cxyXnZ+DrkBuTXYjX2eLHLTes9WJsCH1qGnQHocRvm49UlL/GbLIxwJ5/PBjJlDCvGn3OnthO6XgqsLGhmYQIstxKyrKwfvveQY+c+i7da+DyB3WEm+7IN3K/zmOA/RV20qrHhoUrW3tPkbOheoek+C0hg+VlY0dccyuY62h8B38MJ7/PR9c3lADWeRj68ipQz8OqYT0wgk8XLF4dGP9oyBEFuLStJ0f/svB+zrUauKjZ59UHozUXyVBPPfYmRCGr6FXCPApjtr+zUi/IqhWy60WOXTmV/9wZ2LP1r+i1PXG55/YEpwxMD6l6nz++Dp+jnblg8sgy+zRIo+Xhc/ml+VYEA4hMlF/XbYoFVkkqQAXaiTu+tQ1yrd9XmDx4m27v1o9H0RuMk20ZfnZLho6vmOAbyaXnye3lc3924COkRci6ePRUGrDIckNsPKjc7cnh989xNg8HwxCGpZ/If/3Ni51UWnzusJ3GrJuTsbuF/rdUeWDiegHstrMOXwd/aK4qf4NECITWQnWULUgmLUAxhLDgHrVnlw0cvkC2TZUfIytKVrZmMhr6+PTZ56k232v8qefMXzGq5rgvqH5xu+PWlS/13KyZZDEUQMNUlcbQLOtaepIeGNgacN9kIV2B/JDDh1+I0eaEhCO4OtoC6gBd4AQaSsVqRPcLb6Kq/MtXAov5Xp9I5LapMlcPsjvaxy+ZP5GEKIHyOZAbgksfm1lLFRxDIG6k6/RcxTCA1t3mPfGysJv8A9rMwg6NMIIjmVH8+lDrZ53KyMc97VfEIzWZu3GX/8fKnPZ6RirXgYAAAAASUVORK5CYII=)
$647K
Net Annual Savings
30%
Decrease in overall IT costs